essay on metacognitive knowledge

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• Published: 08 June 2021

Metacognition: ideas and insights from neuro- and educational sciences

• Damien S. Fleur   ORCID: orcid.org/0000-0003-4836-5255 1 , 2 ,
• Bert Bredeweg   ORCID: orcid.org/0000-0002-5281-2786 1 , 3 &
• Wouter van den Bos 2 , 4  

npj Science of Learning volume  6 , Article number:  13 ( 2021 ) Cite this article

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Metacognition comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and to regulate them (metacognitive control). Research in educational sciences has amassed a large body of evidence on the importance of metacognition in learning and academic achievement. More recently, metacognition has been studied from experimental and cognitive neuroscience perspectives. This research has started to identify brain regions that encode metacognitive processes. However, the educational and neuroscience disciplines have largely developed separately with little exchange and communication. In this article, we review the literature on metacognition in educational and cognitive neuroscience and identify entry points for synthesis. We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive constructs and processes, (ii) implement experiments to identify neural substrates necessary for metacognition based on protocols used in educational sciences, (iii) study the effects of training metacognitive knowledge in the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature from educational sciences regarding the domain-generality of metacognition.

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Introduction

Metacognition is defined as “thinking about thinking” or the ability to monitor and control one’s cognitive processes 1 and plays an important role in learning and education 2 , 3 , 4 . For instance, high performers tend to present better metacognitive abilities (especially control) than low performers in diverse educational activities 5 , 6 , 7 , 8 , 9 . Recently, there has been a lot of progress in studying the neural mechanisms of metacognition 10 , 11 , yet it is unclear at this point how these results may inform educational sciences or interventions. Given the potential benefits of metacognition, it is important to get a better understanding of how metacognition works and of how training can be useful.

The interest in bridging cognitive neuroscience and educational practices has increased in the past two decades, spanning a large number of studies grouped under the umbrella term of educational neuroscience 12 , 13 , 14 . With it, researchers have brought forward issues that are viewed as critical for the discipline to improve education. Recurring issues that may impede the relevance of neural insights for educational practices concern external validity 15 , 16 , theoretical discrepancies 17 and differences in terms of the domains of (meta)cognition operationalised (specific or general) 15 . This is important because, in recent years, brain research is starting to orient itself towards training metacognitive abilities that would translate into real-life benefits. However, direct links between metacognition in the brain and metacognition in domains such as education have still to be made. As for educational sciences, a large body of literature on metacognitive training is available, yet we still need clear insights about what works and why. While studies suggest that training metacognitive abilities results in higher academic achievement 18 , other interventions show mixed results 19 , 20 . Moreover, little is known about the long-term effects of, or transfer effects, of these interventions. A better understanding of the cognitive processes involved in metacognition and how they are expressed in the brain may provide insights in these regards.

Within cognitive neuroscience, there has been a long tradition of studying executive functions (EF), which are closely related to metacognitive processes 21 . Similar to metacognition, EF shows a positive relationship with learning at school. For instance, performance in laboratory tasks involving error monitoring, inhibition and working memory (i.e. processes that monitor and regulate cognition) are associated with academic achievement in pre-school children 22 . More recently, researchers have studied metacognition in terms of introspective judgements about performance in a task 10 . Although the neural correlates of such behaviour are being revealed 10 , 11 , little is known about how behaviour during such tasks relates to academic achievement.

Educational and cognitive neuroscientists study metacognition in different contexts using different methods. Indeed, while the latter investigate metacognition via behavioural task, the former mainly rely on introspective questionnaires. The extent to which these different operationalisations of metacognition match and reflect the same processes is unclear. As a result, the external validity of methodologies used in cognitive neuroscience is also unclear 16 . We argue that neurocognitive research on metacognition has a lot of potential to provide insights in mechanisms relevant in educational contexts, and that theoretical and methodological exchange between the two disciplines can benefit neuroscientific research in terms of ecological validity.

For these reasons, we investigate the literature through the lenses of external validity, theoretical discrepancies, domain generality and metacognitive training. Research on metacognition in cognitive neuroscience and educational sciences are reviewed separately. First, we investigate how metacognition is operationalised with respect to the common framework introduced by Nelson and Narens 23 (see Fig. 1 ). We then discuss the existing body of evidence regarding metacognitive training. Finally, we compare findings in both fields, highlight gaps and shortcomings, and propose avenues for research relying on crossovers of the two disciplines.

Meta-knowledge is characterised as the upward flow from object-level to meta-level. Meta-control is characterised as the downward flow from meta-level to object-level. Metacognition is therefore conceptualised as the bottom-up monitoring and top-down control of object-level processes. Adapted from Nelson and Narens’ cognitive psychology model of metacognition 23 .

In cognitive neuroscience, metacognition is divided into two main components 5 , 24 , which originate from the seminal works of Flavell on metamemory 25 , 26 . First, metacognitive knowledge (henceforth, meta-knowledge) is defined as the knowledge individuals have of their own cognitive processes and their ability to monitor and reflect on them. Second, metacognitive control (henceforth, meta-control) consists of someone’s self-regulatory mechanisms, such as planning and adapting behaviour based on outcomes 5 , 27 . Following Nelson and Narens’ definition 23 , meta-knowledge is characterised as the flow and processing of information from the object level to the meta-level, and meta-control as the flow from the meta-level to the object level 28 , 29 , 30 (Fig. 1 ). The object-level encompasses cognitive functions such as recognition and discrimination of objects, decision-making, semantic encoding, and spatial representation. On the meta-level, information originating from the object level is processed and top-down regulation on object-level functions is imposed 28 , 29 , 30 .

Educational researchers have mainly investigated metacognition through the lens of Self-Regulated Learning theory (SRL) 3 , 4 , which shares common conceptual roots with the theoretical framework used in cognitive neuroscience but varies from it in several ways 31 . First, SRL is constrained to learning activities, usually within educational settings. Second, metacognition is merely one of three components, with “motivation to learn” and “behavioural processes”, that enable individuals to learn in a self-directed manner 3 . In SRL, metacognition is defined as setting goals, planning, organising, self-monitoring and self-evaluating “at various points during the acquisition” 3 . The distinction between meta-knowledge and meta-control is not formally laid down although reference is often made to a “self-oriented feedback loop” describing the relationship between reflecting and regulating processes that resembles Nelson and Narens’ model (Fig. 1 ) 3 , 23 . In order to facilitate the comparison of operational definitions, we will refer to meta-knowledge in educational sciences when protocols operationalise self-awareness and knowledge of strategies, and to meta-control when they operationalise the selection and use of learning strategies and planning. For an in-depth discussion on metacognition and SRL, we refer to Dinsmore et al. 31 .

Metacognition in cognitive neuroscience

Operational definitions.

In cognitive neuroscience, research in metacognition is split into two tracks 32 . One track mainly studies meta-knowledge by investigating the neural basis of introspective judgements about one’s own cognition (i.e., metacognitive judgements), and meta-control with experiments involving cognitive offloading. In these experiments, subjects can perform actions such as set reminders, making notes and delegating tasks 33 , 34 , or report their desire for them 35 . Some research has investigated how metacognitive judgements can influence subsequent cognitive behaviour (i.e., a downward stream from the meta-level to the object level), but only one study so far has explored how this relationship is mapped in the brain 35 . In the other track, researchers investigate EF, also referred to as cognitive control 30 , 36 , which is closely related to metacognition. Note however that EF are often not framed in metacognitive terms in the literature 37 (but see ref. 30 ). For the sake of concision, we limit our review to operational definitions that have been used in neuroscientific studies.

Metacognitive judgements

Cognitive neuroscientists have been using paradigms in which subjects make judgements on how confident they are with regards to their learning of some given material 10 . These judgements are commonly referred to as metacognitive judgements , which can be viewed as a form of meta-knowledge (for reviews see Schwartz 38 and Nelson 39 ). Historically, researchers mostly resorted to paradigms known as Feelings of Knowing (FOK) 40 and Judgements of Learning (JOL) 41 . FOK reflect the belief of a subject to knowing the answer to a question or a problem and being able to recognise it from a list of alternatives, despite being unable to explicitly recall it 40 . Here, metacognitive judgement is thus made after retrieval attempt. In contrast, JOL are prospective judgements during learning of one’s ability to successfully recall an item on subsequent testing 41 .

More recently, cognitive neuroscientists have used paradigms in which subjects make retrospective metacognitive judgements on their performance in a two-alternative Forced Choice task (2-AFC) 42 . In 2-AFCs, subjects are asked to choose which of two presented options has the highest criterion value. Different domains can be involved, such as perception (e.g., visual or auditory) and memory. For example, subjects may be instructed to visually discriminate which one of two boxes contains more dots 43 , identify higher contrast Gabor patches 44 , or recognise novel words from words that were previously learned 45 (Fig. 2 ). The subjects engage in metacognitive judgements by rating how confident they are relative to their decision in the task. Based on their responses, one can evaluate a subject’s metacognitive sensitivity (the ability to discriminate one’s own correct and incorrect judgements), metacognitive bias (the overall level of confidence during a task), and metacognitive efficiency (the level of metacognitive sensitivity when controlling for task performance 46 ; Fig. 3 ). Note that sensitivity and bias are independent aspects of metacognition, meaning that two subjects may display the same levels of metacognitive sensitivity, but one may be biased towards high confidence while the other is biased towards low confidence. Because metacognitive sensitivity is affected by the difficulty of the task (one subject tends to display greater metacognitive sensitivity in easy tasks than difficult ones and different subjects may find a task more or less easy), metacognitive efficiency is an important measure as it allows researchers to compare metacognitive abilities between subjects and between domains. The most commonly used methods to assess metacognitive sensitivity during retrospective judgements are the receiver operating curve (ROC) and meta- d ′. 46 Both derive from signal detection theory (SDT) 47 which allows Type 1 sensitivity, or d’ ′ (how a subject can discriminate between stimulus alternatives, i.e. object-level processes) to be differentiated from metacognitive sensitivity (a judgement on the correctness of this decision) 48 . Importantly, only comparing meta- d ′ to d ′ seems to give reliable assessments metacognitive efficiency 49 . A ratio of 1 between meta- d’ ′ and d’ ′, indicates that a subject was perfectly able to discriminate between their correct and incorrect judgements. A ratio of 0.8 suggests that 80% of the task-related sensory evidence was available for the metacognitive judgements. Table 1 provides an overview of the different types of tasks and protocols with regards to the type of metacognitive process they operationalise. These operationalisations of meta-knowledge are used in combination with brain imaging methods (functional and structural magnetic resonance imaging; fMRI; MRI) to identify brain regions associated with metacognitive activity and metacognitive abilities 10 , 50 . Alternatively, transcranial magnetic stimulation (TMS) can be used to temporarily deactivate chosen brain regions and test whether this affects metacognitive abilities in given tasks 51 , 52 .

a Visual perception task: subjects choose the box containing the most (randomly generated) dots. Subjects then rate their confidence in their decision. b Memory task: subjects learn a list of words. In the next screen, they have to identify which of two words shown was present on the list. The subjects then rate their confidence in their decision.

The red and blue curves represent the distribution of confidence ratings for incorrect and correct trials, respectively. A larger distance between the two curves denotes higher sensitivity. Displacement to the left and right denote biases towards low confidence (low metacognitive bias) and high confidence (high metacognitive bias), respectively (retrieved from Fig. 1 in Fleming and Lau 46 ). We repeat the disclaimer of the original authors that this figure is not a statistically accurate description of correct and incorrect responses, which are typically not normally distributed 46 , 47 .

A recent meta-analysis analysed 47 neuroimaging studies on metacognition and identified a domain-general network associated with high vs. low confidence ratings in both decision-making tasks (perception 2-AFC) and memory tasks (JOL, FOK) 11 . This network includes the medial and lateral prefrontal cortex (mPFC and lPFC, respectively), precuneus and insula. In contrast, the right anterior dorsolateral PFC (dlPFC) was specifically involved in decision-making tasks, and the bilateral parahippocampal cortex was specific to memory tasks. In addition, prospective judgements were associated with the posterior mPFC, left dlPFC and right insula, whereas retrospective judgements were associated with bilateral parahippocampal cortex and left inferior frontal gyrus. Finally, emerging evidence suggests a role of the right rostrolateral PFC (rlPFC) 53 , 54 , anterior PFC (aPFC) 44 , 45 , 55 , 56 , dorsal anterior cingulate cortex (dACC) 54 , 55 and precuneus 45 , 55 in metacognitive sensitivity (meta- d ′, ROC). In addition, several studies suggest that the aPFC relates to metacognition specifically in perception-related 2-AFC tasks, whereas the precuneus is engaged specifically in memory-related 2-AFC tasks 45 , 55 , 56 . This may suggest that metacognitive processes engage some regions in a domain-specific manner, while other regions are domain-general. For educational scientists, this could mean that some domains of metacognition may be more relevant for learning and, granted sufficient plasticity of the associated brain regions, that targeting them during interventions may show more substantial benefits. Note that rating one’s confidence and metacognitive sensitivity likely involve additional, peripheral cognitive processes instead of purely metacognitive ones. These regions are therefore associated with metacognition but not uniquely per se. Notably, a recent meta-analysis 50 suggests that domain-specific and domain-general signals may rather share common circuitry, but that their neural signature varies depending on the type of task or activity, showing that domain-generality in metacognition is complex and still needs to be better understood.

In terms of the role of metacognitive judgements on future behaviour, one study found that brain patterns associated with the desire for cognitive offloading (i.e., meta-control) partially overlap with those associated with meta-knowledge (metacognitive judgements of confidence), suggesting that meta-control is driven by either non-metacognitive, in addition to metacognitive, processes or by a combination of different domain-specific meta-knowledge processes 35 .

Executive function

In EF, processes such as error detection/monitoring and effort monitoring can be related to meta-knowledge while error correction, inhibitory control, and resource allocation can be related to meta-control 36 . To activate these processes, participants are asked to perform tasks in laboratory settings such as Flanker tasks, Stroop tasks, Demand Selection tasks and Motion Discrimination tasks (Fig. 4 ). Neural correlates of EF are investigated by having subjects perform such tasks while their brain activity is recorded with fMRI or electroencephalography (EEG). Additionally, patients with brain lesions can be tested against healthy participants to evaluate the functional role of the impaired regions 57 .

a Flanker task: subjects indicate the direction to which the arrow in the middle points. b Stroop task: subjects are presented with the name of colour printed in a colour that either matches or mismatches the name. Subjects are asked to give the name of the written colour or the printed colour. c Motion Discrimination task: subjects have to determine in which direction the dots are going with variating levels of noise. d Example of a Demand Selection task: in both options subjects have to switch between two tasks. Task one, subjects determine whether the number shown is higher or lower than 5. Task two, subjects determine whether the number is odd or even. The two options (low and high demand) differ in their degree of task switching, meaning the effort required. Subjects are allowed to switch between the two options. Note, the type of task is solely indicated by the colour of the number and that the subjects are not explicitly told about the difference in effort between the two options (retrieved from Fig. 1c in Froböse et al. 58 ).

In a review article on the neural basis of EF (in which they are defined as meta-control), Shimamura argues that a network of regions composed of the aPFC, ACC, ventrolateral PFC (vlPFC) and dlPFC is involved in the regulations of cognition 30 . These regions are not only interconnected but are also intricately connected to cortical and subcortical regions outside of the PFC. The vlPFC was shown to play an important role in “selecting and maintaining information in working memory”, whereas the dlPFC is involved in “manipulating and updating information in working memory” 30 . The ACC has been proposed to monitor cognitive conflict (e.g. in a Stroop task or a Flanker task), and the dlPFC to regulate it 58 , 59 . In particular, activity in the ACC in conflict monitoring (meta-knowledge) seems to contribute to control of cognition (meta-control) in the dlPFC 60 , 61 and to “bias behavioural decision-making toward cognitively efficient tasks and strategies” (p. 356) 62 . In a recent fMRI study, subjects performed a motion discrimination task (Fig. 4c ) 63 . After deciding on the direction of the motion, they were presented additional motion (i.e. post-decisional evidence) and then were asked to rate their confidence in their initial choice. The post-decisional evidence was encoded in the activity of the posterior medial frontal cortex (pMFC; meta-knowledge), while lateral aPFC (meta-control) modulated the impact of this evidence on subsequent confidence rating 63 . Finally, results from a meta-analysis study on cognitive control identified functional connectivity between the pMFC, associated with monitoring and informing other regions about the need for regulation, and the lPFC that would effectively regulate cognition 64 .

Online vs. offline metacognition

While the processes engaged during tasks such as those used in EF research can be considered as metacognitive in the sense that they are higher-order functions that monitor and control lower cognitive processes, scientists have argued that they are not functionally equivalent to metacognitive judgements 10 , 11 , 65 , 66 . Indeed, engaging in metacognitive judgements requires subjects to reflect on past or future activities. As such, metacognitive judgements can be considered as offline metacognitive processes. In contrast, high-order processes involved in decision-making tasks such as used in EF research are arguably largely made on the fly, or online , at a rapid pace and subjects do not need to reflect on their actions to perform them. Hence, we propose to explicitly distinguish online and offline processes. Other researchers have shared a similar view and some have proposed models for metacognition that make similar distinctions 65 , 66 , 67 , 68 . The functional difference between online and offline metacognition is supported by some evidence. For instance, event-related brain potential (ERP) studies suggest that error negativities are associated with error detection in general, whereas an increased error positivity specifically encodes error that subjects could report upon 69 , 70 . Furthermore, brain-imaging studies suggest that the MFC and ACC are involved in online meta-knowledge, while the aPFC and lPFC seem to be activated when subjects engage in more offline meta-knowledge and meta-control, respectively 63 , 71 , 72 . An overview of the different tasks can be found in Table 1 and a list of different studies on metacognition can be found in Supplementary Table 1 (organised in terms of the type of processes investigated, the protocols and brain measures used, along with the brain regions identified). Figure 5 illustrates the different brain regions associated with meta-knowledge and meta-control, distinguishing between what we consider to be online and offline processes. This distinction is often not made explicitly but it will be specifically helpful when building bridges between cognitive neuroscience and educational sciences.

The regions are divided into online meta-knowledge and meta-control, and offline meta-knowledge and meta-control following the distinctions introduced earlier. Some regions have been reported to be related to both offline and online processes and are therefore given a striped pattern.

Training metacognition

There are extensive accounts in the literature of efforts to improve EF components such as inhibitory control, attention shifting and working memory 22 . While working memory does not directly reflect metacognitive abilities, its training is often hypothesised to improve general cognitive abilities and academic achievement. However, most meta-analyses found that training methods lead only to weak, non-lasting effects on cognitive control 73 , 74 , 75 . One meta-analysis did find evidence of near-transfer following EF training in children (in particular working memory, inhibitory control and cognitive flexibility), but found no evidence of far-transfer 20 . According to this study, training on one component leads to improved abilities in that same component but not in other EF components. Regarding adults, however, one meta-analysis suggests that EF training in general and working memory training specifically may both lead to significant near- and far-transfer effects 76 . On a neural level, a meta-analysis showed that cognitive training resulted in decreased brain activity in brain regions associated with EF 77 . According to the authors, this indicates that “training interventions reduce demands on externally focused attention” (p. 193) 77 .

With regards to meta-knowledge, several studies have reported increased task-related metacognitive abilities after training. For example, researchers found that subjects who received feedback on their metacognitive judgements regarding a perceptual decision-making task displayed better metacognitive accuracy, not only in the trained task but also in an untrained memory task 78 . Related, Baird and colleagues 79 found that a two-week mindfulness meditation training lead to enhanced meta-knowledge in the memory domain, but not the perceptual domain. The authors link these results to evidence of increased grey matter density in the aPFC in meditation practitioners.

Research on metacognition in cognitive science has mainly been studied through the lens of metacognitive judgements and EF (specifically performance monitoring and cognitive control). Meta-knowledge is commonly activated in subjects by asking them to rate their confidence in having successfully performed a task. A distinction is made between metacognitive sensitivity, metacognitive bias and metacognitive efficacy. Monitoring and regulating processes in EF are mainly operationalised with behavioural tasks such as Flanker tasks, Stroop tasks, Motion Discrimination tasks and Demand Selection tasks. In addition, metacognitive judgements can be viewed as offline processes in that they require the subject to reflect on her cognition and develop meta-representations. In contrast, EF can be considered as mostly online metacognitive processes because monitoring and regulation mostly happen rapidly without the need for reflective thinking.

Although there is some evidence for domain specificity, other studies have suggested that there is a single network of regions involved in all meta-cognitive tasks, but differentially activated in different task contexts. Comparing research on meta-knowledge and meta-control also suggest that some regions play a crucial role in both knowledge and regulation (Fig. 5 ). We have also identified a specific set of regions that are involved in either offline or online meta-knowledge. The evidence in favour of metacognitive training, while mixed, is interesting. In particular, research on offline meta-knowledge training involving self-reflection and metacognitive accuracy has shown some promising results. The regions that show structural changes after training, were those that we earlier identified as being part of the metacognition network. EF training does seem to show far-transfer effects at least in adults, but the relevance for everyday life activity is still unclear.

One major limitation of current research in metacognition is ecological validity. It is unclear to what extent the operationalisations reviewed above reflect real-life metacognition. For instance, are people who can accurately judge their performance on a behavioural task also able to accurately assess how they performed during an exam? Are people with high levels of error regulation and inhibitory control able to learn more efficiently? Note that criticism on the ecological validity of neurocognitive operationalisations extends beyond metacognition research 16 . A solution for improving validity may be to compare operationalisations of metacognition in cognitive neuroscience with the ones in educational sciences, which have shown clear links with learning in formal education. This also applies to metacognitive training.

Metacognition in educational sciences

The most popular protocols used to measure metacognition in educational sciences are self-report questionnaires or interviews, learning journals and thinking-aloud protocols 31 , 80 . During interviews, subjects are asked to answer questions regarding hypothetical situations 81 . In learning journals, students write about their learning experience and their thoughts on learning 82 , 83 . In thinking-aloud protocols, subjects are asked to verbalise their thoughts while performing a problem-solving task 80 . Each of these instruments can be used to study meta-knowledge and meta-control. For instance, one of the most widely used questionnaires, the Metacognitive Awareness Inventory (MAI) 42 , operationalises “Flavellian” metacognition and has dedicated scales for meta-knowledge and meta-control (also popular are the MSLQ 84 and LASSI 85 which operate under SRL). The meta-knowledge scale of the MAI operationalises knowledge of strategies (e.g., “ I am aware of what strategies I use when I study ”) and self-awareness (e.g., “ I am a good judge of how well I understand something ”); the meta-control scale operationalises planning (e.g., “ I set a goal before I begin a task ”) and use of learning strategies (e.g., “ I summarize what I’ve learned after I finish ”). Learning journals, self-report questionnaires and interviews involve offline metacognition. Thinking aloud, though not engaging the same degree self-reflection, also involves offline metacognition in the sense that online processes are verbalised, which necessitate offline processing (see Table 1 for an overview and Supplementary Table 2 for more details).

More recently, methodologies borrowed from cognitive neuroscience have been introduced to study EF in educational settings 22 , 86 . In particular, researchers used classic cognitive control tasks such as the Stroop task (for a meta-analysis 86 ). Most of the studied components are related to meta-control and not meta-knowledge. For instance, the BRIEF 87 is a questionnaire completed by parents and teachers which assesses different subdomains of EF: (1) inhibition, shifting, and emotional control which can be viewed as online metacognitive control, and (2) planning, organisation of materials, and monitoring, which can be viewed as offline meta-control 87 .

Assessment of metacognition is usually compared against metrics of academic performance such as grades or scores on designated tasks. A recent meta-analysis reported a weak correlation of self-report questionnaires and interviews with academic performance whereas think-aloud protocols correlated highly 88 . Offline meta-knowledge processes operationalised by learning journals were found to be positively associated with academic achievement when related to reflection on learning activities but negatively associated when related to reflection on learning materials, indicating that the type of reflection is important 89 . EF have been associated with abilities in mathematics (mainly) and reading comprehension 86 . However, the literature points towards contrary directions as to what specific EF component is involved in academic achievement. This may be due to the different groups that were studied, to different operationalisations or to different theoretical underpinnings for EF 86 . For instance, online and offline metacognitive processes, which are not systematically distinguished in the literature, may play different roles in academic achievement. Moreover, the bulk of research focussed on young children with few studies on adolescents 86 and EF may play a role at varying extents at different stages of life.

A critical question in educational sciences is that of the nature of the relationship between metacognition and academic achievement to understand whether learning at school can be enhanced by training metacognitive abilities. Does higher metacognition lead to higher academic achievement? Do these features evolve in parallel? Developmental research provides valuable insights into the formation of metacognitive abilities that can inform training designs in terms of what aspect of metacognition should be supported and the age at which interventions may yield the best results. First, meta-knowledge seems to emerge around the age of 5, meta-control around 8, and both develop over the years 90 , with evidence for the development of meta-knowledge into adolescence 91 . Furthermore, current theories propose that meta-knowledge abilities are initially highly domain-dependent and gradually become more domain-independent as knowledge and experience are acquired and linked between domains 32 . Meta-control is believed to evolve in a similar fashion 90 , 92 .

Common methods used to train offline metacognition are direct instruction of metacognition, metacognitive prompts and learning journals. In addition, research has been done on the use of (self-directed) feedback as a means to induce self-reflection in students, mainly in computer-supported settings 93 . Interestingly, learning journals appear to be used for both assessing and fostering metacognition. Metacognitive instruction consists of teaching learners’ strategies to “activate” their metacognition. Metacognitive prompts most often consist of text pieces that are sent at specific times and that trigger reflection (offline meta-knowledge) on learning behaviour in the form of a question, hint or reminder.

Meta-analyses have investigated the effects of direct metacognitive instruction on students’ use of learning strategies and academic outcomes 18 , 94 , 95 . Their findings show that metacognitive instruction can have a positive effect on learning abilities and achievement within a population ranging from primary schoolers to university students. In particular, interventions lead to the highest effect sizes when they both (i) instructed a combination of metacognitive strategies with an emphasis on planning strategies (offline meta-control) and (ii) “provided students with knowledge about strategies” (offline meta-knowledge) and “illustrated the benefits of applying the trained strategies, or even stimulated metacognitive reasoning” (p.114) 18 . The longer the duration of the intervention, the more effective they were. The strongest effects on academic performance were observed in the context of mathematics, followed by reading and writing.

While metacognitive prompts and learning journals make up the larger part of the literature on metacognitive training 96 , meta-analyses that specifically investigate their effectiveness have yet to be performed. Nonetheless, evidence suggests that such interventions can be successful. Researchers found that metacognitive prompts fostered the use of metacognitive strategies (offline meta-control) and that the combination of cognitive and metacognitive prompts improved learning outcomes 97 . Another experiment showed that students who received metacognitive prompts performed more metacognitive activities inside the learning environment and displayed better transfer performance immediately after the intervention 98 . A similar study using self-directed prompts showed enhanced transfer performance that was still observable 3 weeks after the intervention 99 .

Several studies suggest that learning journals can positively enhance metacognition. Subjects who kept a learning journal displayed stronger high meta-control and meta-knowledge on learning tasks and tended to reach higher academic outcomes 100 , 101 , 102 . However, how the learning journal is used seems to be critical; good instructions are crucial 97 , 103 , and subjects who simply summarise their learning activity benefit less from the intervention than subjects who reflect about their knowledge, learning and learning goals 104 . An overview of studies using learning journals and metacognitive prompts to train metacognition can be found in Supplementary Table 3 .

In recent years, educational neuroscience researchers have tried to determine whether training and improvements in EF can lead to learning facilitation and higher academic achievement. Training may consist of having students continually perform behavioural tasks either in the lab, at home, or at school. Current evidence in favour of training EF is mixed, with only anecdotal evidence for positive effects 105 . A meta-analysis did not show evidence for a causal relationship between EF and academic achievement 19 , but suggested that the relationship is bidirectional, meaning that the two are “mutually supportive” 106 .

A recent review article has identified several gaps and shortcoming in the literature on metacognitive training 96 . Overall, research in metacognitive training has been mainly invested in developing learners’ meta-control rather than meta-knowledge. Furthermore, most of the interventions were done in the context of science learning. Critically, there appears to be a lack of studies that employed randomised control designs, such that the effects of metacognitive training intervention are often difficult to evaluate. In addition, research overwhelmingly investigated metacognitive prompts and learning journals in adults 96 , while interventions on EF mainly focused on young children 22 . Lastly, meta-analyses evaluating the effectiveness of metacognitive training have so far focused on metacognitive instruction on children. There is thus a clear disbalance between the meta-analyses performed and the scope of the literature available.

An important caveat of educational sciences research is that metacognition is not typically framed in terms of online and offline metacognition. Therefore, it can be unclear whether protocols operationalise online or offline processes and whether interventions tend to benefit more online or offline metacognition. There is also confusion in terms of what processes qualify as EF and definitions of it vary substantially 86 . For instance, Clements and colleagues mention work on SRL to illustrate research in EF in relation to academic achievement but the two spawn from different lines of research, one rooted in metacognition and socio-cognitive theory 31 and the other in the cognitive (neuro)science of decision-making. In addition, the MSLQ, as discussed above, assesses offline metacognition along with other components relevant to SRL, whereas EF can be mainly understood as online metacognition (see Table 1 ), which on the neural level may rely on different circuitry.

Investigating offline metacognition tends to be carried out in school settings whereas evaluating EF (e.g., Stroop task, and BRIEF) is performed in the lab. Common to all protocols for offline metacognition is that they consist of a form of self-report from the learner, either during the learning activity (thinking-aloud protocols) or after the learning activity (questionnaires, interviews and learning journals). Questionnaires are popular protocols due to how easy they are to administer but have been criticised to provide biased evaluations of metacognitive abilities. In contrast, learning journals evaluate the degree to which learners engage in reflective thinking and may therefore be less prone to bias. Lastly, it is unclear to what extent thinking-aloud protocols are sensitive to online metacognitive processes, such as on-the-fly error correction and effort regulation. The strength of the relationship between metacognitive abilities and academic achievement varies depending on how metacognition is operationalised. Self-report questionnaires and interviews are weakly related to achievement whereas thinking-aloud protocols and EF are strongly related to it.

Based on the well-documented relationship between metacognition and academic achievement, educational scientists hypothesised that fostering metacognition may improve learning and academic achievement, and thus performed metacognitive training interventions. The most prevalent training protocols are direct metacognitive instruction, learning journals, and metacognitive prompts, which aim to induce and foster offline metacognitive processes such as self-reflection, planning and selecting learning strategies. In addition, researchers have investigated whether training EF, either through tasks or embedded in the curriculum, results in higher academic proficiency and achievement. While a large body of evidence suggests that metacognitive instruction, learning journals and metacognitive prompts can successfully improve academic achievement, interventions designed around EF training show mixed results. Future research investigating EF training in different age categories may clarify this situation. These various degrees of success of interventions may indicate that offline metacognition is more easily trainable than online metacognition and plays a more important role in educational settings. Investigating the effects of different methods, offline and online, on the neural level, may provide researchers with insights into the trainability of different metacognitive processes.

In this article, we reviewed the literature on metacognition in educational sciences and cognitive neuroscience with the aim to investigate gaps in current research and propose ways to address them through the exchange of insights between the two disciplines and interdisciplinary approaches. The main aspects analysed were operational definitions of metacognition and metacognitive training, through the lens of metacognitive knowledge and metacognitive control. Our review also highlighted an additional construct in the form of the distinction between online metacognition (on the fly and largely automatic) and offline metacognition (slower, reflective and requiring meta-representations). In cognitive neuroscience, research has focused on metacognitive judgements (mainly offline) and EF (mainly online). Metacognition is operationalised with tasks carried out in the lab and are mapped onto brain functions. In contrast, research in educational sciences typically measures metacognition in the context of learning activities, mostly in schools and universities. More recently, EF has been studied in educational settings to investigate its role in academic achievement and whether training it may benefit learning. Evidence on the latter is however mixed. Regarding metacognitive training in general, evidence from both disciplines suggests that interventions fostering learners’ self-reflection and knowledge of their learning behaviour (i.e., offline meta-knowledge) may best benefit them and increase academic achievement.

We focused on four aspects of research that could benefit from an interdisciplinary approach between the two areas: (i) validity and reliability of research protocols, (ii) under-researched dimensions of metacognition, (iii) metacognitive training, and (iv) domain-specificity vs. domain generality of metacognitive abilities. To tackle these issue, we propose four avenues for integrated research: (i) investigate the degree to which different protocols relate to similar or different metacognitive constructs, (ii) implement designs and perform experiments to identify neural substrates necessary for offline meta-control by for example borrowing protocols used in educational sciences, (iii) study the effects of (offline) meta-knowledge training on the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature in educational sciences regarding the domain-generality of metacognitive processes and metacognitive abilities.

First, neurocognitive research on metacognitive judgements has developed robust operationalisations of offline meta-knowledge. However, these operationalisations often consist of specific tasks (e.g., 2-AFC) carried out in the lab. These tasks are often very narrow and do not resemble the challenges and complexities of behaviours associated with learning in schools and universities. Thus, one may question to what extent they reflect real-life metacognition, and to what extent protocols developed in educational sciences and cognitive neuroscience actually operationalise the same components of metacognition. We propose that comparing different protocols from both disciplines that are, a priori, operationalising the same types of metacognitive processes can help evaluate the ecological validity of protocols used in cognitive neuroscience, and allow for more holistic assessments of metacognition, provided that it is clear which protocol assesses which construct. Degrees of correlation between different protocols, within and between disciplines, may allow researchers to assess to what extent they reflect the same metacognitive constructs and also identify what protocols are most appropriate to study a specific construct. For example, a relation between meta- d ′ metacognitive sensitivity in a 2-AFC task and the meta-knowledge subscale of the MAI, would provide external validity to the former. Moreover, educational scientists would be provided with bias-free tools to assess metacognition. These tools may enable researchers to further investigate to what extent metacognitive bias, sensitivity and efficiency each play a role in education settings. In contrast, a low correlation may highlight a difference in domain between the two measures of metacognition. For instance, metacognitive judgements in brain research are made in isolated behaviour, and meta-d’ can thus be viewed to reflect “local” metacognitive sensitivity. It is also unclear to what extent processes involved in these decision-making tasks cover those taking place in a learning environment. When answering self-reported questionnaires, however, subjects make metacognitive judgements on a large set of (learning) activities, and the measures may thus resemble more “global” or domain-general metacognitive sensitivity. In addition, learners in educational settings tend to receive feedback — immediate or delayed — on their learning activities and performance, which is generally not the case for cognitive neuroscience protocols. Therefore, investigating metacognitive judgements in the presence of performance or social feedback may allow researchers to better understand the metacognitive processes at play in educational settings. Devising a global measure of metacognition in the lab by aggregating subjects’ metacognitive abilities in different domains or investigating to what extent local metacognition may affect global metacognition could improve ecological validity significantly. By investigating the neural correlates of educational measures of metacognition, researchers may be able to better understand to what extent the constructs studied in the two disciplines are related. It is indeed possible that, though weakly correlated, the meta-knowledge scale of the MAI and meta-d’ share a common neural basis.

Second, our review highlights gaps in the literature of both disciplines regarding the research of certain types of metacognitive processes. There is a lack of research in offline meta-control (or strategic regulation of cognition) in neuroscience, whereas this construct is widely studied in educational sciences. More specifically, while there exists research on EF related to planning (e.g. 107 ), common experimental designs make it hard to disentangle online from offline metacognitive processes. A few studies have implemented subject reports (e.g., awareness of error or desire for reminders) to pin-point the neural substrates specifically involved in offline meta-control and the current evidence points at a role of the lPFC. More research implementing similar designs may clarify this construct. Alternatively, researchers may exploit educational sciences protocols, such as self-report questionnaires, learning journals, metacognitive prompts and feedback to investigate offline meta-control processes in the brain and their relation to academic proficiency and achievement.

Third, there is only one study known to us on the training of meta-knowledge in the lab 78 . In contrast, meta-knowledge training in educational sciences have been widely studied, in particular with metacognitive prompts and learning journals, although a systematic review would be needed to identify the benefits for learning. Relative to cognitive neuroscience, studies suggest that offline meta-knowledge trained in and outside the lab (i.e., metacognitive judgements and meditation, respectively) transfer to meta-knowledge in other lab tasks. The case of meditation is particularly interesting since meditation has been demonstrated to beneficiate varied aspects of everyday life 108 . Given its importance for efficient regulation of cognition, training (offline) meta-knowledge may present the largest benefits to academic achievement. Hence, it is important to investigate development in the brain relative to meta-knowledge training. Evidence on metacognitive training in educational sciences tends to suggest that offline metacognition is more “plastic” and may therefore benefit learning more than online metacognition. Furthermore, it is important to have a good understanding of the developmental trajectory of metacognitive abilities — not only on a behavioural level but also on a neural level — to identify critical periods for successful training. Doing so would also allow researchers to investigate the potential differences in terms of plasticity that we mention above. Currently, the developmental trajectory of metacognition is under-studied in cognitive neuroscience with only one study that found an overlap between the neural correlates of metacognition in adults and children 109 . On a side note, future research could explore the potential role of genetic factors in metacognitive abilities to better understand to what extent and under what constraints they can be trained.

Fourth, domain-specific and domain-general aspects of metacognitive processes should be further investigated. Educational scientists have studied the development of metacognition in learners and have concluded that metacognitive abilities are domain-specific at the beginning (meaning that their quality depends on the type of learning activity, like mathematics vs. writing) and progressively evolve towards domain-general abilities as knowledge and expertise increase. Similarly, neurocognitive evidence points towards a common network for (offline) metacognitive knowledge which engages the different regions at varying degrees depending on the domain of the activity (i.e., perception, memory, etc.). Investigating this network from a developmental perspective and comparing findings with the existing behavioural literature may improve our understanding of the metacognitive brain and link the two bodies of evidence. It may also enable researchers to identify stages of life more suitable for certain types of metacognitive intervention.

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Acknowledgements

We would like to thank the University of Amsterdam for supporting this research through the Interdisciplinary Doctorate Agreement grant. W.v.d.B. is further supported by the Jacobs Foundation, European Research Council (grant no. ERC-2018-StG-803338), the European Union Horizon 2020 research and innovation programme (grant no. DiGYMATEX-870578), and the Netherlands Organization for Scientific Research (grant no. NWO-VIDI 016.Vidi.185.068).

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Fleur, D.S., Bredeweg, B. & van den Bos, W. Metacognition: ideas and insights from neuro- and educational sciences. npj Sci. Learn. 6 , 13 (2021). https://doi.org/10.1038/s41539-021-00089-5

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The Role of Metacognition in Teaching Geoscience Carleton College, Northfield, Minnesota

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Example Essays

We ask each participant in this workshop to write a short essay on metacognition. The purpose of this essay is to provide an introduction to the other workshop participants to your work and thinking on the topic of metacognition. The essays will be posted on the workshop website and become part of the On the Cutting Edge resource collections. We hope that your essay will be a strong piece of writing sharing a few important ideas, points, or results that you think will be of high interest to the other participants, rather than a comprehensive list of all of your work. Your essay might address:

• how you define metacognition
• how you teach metacognition
• results from your study of metacognition
• why you think metacognition is important in teaching
• how you assess gains in metacognition
• one or more aspects of metacognition that you think are important for us to consider at the workshop

Essays should be no longer than two pages. If there are references (either your own or the work of others) that are important to your thinking, please list them at the end of the essay. Please save your essay as a Microsoft Word or PDF file to upload with your workshop registration. See example essays from two of the workshop conveners below.

In addition, we invite participants to submit teaching activities that are designed to improve students' metacognition. These activities will be part of the On the Cutting Edge activity collection which receives very high use by faculty in the geosciences and beyond. Because of its high use, the activity collection is a more effective vehicle for disseminating the "how to" of a specific teaching activity than your essay. See example activities from two of the workshop conveners.

Presentations at the workshop will be selected on the basis of the essay and activity submissions received by November 1.

Example Essay #1:

Learning about thinking and thinking about learning: metacognitive knowledge and skills for intentional learners.

by Karl Wirth, Department of Geology & Center for Scholarship and Teaching, Macalester College

In an increasingly complex and interconnected world it is ever more important that students develop intellectual and practical skills for lifelong learning. Panel reports by the AAC&U (2002, 2007) call for "higher education to help college students become intentional learners who can adapt to new environments, integrate knowledge from different sources, and continue learning throughout their lives." Becoming an intentional learner includes "developing self-awareness about the reason for study, the learning process itself, and how education is used." Intentional, or "expert," learners are more purposeful, they are more aware of themselves as learners, and they "take the initiative to diagnose their learning needs, formulate learning goals, identify resources for learning, select an implement learning strategies, and evaluate learning outcomes" (Savin-Baden and Major 2004). Research on cognition and learning (e.g., see review in Bransford et al., 2000) indicates that expert learners are characterized by having better-developed metacognitive knowledge (about the learner, learning tasks, learning strategies, and content), metacognitive control (planning, monitoring, and self-evaluation), and reflection (a critical link between knowledge and control of the learning process) (Ertmer and Newby, 1996). If an important goal of higher education is to help students become intentional learners, then our curricula should reflect those aims. Most post-secondary instruction, however, remains focused on disciplinary content. Instruction about metacognitive knowledge and skills need not "displace" disciplinary content, but can instead be used to support ("wrap") learning of that content (Lovett, 2008).

The transition from being a dependent to independent learner involves major changes involving not only how students think, but also who they are. Fink's (2003) taxonomy of significant learning promotes lasting change in the learner through integration of foundational knowledge with learning how to learn and the affective domain (feelings, values, motivations, and attitudes of the learner). To help students develop into self-directing learners I include explicit instruction about learning in all of my courses. This "co-curriculum" on learning is interwoven with geoscience content in each course. The goals of the learning co-curriculum are: (1) to encourage students to be more intentional about their learning; (2) to help students develop their metacognitive knowledge and skills; and (3) to help students construct greater personal meaning with their new knowledge and understanding. This co-curriculum helps provide structure, or scaffolding, in a learning environment that may not always be familiar to all students. Together with Dexter Perkins, I developed a summary article entitled "Learning to Learn" (Wirth and Perkins, 2008) on the essential elements of learning. This document, which is the first reading assignment in all of my courses, explores various meanings of learning, understanding, and thinking. It also highlights research on the brain, learning styles, intellectual development, metacognition, collaborative learning, and the behavioral dimensions of grades. The learning document not only serves to help students develop their metacognitive knowledge and skills, it also helps establish that my expectations for student learning in the course go far beyond memorizing content.

After introducing students to some of the elements of learning, I use a variety of activities to help them develop their metacognitive knowledge and skills. At the beginning of the semester students write a letter to the instructor, in the past tense and dated to the end of the semester, that describes what they did and how they changed to earn an "A" in the course. The purpose of this journal activity is to help students set goals and plan their learning. In other journal assignments, students reflect on the learning strategies they are employing, the success of these strategies, and modifications that they might undertake for improving their learning. Knowledge surveys, which have been described elsewhere (e.g., Nuhfer, 1996; Nuhfer and Knipp, 2003; on the SERC website ), guide student learning, facilitate student mastery of course content and skills, and help students develop their monitoring and self-assessment skills. Reading reflections, which can be readily implemented in any class or discipline, are completed by students after each reading assignment and before coming to class ( see example ). These short reflections encourage students to deepen their understanding of the readings by summarizing the important concepts and by describing what was surprising or confusing to them. This activity not only promotes student reading before class and deepens their content knowledge, it also provides opportunities for students to develop their skills for monitoring and evaluating their learning. Although reading reflections constitute only a small fraction (5-10%) of total points in each course, student performance on these activities is a good predictor of their final course grade (Figure 1) suggesting that monitoring and evaluation skills are closely associated with deeper learning. An important goal is that these reflective activities will also help students develop as intentional learners.

References Cited

AAC&U, 2002, Greater Expectations: A New Vision for Learning as a Nation Goes to College : American Association of Colleges and Universities, Washington, DC, 62 p.

AAC&U, 2007, College Learning for the New Global Century : American Association of Colleges and Universities, Washington, DC, 76 p.

Bransford, J.D., Brown, A.L., and Cocking, A.R. (editors), 2000, How People Learn: Brain, Mind, Experience, and School : National Research Council, National Academy Press, Washington D.C., 346 p.

Ertmer, P.A. and Newby, T.J., 1996, The Expert Learner: Strategic, Self-Regulated, and Reflective : Instructional Science, v. 24, p. 1-24.

Fink, L.D., 2003, Creating Significant learning Experiences: An Integrated Approach to Designing College Courses : Jossey-Bass Publishers, San Francisco, CA, 295 p.

Lovett, M.C., 2008, Teaching Metacognition : Presentation to the Educause Learning Initiative Annual Meeting, 29 January 2008.

Nuhfer, E.B., 1996, The place of formative evaluations in assessment and ways to reap their benefits : Journal of Geoscience Education, v. 44, p. 385-394.

Nuhfer, E.B., and Knipp, D., 2003, The knowledge survey: A tool for all reasons : To Improve the Academy, v. 21, p. 59-78.

Savin-Baden M., and Major C.H., 2004, Foundations of Problem-Based Learning : Society for Research into Higher Education and Open University Press, Berkshire, England, 197 p.

Wirth, K.R., and Perkins, D., 2008, Learning to Learn : online document available from: http://web.archive.org/web/20180310005012/https://www.macalester.edu/academics/geology/wirth/learning.pdf , 29 p.

Example Essay #2:

Teaching metacognition: preparing students to be successful.

by Kaatje Kraft, Physical Science Department, Mesa Community College

As a faculty member at a community college I encounter a wide diversity of students' life experiences, academic expectations, and personal goals of the students enrolled in my geoscience courses. I have had students in a single class who range in age from 17 to 65, and in academic preparation may be extremely competent and motivated to very underprepared and lacking an understanding as to how to be an effective learner. Over the last 10 years, I see more and more students arrive underprepared to be successful in post-secondary academics, a trend that is supported by recent studies (Kozeracki & Brooks, 2006; U.S. Department of Education, 2003). Knowing that fewer than 1% of these students will go on to become geology majors, it is important for me to help my students be successful, no matter their major or academic goals. Educational research supports that in order for students to learn most effectively, students must be able to compare their understanding to what they already know, fit the concepts they learn to a big picture and reflect on their learning (NRC (National Resource Council), 2005; Weinstein, Meyer, Husman, Van Mater, & McKeachie, 2006). Recent research indicates that many students lack the skills needed to be successful in the workforce, including critical thinking and self-monitoring skills (Partnership for the 21st Century Skills, 2006).

During the past 5 years, I have worked to integrate these components with the geoscience content I teach. Most recently, I have worked to integrate situated metacognition into my course content. Situated metacognition (integrated metacognition in the context of the content area) is a way to combine key learning skills within a specific course. This integration provided students with the opportunity for changes in their thinking that can lead to conceptual changes over time (Blank, 2000; Georghiades, 2004; White & Gunstone, 1989). Specifically, I have looked to see if I can increase student understanding of the nature of science, especially as it pertains to the process of geosciences, with the integration of situated metacognitive prompts throughout the course content. In order to do this effectively, I teach my class as a scientific classroom discourse community (Yerrick & Roth, 2005). This means that I teach my class from an inquiry approach and students are actively engaged in talking and writing in small and large group settings. Students are also asked periodically to reflect on their learning process both to help them gauge what they know, what they don't know, and what they can do to better understand the content they don't know. This also allows me to receive valuable formative feedback as I teach content and can better address my students' learning needs as I modify my instruction.

To help my students organize their ideas, writing, and course content, I have integrated student notebooks into my classroom. This allows students to learn to regulate their learning through an organizational system, in which support strategies are integrated into the process. Using notebooks as a learning tool provides opportunities for self- assessment, self-organization, and general self-monitoring; all of which are important for developing metacognitive skills (Klentschy & Molina-De La Torre, 2004; Moon, 2006). In the end, I hope to produce students who are more capable at being successful in any classroom and more confident that they can be successful. I'm not sure my class alone will do that (Weinstein, Husman, & Dierking, 2000), however, it's an important start.

Blank. (2000). A metacognitive learning cycle: a better warranty for student understandings? Science Education, 48 (4), 486-506.

Georghiades, P. (2004). From the general to the situated: 3 decades of metacognition. International Journal of Science Education, 26 (3), 365-383.

Klentschy, M. P., & Molina-De La Torre, E. (2004). Sudents' Science Notebooks and the Inquiry Process. In E. W. Saul (Ed.), Crossing Borders in Literacy and Science Instruction: Perspectives on Theory and Practice (pp. 340-354). Arlington, VA: International Reading Association & National Science Teachers Association (NSTA) Press.

Kozeracki, C. A., & Brooks, J. B. (2006). Emerging Institutional Support for Developmental Education. New Directions for Community Colleges, 136 (Winter), 63-73.

Moon, J. A. (2006). Learning Journals (2nd ed.). London & New York: Routledge.

NRC (National Resource Council). (2005). How Students Learn, Science in the classroom . Washington, D.C.: National Academies Press.

Partnership for the 21st Century Skills. (2006). Most Young People Entering U.S. Workforce Lack Critical Skills Essential for Success. Retrieved 28 February, 2008, from http://www.21stcenturyskills.org/index.php?option=com_content&task=view&id =250&Itemid=64

U.S. Department of Education. (2003). Community College Students: Goals, Academic Preparation, and Outcomes.

Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Self-Regulation Interventions with a focus on Learning Strategies. In M. Boekaerts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of Self Regulation (pp. 727-747): Academic Press.

Weinstein, C. E., Meyer, D. K., Husman, J., Van Mater, G., & McKeachie, W. J. (2006). Teaching Students how to Learn. In Teaching Tips: Strategies, research, and theory for college and university teachers (pp. 270-283): Houghton Mifflin.

White, R. T., & Gunstone, R. F. (1989). Metalearning and conceptual change. International Journal of Science Education, 11 (Special Issue), 577-586.

Yerrick, R. K., & Roth, W.-M. (Eds.). (2005). Establishing Scientific Classroom Discourse Communities: Multiple Voices of Teaching and Learning Research . Mahwah, NJ: Lawrence Erlbaum Associates.

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Making Metacognition Part of Student Writing

When students are encouraged to think deeply about their writing processes, they become better writers.

Writing conferences are a staple in many English language arts classrooms today. Teachers recognize the benefit of conversational feedback, allowing students to feel more agency over their own writing, and the power of building rapport that comes with conferences.

In my own classroom, I’ve been on the journey of incorporating writing conferences for over a decade, and they have changed drastically from when I first began. I’ve transitioned from doing most of the talking to students doing more and more sharing. Recently, my thinking on writing conferences has shifted again. After realizing that our conferences were primarily centered on a piece with little to no reflection on the thought process of writing, I added a new layer of complexity. 

Metacognitive Reflection

Metacognition is a term that describes thinking about one’s thinking as a means of reflection. The goal is for students to think more about the process—how they approach writing, barriers to good writing, and strategies that help them write successfully—instead of focusing only on content or rubric requirements. Metacognitive reflection can awaken students to be more aware of their thinking during writing, resulting in a deeper understanding of who they are as writers and of how to transfer their knowledge to any genre of writing. 

So what exactly does metacognitive thinking on writing look like, and how can teachers build this type of reflection into writing conferences?

A whole-class conversation about the importance of metacognition is a good starting place, since students are often focused on assignments rather than their thinking while completing them. These strategies can help students become aware of their thinking while writing and are easy to incorporate in assignments, providing students with opportunities to pause and think about their thinking while writing. Observations from these activities will enable students to talk about metacognition during conferences. 

6 Activities to Encourage Metacognition

1. Keeping a journal. Encourage students to take metacognitive breaks of two to three minutes during writing to record their thoughts. Describe your process to this point . What was a barrier to your writing? How did you overcome this? What do you think you could do to prevent this from occurring next time? These breaks can and should occur at different points in the writing process. 

2. Recording troubleshooting ideas. Encourage students to keep a list of strategies and ideas they have found successful in the past that they can use during writing to help them push through when they’re experiencing difficulty.

3. Writing collaboratively. Provide opportunities for students to work on writing assignments together. The students can discuss why they are making the choices they make along the way. Thoughts can be addressed in comments in a Google Doc or on sticky notes placed on the student’s paper. 

4. Using graphic organizers. Graphic organizers can also serve as tools to guide students to think about their thinking while writing and to identify successful strategies. The object is not to fill the entire graphic organizer but to provide multiple entry points to think about their thinking while writing. 

5. Highlighting papers. I often have students highlight papers for claims, evidence, and analysis, but this can be modified for any focus. This strategy adds a visual component to reflection and opens opportunities for students to think about what leads to strong components of a piece and why other components are weaker.

6. Recording post-writing thoughts. Writing a paragraph on the thought process during an assignment can be particularly helpful for the big-picture process. What would you do differently if writing again? Why? What would you keep the same? Why? What strategies did you employ that worked well that you can use for future writing?

The insights gathered from these metacognitive tools can carry over into writing conversations. In your next writing conferences, try adding some of the italicized questions to questions already commonly asked to gather insight and give input into the thought process behind the writing. 

• What do you like best about this writing? Why do you think this section is strong? What did you notice as you were writing this section? 
• Where did you struggle with this piece? Why did you struggle with this section? How did you feel while you were writing this section? What could have helped you while writing this particular section? Let’s review your list of troubleshooting ideas and strategies. What can you add to these?
• Where is an area you took a risk or experimented with something new? Why did you decide to do something different here? Was it successful? Why or why not? If so, how could you incorporate this into other writing? 
• How do you feel about the piece overall? How did you feel about the overall process? How do you see yourself growing as a writer? Are there particular things in your learning environment or mindset that contribute to successful writing? Identify one or two concrete strategies to use moving forward. 

Metacognition is an important step in writing instruction and where the real magic happens in learning. Students do need feedback on specific pieces of writing but should be given the opportunity to think beyond the product. Providing students with opportunities for metacognitive reflection and the opportunity to discuss their thinking strengthens their writing not only in class but for years to come.

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The Use of Metacognitive Knowledge in Essay Writing among High School Students

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• Published: 05 June 2018

Metacognitive awareness of skilled and less-skilled EFL writers

• Majid Farahian   ORCID: orcid.org/0000-0002-5367-5138 1 &
• Farnaz Avarzamani 1  

Asian-Pacific Journal of Second and Foreign Language Education volume  3 , Article number:  10 ( 2018 ) Cite this article

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The present study sought the differences between more and less proficient EFL (English as a Foreign Language) writers regarding their metacognitive awareness of writing (MAW). We also examined the relationship between MAW and EFL writing success. We used a validated MAW questionnaire for a comprehensive evaluation of the writers’ metacognitive awareness. The results demonstrated that skillful EFL writers benefit from higher metacognitive awareness. Furthermore, we found that metacognitive awareness (and its sub-categories) is positively correlated with writing proficiency, except for avoidance strategy which showed a negative correlation. The mixed design of this study which is a part of a larger project would help researchers to gain new insights into the role of metacognitive awareness in EFL writing success.

Introduction

Metacognition and its role in the process of writing have been given a credit by different lines of research studies, each having its own emphasis on the issue. Cognitive psychology, developmental psychology, and social cognitive psychology, each has had its own share of experimental research on metacognition and have contributed to theoretical developments.

In general terms, metacognition is thinking about our thinking and was defined as “the knowledge and regulation about cognitive phenomena” (Yu-Ling and Shih-Guey 2001 , p. 1). The vital role of metacognition in the writing process has been widely acknowledged after the emergence of process-oriented approaches in writing. As a notable instance, Hayes and Flower ( 1980 ) who focused mainly on the critical role of metacognition argued that “a great part of the skill in writing is the ability to monitor and direct one’s own composing process” (p. 39). Besides, Hacker, Keener, and Kircher ( 2009 ) have the same approach and maintain that “not only are metacognitive monitoring and control essential components of writing, but that writing is applied metacognition” (p. 154). In this regard, one may seek to understand the nature of the relationship between metacognition and writing. Magno ( 2008 ), who investigated the cognitive and affective predictors of English writing proficiency such as metacognition and anxiety among EFL learners found that among other factors, metacognition was highly related to writing proficiency.

Interestingly, metacognition has also been regarded as a construct which could discriminate efficient and poor writers and it may develop as the writers grow older and go to higher levels in school. According to what Harris, Santangelo, and Graham ( 2010 ) obtained, substantial research has come to the conclusions that (1) efficient writers possess more knowledge of writing than less effective writers; (2) age and schooling run parallel with writing ability; (3) a writer’s knowledge of writing is related to his performance in writing; and (4) if instruction in writing is combined with meaningful practice, the output and quality improves. They also revealed that efficient writers have more knowledge of the elements of composition and are more aware of the characteristics of high-quality compositions than poor writers. Accordingly, they are familiar with the higher order processes, such as revising and writing strategies. Unlike efficient writers, the knowledge of the less effective writers regarding writing processes is limited to form, but not the function of the composition. Therefore, they are expected, for example, to know more about writing mechanics than writing strategies. Having compared more skilled with less skilled writers, Abdullah, Bakar, Ali, and Yaacob ( 2011 ) demonstrated that both good and weak SL (second language) writers shared similar cognitive strategies, however, they were different in terms of the reasons and the ways they used strategies while writing.

As can be inferred, many scholars have touched on the important role of the metacognitive awareness in FL (Foreign Language)/SL writing in general, meanwhile, far fewer studies (e.g. Ruan 2014 ; Victori 1999 ) have investigated the significance of different types of metacognitive awareness (categories and sub-categories), especially in the domain of FL writing achievement. With regards to the categories of metacognitive awareness, Yu-Ling and Shih-Guey ( 2001 ) defined declarative knowledge as “knowing “what” are the things”; procedural knowledge as “knowing “how” to do things”; and conditional knowledge as “knowing “why” and “when” the strategies or procedures are appropriate” (p. 3). Finally, Regulation of Cognition was defined by Schraw and Moshman ( 1995 ) as “metacognitive activities that help control one’s thinking or learning” (p. 354). In the present study, we considered both categories and sub-categories of MAW using a well-developed and validated self-design questionnaire (Farahian, 2017 ).

To our knowledge, no earlier study of FL/SL writing has used a measure that could cover metacognitive awareness with its developed categories along with their sub-categories. Although Yanyan ( 2010 ) developed a metacognitive knowledge questionnaire, it only explored learners’ metacognitive knowledge and thus metacognitive experience or regulation of competence was not elicited by the questionnaire. The other limitation of the study was that there was no report of the validation procedure of the questionnaire. In order for bridging the existing gap, an inclusive and validated questionnaire was developed by Farahian ( 2017 ). We used the questionnaire in the present study so as to conduct quantitative analyses as a complementation for the earlier study by Maftoon, Birjandi, and Farahian ( 2014 ).

Finally, our main objectives were to see whether skilled and less-skilled EFL writers are different in terms of their writing MAW in general and to investigate the relationships between MAW and its categories and sub-categories with EFL writing proficiency. In this regard, we preferred to run correlational analyses instead of causal comparatives since we were determined to find out how related are the variables. In fact, many studies have used comparative analyses instead of predictive ones. Therefore, it seemed to us that correlational analysis is a more natural approach for investigating the role each sub-category of the MAW could play. In the following section, the related literature is reviewed to present essential information on what has been done so far regarding the role of metacognition in FL writing.

Research Questions (RQ)

According to the objectives of the present study, these questions were formulated:

Do skilled and less-skilled EFL writers take advantage of metacognitive awareness differently?

Is there any significant relationship between metacognitive awareness and English writing proficiency of EFL learners?

Is/are there any significant relationship(s) between sub-categories of metacognitive awareness and English writing proficiency of EFL learners?

Literature review

Since cognitive and developmental psychology have much in common in terms of the role of metacognition in learning (Koriat 2002 ), a brief review of studies in EFL context which has been mostly influenced by cognitive psychology and developmental psychology will be briefly reviewed here. It should be noted that social cognitive psychology regards metacognition as one of the components of self-regulation and tends to investigate self-regulation as the attribute that controls the writing process (Dinsmore et al. 2008 ). Therefore, studies which explore the role of self-regulation in writing adopt a much broader view and hence are not mentioned here.

Metacognitive knowledge plays a great role, similar but not exactly identical to the first language, in cognitive activities which are responsible for language learning (Flavell 1979 ). Regarding writing, in particular, it is argued that employing metacognitive strategies has significant effects not only on the global levels, but also on the content, organization, vocabulary, and mechanics of writing (Dülger 2011 ). Moreover, research findings have revealed that successful learners employ this knowledge effectively to learn the target language (Wenden 1998 ).

In a study which investigated the relationship between metacognitive abilities and writing performance in the first as well as the foreign language, Devine, Railey, and Boshoff ( 1993 ) found a potential link between metacognitive knowledge and learners’ writing. As the study suggests, cognitive models have important contributions to the writing task performance in both L1 (first language) and L2 (second language). In addition, based on the findings, metacognition plays a more important role than linguistic knowledge in effective L2 writing. The authors also report that ineffective SL writers lack sufficient metacognitive knowledge. Above all, less skilled L2 writers do not set goals for the writing tasks. In the same vein, Saddler and Graham ( 2007 ) noted that skilled writers are more purposeful in their writing and are more aware of the writing benefits. Another study that considered both L1 and L2 was Schoonen et al. ( 2003 ) in which they reported that metacognitive and linguistic knowledge was highly correlated with the learners’ writing proficiency. Furthermore, metacognitive knowledge showed a higher variance than other variables in both FL and first language. It was also found that writing in L2 was more demanding than writing in L1 since, as the authors explain, “several of these constituent abilities may be less developed than one’s first language” (p. 166).

In an attempt to assess the relationship between metacognitive personal knowledge, task knowledge, and strategy knowledge and the writing performance of ESL (English as a Second Language) students, Kasper ( 1997 ) explored the metacognitive growth of intermediate and advanced level ESL learners on their writing performance. The findings revealed that the SL learners who received higher scores on their writing tasks were those who had gained higher ratings on all three metacognitive variables, namely, person knowledge, task knowledge and strategy knowledge. Thus, as Kasper ( 1997 ) concludes, there is a positive correlation between metacognitive knowledge and writing performance of the ESL learners.

In a case study, Victori ( 1999 ) reported that the ineffective writers ineptly employed their metacognitive knowledge, and they also made limited use of it. Such a scant and limited use of metacognition affected the learners’ use of strategies. Even, as Victori ( 1999 ) maintains, “this knowledge determines the type of strategy or writing approach to be adopted by the writer” (p. 549). On the contrary, the effective writers used the person, task, and strategy knowledge more appropriately; consequently, this “endowed them with a sound basis, with which to make informed decisions and, thus, with better tools for approaching their writing” (Victori 1999 , p. 550). In the same vein, Baker ( 2010 ) reported a clear distinction between less and more skilled writers regarding both metacognitive knowledge and control. To clarify the difference, Baker ( 2010 ) concludes that better writers focus more on the function of writing, whereas poor writers focus more on the form. When asked about their conceptions of writing, better writers discuss the qualities of good writing, such as having a clear beginning, middle, and end, whereas poor writers discuss spelling all of the words correctly. Skilled writers have a higher-order awareness of the writing process, such as awareness of the organization, whereas less skilled writers tend to focus on lower-order processes, dealing with spelling, grammar, and pronunciation.

Angelova ( 2001 ) also observed a direct link between the learners’ writing performance and metacognitive knowledge and found that there was a link between EFL learners’ metacognitive knowledge and quality of writing. The results from these studies clearly indicated that the reason for the EFL writers’ failure in writing is that they did not possess the conscious cognitive knowledge of the writing process. It was also revealed that the metacognitive strategies employed by the poor writers differed from those of skilled writers in that the poor writers began writing with no specific plan and aimed to write as much as possible. Finally, Razı ( 2014 ) highlighted “successful language learners are aware of which strategies they use and they select the most suitable ones for themselves. They can also explain why they use certain kind of strategies” (p. 11).

Based on the study carried out by Maftoon et al. ( 2014 ) concerning the knowledge of cognition and regulation of cognition as the broad categories of the MAW, a framework was created during the qualitative analysis on the participants’ metacognitive awareness. The summary of the obtained results is summarized in Table  1 .

Research design

The whole project regarding the investigation of the metacognitive awareness of skilled and less-skilled EFL writers adopted a mixed (both qualitative and quantitative) approaches. The present study reports the results obtained from the quantitative analyses of the participants’ responses to the MAWQ (Metacognitive Awareness of Writing Questionnaire).

Participants

Out of 538 participants, 59 students voluntarily take part in the interview. After scoring the essays and applying Jacobs et al. ( 1981 ) we noticed that the number of less skilled writers was considerably higher than the skilled writers (16 skilled and 43 less-skilled EFL writers); Besides, the group variances were not homogeneous. Therefore, groups could not be subjected to accurate statistical analysis (Zimmerman 1987 ) As such, we randomly dropped some participants from the skilled writers’ group to have equal number in both groups. Accordingly, there was an equal number of 16 in each group.

They were selected from a population ( n  = 538) who were Iranian EFL university students majoring in English teaching, translation, and literature. Table  2 summarizes the English proficiency of the population based on their paper-based TOEFL (Test of English as a Foreign Language) exam results.

Instrumentation

The MAWQ was constructed and developed based on the view that writing metacognitive awareness, with two recognized broad categories (i.e. Knowledge of Cognition & Regulation of cognition) contains subcomponents which play critical roles in determining individuals’ metacognitive awareness in terms of EFL writing. In the qualitative analyses of writers’ essays (Maftoon et al. 2014 ), a framework emerged that contributed to a wider understanding of the role of metacognition in foreign language writing, on one hand, and the development of an inclusive survey, on the other hand (See the final version of the MAWQ in Appendix). In the questionnaire which contains 36 items, the first 20 items are classified under the first broad category of Knowledge of Cognition within which 13 items assess declarative knowledge awareness (Person = 3, General Facts = 5, Task Knowledge = 5), 3 items are related to procedural knowledge , 4 items measure conditional knowledge . In the second broad category of Regulation of Cognition , 5 items are for the assessment of Planning & Drafting , 4 items for General Strategies, 2 items for Allocating time and place , 2 items for Avoidance , and finally, 3 items for Revision .

The knowledge of cognition and its components enjoyed acceptable reliability. The reliability indices ranged from .67 to .91. To examine the validity of the scale exploratory and confirmatory factor Analyses method was used to ensure the relationships among variables (see, Farahian ( 2017 ).

The following steps were taken in order to develop the MAWQ. First, a language proficiency test (TOEFL-paper based) was given to 538 available EFL participants. Based on the results gained from the proficiency test, the participants were divided across three proficiency levels (see Table  2 ). Next, 59 EFL learners from the intermediate and advanced groups agreed to take part in a timed-essay test and the interview sessions. Based on their essay scores, the participants were divided into skilled and less-skilled EFL writers using Jacobs et al. ( 1981 ) composition profile. The essays were rated by three TEFL university instructors using the Jacobs et al. ( 1981 ) scale which measures the written ability of the learners from the perspectives of content, organization, vocabulary, and language use. Based on Jacobs et al. ( 1981 ) those who scored 68 and above were regarded as skilled writers. Accordingly, 59 participants were divided into 43 skilled and 16 less-skilled writers. It should be mentioned that the consistency of the raters was calculated by α-Cronbach index showing a high reliability among all raters (0.74).

Subsequently, the interview which was prepared according to the Dörnyei’s guidelines (2003) was conducted. The interview questions and the final obtained findings can be found in Maftoon et al. ( 2014 ). After the content analysis of responses, the preliminary items were prepared. Five experts passed their judgments on the statements. The resultant questionnaire was piloted with twenty participants. They were required to identify any unclear or ambiguous item and to write their comments regarding the items. The main version of the questionnaire was prepared and distributed among the participants.

Eventually, we conducted the quantitative analyses as a follow-up study to the qualitative investigation to explore the correlation between EFL learners’ writing proficiency and their metacognitive awareness (together with its sub-categories), as well as finding the differences between metacognitive awareness of skilled and less skilled EFL writers.

It should be noted that although the terms “efficient” and “poor” writers have extensively been extensively used in the related literature, “successful” versus “unsuccessful”, and “skilled” versus “less-skilled” are more accurate labels which differentiate proficient from less proficient writers. That is why for the consistency of the terms, skilled and less skilled labels are used in the present study.

Data analysis

To answer the first research question, we conducted the independent samples t-test, and to investigate the second and third questions, we employed the Pearson Correlation analysis. For the purpose of assessing the normality of our data, we conducted Shapiro-Wilk to make sure that the normality assumption is met. It is also noteworthy that we corrected the unequal sample sizes by random case selection of the larger group (Skilled writers). Accordingly, the groups were compared with 16 members in each. All the statistical analyses were undertaken in the SPSS 22 software.

The table below describes the two groups of writers statistically from the vantage point of their general metacognitive awareness in FL writing. Table  4 reports the normality of data in the groups.

Base on Table  3 , the mean for MAW is much greater in the group of skilled writers. The inferential statistics (Table  5 ) will show if the difference is statistically significant. Besides, the values in the Standard Deviation (SD) column show that the MAW scores of less-skilled writers were almost twice as varied as the ones in the skilled group. As a complement to the SD, we reported the Standard Error (SE) as well.

Based on Shapiro-Wilk (S-W) which is the most powerful test for small samples (Razali and Wah 2011 ), a significance value greater than 0.05 indicates that the distribution of the sample does not significantly differ from a normal distribution. In contrast, significance values lower than 0.05 tell us otherwise (Ho, 2014 ). According to Table  4 , both p -values reported are greater than 0.05 and thus both variables comply with the normality assumption.

Findings of the differences between skilled and less-skilled EFL writers regarding their metacognitive awareness of writing

To explore whether there is any significant difference between the groups regarding their metacognitive awareness of writing, an independent t-test was run. Since the variance homogeneity of the data was not met, we considered the result in the second row of the able (Equal variances not assumed).

As Table  5 displays, there is a significant difference between skilled and less-skilled writers in terms of their overall metacognitive awareness. In other words, the MAW is greater in skilled writers’ group meaning that proficient EFL writers are metacognitively more aware than their less-skilled counterpart. Therefore, we conclude that skilled and less-skilled EFL writers take advantage of metacognitive awareness differently. The authors also calculated the effect size (Cohen) to find the magnitude of this distinction. The obtained result for effect size is 6.36 which is a large one in this regard indicating that the EFL writers’ difference was largely accounted by their metacognitive awareness in writing.

The correlations between metacognitive awareness (and its sub-categories) and English writing proficiency

As was reported in the qualitative analyses, skilled and less-skilled EFL writers were different regarding the components and sub-components of writing metacognitive awareness which we identified in the framework (Maftoon et al. 2014 ). In the present study, we aimed to analyze the above-mentioned relationships quantitatively so as to find whether there exist significant relationships and if there are some, which of them are stronger than others in the categories of MAW. To that end, the correlations between MAW (both overall and its components) and writing proficiency were calculated to answer the second and third research questions. We employed the Product Moment Coefficient the results of which are shown in Table  6 .

As was demonstrated by Maftoon et al. ( 2014 ), successful and less successful EFL writers were different in most of the metacognitive awareness sub-categories. Our statistical analyses also confirmed such a considerable difference in general. As a further clarification, we decided to investigate the relationship between writing proficiency and metacognitive awareness on one hand (to answer the second RQ), and the relationship between writing proficiency and the categories (and sub-categories) of FL writing metacognitive awareness (to answer the third RQ), on the other hand.

As can be seen in Table  5 , MAW has a strong positive relationship with writing proficiency of our participants. The identified categories of MAW also show significant ‘positive’ (except for avoidance ) correlations with writing skill and among them, the strongest relationship belongs to Writing Proficiency and Regulation of Cognition (General Strategies) with the Pearson correlation of .87. On the other hand, the Pearson correlation of Writing Proficiency and Declarative Knowledge (General Facts) were less than others in the table (.70), though it is still highly significant. All in all, it can be concluded that as EFL learners’ metacognitive awareness of writing increases as their level of proficiency of writing grows.

The findings showed a significant difference in the performance of both groups, and it was revealed that skilled writers performed better at MAWRQ, suggesting there may be a link between EFL learners’ quality of writing and metacognitive knowledge. Accordingly, we were encouraged to conduct correlational analyses in order to look more closely into the possible links between FL writing proficiency and noteworthy MAW sub-categories which have already been identified in the framework presented by Maftoon et al. ( 2014 ).

The findings of the present study shed some light on the first research question as a comparative question and the second and third as correlational ones. First, we found that the two groups are considerably different in that the skilled writers outweighed the less-skilled in terms of the general MAW score. This is in harmony with many earlier studies (Baker 2010 ; Harris et al. 2010 ; Kasper 1997 ; Schoonen et al. 2003 ; Victori 1999 ). The results obtained from the qualitative study of our sample (see Maftoon et al. 2014 ) also suggest that successful writers showed a far more awareness of the process of writing. They were more aware of their strength and weaknesses as well. In contrast, while less-successful writers reported having awareness of knowledge of metacognition, neither were they able to employ them in writing nor did they know how to make use of them since, as speculated in the literature (Hamp-Lyons 1993 ), they largely focused on lower-order processes which mostly deal with spelling, grammar, and vocabulary. This, as Victori ( 1999 ) suggests, indicates that the metacognitive awareness of many ESL writers is limited and inadequate.

Congruent with previous studies (Tsai 2009 ; Victori 1999 ), the analysis of the semi-structured interviews in Maftoon et al. ( 2014 ) indicated that skilled and novice EFL writers have also differences in the type of their metacognitive awareness (whether declarative or regulatory) and/or the degree of the employment of their knowledge. The present study also acknowledged these differences and links.

Because of the key role of metacognition in FL writing, it has been suggested that there is a potential link between these two (Angelova 2001 ; Kasper 1997 ). However, the relationship between metacognitive knowledge and FL writing performance has received less attention (Xiao 2007 ), specifically regarding its constituents. The present results seem to be inconsistent with the previous findings (Devine et al. 1993 ; Kasper 1997 ) concerning the relationship between metacognition and writing since the findings demonstrated that metacognitive awareness positively correlated with the participants’ English writing performance. This means that students who rated successfully on MARQ were among the students who were labeled as skilled writers. The findings came as no surprise because all writing theories discuss the crucial role of self-regulatory and decision-making processes which can improve writing performance (Zimmerman and Bandura 1994 ). Further, Devine ( 1993 ) argues that the role of metacognition in writing is more crucial than the role of linguistic competence.

Besides the prominent role of general metacognitive ability, our quantitative analysis indicated that declarative metacognitive knowledge is positively correlated with FL writing proficiency and it was supported by many studies that we are going to remark while explaining each sub-category (person, translation, reading, and task knowledge) in the following paragraphs.

As to the awareness of knowledge of cognition in writing, while the obtained results indicated that there is a considerable positive relationship between knowledge of cognition and writing proficiency, semi-structured interviews (Maftoon et al. 2014 ), showed that proficient writers have positive attitude toward themselves and their abilities and also a greater motivation to write. This is in agreement with previous studies which documented that in foreign language context, writing has a positive relationship with attitude and self-esteem (Brooks 1985 ; Fahim and Khojaste Rad 2012 ; Hassan 1999 ; Khaldieh 2000 ; Prat-Sala and Redford 2012 ; Rose 1980 ; Victori 1999 ). It also mirrors the literature which indicates negative attitude and anxiety act as a hindrance to L2 writing (Ruan 2014 ). However, we should be cautious about the interpretation of these results because task type can also be an influential variable concerning self-concept and self-efficacy (Maftoon et al. 2014 ).

In addition to the impact of one’s self-concept, the influences of mental translation and L2 reading (termed as general facts in Table  6 ) on L2 witting proficiency were also our concern. In a study by Cohen and Brooks-Carson ( 2001 ), the role of mental translation was investigated and it turned out that direct writing (in L2) is far more effective for writers, specifically, when there is some time pressure. The semi-structured interview in the qualitative study also showed that both skilled and less-skilled writers maintain that translation is a barrier to L2 writing. However, it was revealed that mental translation was unavoidable or even beneficial in the writing processes which is in line with what de Larios, Murphy, and Marin ( 2002 ) found. For example, some skillful writers (in the semi-structured interview) stated that they use their mother tongue for planning. However, the results are in controversy regarding the pros and cons of using the first language in the L2 writing processes (Sasaki 2002 ).

Together with the above-mentioned sub-categories of declarative knowledge, task awareness also plays its role in writing essays. We found that skilled writers are more familiar with text-type and its organization and thus showed more awareness of writing tasks. In this regard, Negretti ( 2012 ), explained that “mental representation of the task will, therefore, influence metacognitive dynamics entailed in writing” (p. 146). In fact, familiarity with writing as a task in general and text organization and text-type, in particular, seem to have a link with writing expertise. However, this type of declarative knowledge should be studied cautiously since the relationship seems to be reciprocal. In other words, task properties and conditions may affect writers’ metacognitive awareness and their performance as well (Ong 2014 ).

With regard to the procedural knowledge and FL writing proficiency, we found that skilled writers were significantly more aware of the way they did the writing task. This was also revealed in their interviews (Maftoon et al. 2014 ), which is in line with what many scholars have remarked (e.g., de Larios et al. 2002 ; Razı 2014 ; Schraw and Moshman 1995 ; Yu-Ling & Shih-Guey 2001 ) in that knowing how to write and being aware of the procedures one uses have to do with their L2 writing success. On the other hand, a substantive number of less-skilled writers not only showed a shallow understanding of declarative knowledge but they also lacked enough procedural knowledge to help them employ writing strategies. It should be mentioned that the skilled writers’ responses regarding the procedural knowledge were varied in their interviews. Interestingly, some of the skilled writers were not able to articulate why and under what conditions they used writing strategies. Although such a gap has been referred to in the related literature (de Larios et al. 2002 ), there is a dearth of research studies on the issue.

Here there is a caveat that is our concern. Although skilled writers showed much higher procedural awareness, one cannot deny the fact that strategy has a capability of becoming implicit and automatized to that extent that it could be hardly ever accessible to skillful writers’ consciousness (DeKeyser 2003 ; Scheffler and Cinciała 2010 ). Moreover, no agreements have been reached in term of the degree of consciousness involved in metacognitive knowledge and strategy (Cary and Reder 2002 ). Therefore, regarding the procedural awareness, relying on qualitative analyses rather quantitative ones would yield more reliable results. This issue also applies to conditional metacognitive awareness because some of the participants including skilled and less-skilled writers did not explicitly know why and when they do what they do while preparing an essay. However, skilled writers showed a higher awareness of the conditions in which they employ strategies and this was unveiled through giving explicit examples in their interviews.

Additionally, the findings indicated that regulation of cognition and its components have a positive correlation (except avoidance with negative correlation) with L2 writing scores. It means that writers with higher skills enjoy higher ability to regulate and manage their thoughts and actions when dealing with writing tasks. In line with the literature (Baker 2010 ), which postulates that regulatory process is handled differently among efficient and poor writers, noticeable differences were found between the groups regarding their awareness of regulation of cognition. In our study, regulatory skills include Planning & drafting, and general strategies . We also found other skills that are not directly classified under the regulation of cognition category but can affect it as online strategies. They are time and place allocation , avoidance , and revision (Maftoon et al. 2014 ).

Firstly, planning has always been regarded as a fundamental skill of skilled writers. Similar to the obtained results, scholars have found that skilled writers take more time to make organized plans before writing (Becker 2006 ; Manchón and de Larios 2007 ; Sasaki 2002 ; Victori 1999 ). In this regard, some studies showed that writers would not necessarily stick to their plan. In fact, their plan may be altered in the process of writing (Victori 1999 ; Sasaki 2002 ). On the other hand, in the qualitative phase of the study, it was found that writers (especially skilled ones) tended to follow their initial plans without changing (Maftoon et al. 2014 ). All in all, what is commonly accepted is that skillful writers devote more time and attention than less-skilled writers for planning.

Regarding time allocation as an online strategy, the present study showed that it has a significant positive relationship with higher writing achievement (L2 essay writing). This was supported by Zimmerman and Risemberg ( 1997 ) who referred to time management as an essential element of effective writing. The second online strategy was avoidance by which less-skilled writers escaped the expressions and structures that they did not know or had difficulty to use them. This strategy was the only one with negative correlation (See Table  6 ). In this regard, Victori ( 1999 ), remarked that laziness or lack of commitment may result in avoidance. In the same vein, Fahim and Noormohammadi ( 2014 ), reported that high achievers tend to take risks but low achievers are “(…meticulous about language learning, dislike ambiguity, and safeguard themselves by avoiding tentative steps)” (p. 1433).

Finally, regarding revision, we found that although revising at the level of structure is helpful, it is also needed to yield a refined text at the level of content. This is what the skillful writers reported they often engaged in. This is in agreement with what Yu-Ling and Shih-Guey ( 2001 ), and Victori ( 1999 ) found. This is also in tandem with Barkaoui ( 2007 ) who explains that less- skilled SL writers do not distinguish between editing and revising. Skilled writers, on the contrary, regarded revision as a strategy which reshapes their works. For them, revision is, as Barkaoui ( 2007 ) puts it, “a recursive process that permeates the whole writing endeavor” (p. 89). That is why the majority of skilled writers in the qualitative study (Maftoon et al. 2014 ) reported that they evaluated texts globally and less-skilled writers, on the other hand, paid more attention to local corrections.

In the present study, we aimed to investigate the MAW of skilled and less-skilled Iranian EFL learners quantitatively. Accordingly, we used a self-designed questionnaire in order to analyze the writers’ awareness in an objective way as well. This study showed that the obtained results from our quantitative analyses are in harmony with what has been gained in the qualitative analyses (i.e. Maftoon et al. 2014 ). Regarding the metacognitive awareness components, the research results were in harmony with most of the previous studies. However, conducting rigorous research especially regarding procedural and conditional knowledge is a big challenge due to the lack of suitable measures on one hand, and the complicated nature of human consciousness on the other hand. We believe that conducting both qualitative and quantitative or mixed methods would shed more light on the role of metacognition in L2 writing.

The pedagogical implication of the obtained results lies in a better understanding of what is called ‘good writing’ and also in the development of a measure for recognizing metacognitive awareness in L2 writing. Only after better understanding, we could help students with lower degrees of metacognitive awareness to write cohesive essays. As maintained by Yu-Ling and Shih-Guey ( 2001 ), “EFL instructors can, therefore, help our students to strengthen their metacognitive models and learn to write well in the target language.” (p. 12).

Moreover, knowing that higher skill of efficient EFL writers is, to a significant extent, due to their superiority in metacognitive awareness of writing processes would encourage teachers to take students’ metacognitive awareness of writing more seriously into consideration. Consequently, the findings would inform language instructors regarding qualitative and quantitative differences in learners’ awareness of knowledge and regulation of cognition. This will help language teachers to “be supportive and encouraging to learners, and attend to their voices from different venues to monitor, evaluate and regulate the teaching strategies employed” (Tsai 2009 , p. 13).

As regards the issues that imposed some restrictions on our study, we would first refer to the small sample size, plus the unequal sample sizes that made us correct it at the expense of having an even smaller sample (16 in each of the writing proficiency groups). In fact, this was due to the unavailability of participants and thus we acknowledge that the results are not generalizable. On top of that, self-report data gathering tools such as questionnaires and interviews have their own weaknesses especially when the participants are reluctant (Jacobs and Paris 1987 ). Furthermore, the limitation inherent in the interview as the data collection technique was that the researcher had to rely on the interviewees’ reports of their metacognitive awareness while the statements produced by the participants may have been distorted by their memories and imperfect recall.

Furthermore, ceiling and floor effects which are related to the limitation regarding the scope of research tools in capturing the existing data may have affected the results. That being the case, longitudinal research designs would give scholars a comprehensive data on L2 writing success from the metacognition vantage point.

As mentioned before, a retrospective interview was employed in the study. That is to say, the participants took part in a retrospective activity in which the participants were asked to report on their metacognitive awareness after they finished writing. One of the major shortcomings of retrospective methods is that learners may forget the mental processes. Furthermore, memory distortions may disrupt the flow of information. It is also assumed that retrospective methods can assess individuals’ declarative knowledge. This means that the procedural knowledge which is employed in the process of composing is not adequately assessed through interviews. Accordingly, the participants’ may provide the researcher with invalid data. As such, future studies with thinking aloud protocols accompanying the interview would gain a clearer view of the participants’ metacognitive awareness. Moreover, further research with the recruitment of EFL learners from diverse regions of the country is also essential to enhance the generalizability of the findings.

Abbreviations

English as a Foreign Language

English as a Second Language

Foreign language

First language

Second language

• Metacognitive awareness of writing

Metacognitive Awareness of Writing Questionnaire

Research question(s)

Test of English as a Foreign Language

Writing proficiency

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Department of ELT, College of Literature and Humanities, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Majid Farahian & Farnaz Avarzamani

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MF conducted the procedures, gathered the data, wrote the first draft and reviewed the final draft. FA performed the statistical analyses and wrote the final draft. Both authors read and approved the final manuscript.

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Majid Farahian is an assistant professor in the Department of Foreign Languages in Islamic Azad University, Kermanshah Branch. He holds a Ph.D. in Applied Linguistics. His research interests include language education and foreign language writing. His published articles appear in both national and international journals. He also has worked as a reviewer for national and international journals.

Farnaz Avarzamani holds MA in TEFL and is an English teacher in Kermanshah, Iran. Her areas of interest include language education and psychology of language learning. She has published articles in international journals and blogs and has worked for international journals as a reviewer as well.

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Farahian, M., Avarzamani, F. Metacognitive awareness of skilled and less-skilled EFL writers. Asian. J. Second. Foreign. Lang. Educ. 3 , 10 (2018). https://doi.org/10.1186/s40862-018-0052-4

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• Metacognitive awareness
• Metacognition
• EFL writing
• Expert and novice writers

Teaching Connections

Advancing discussions about teaching, integrating reflections on assignments to develop metacognitive awareness.

Leslie LEE Department of English, Linguistics and Theatre Studies, Faculty of Arts and Social Sciences (FASS)

Leslie shares his experience of adapting Tanner (2012)’s approach to promoting metacognition amongst undergraduates, i.e. administrating self-questions to raise students’ metacognition in his undergraduate linguistic morphology course.

Image by ijeab on Freepik

Metacognition refers to the knowledge that we have about our own cognitive processes (Flavell, 1979). It is recognised as an important aspect of learning (National Research Council, 2000, 97).

Tanner (2012) discusses ways to promote metacognition amongst undergraduate students of biology, including administrating self-questions for learners to ask “in the process of planning, monitoring, and evaluating their learning”. Adapting Tanner’s self-questions to linguistics, Vallejos and Rodríguez-González (2021) explored the impact of employing self-questions to raise undergraduates’ metacognition as they did their assignments, and found that these helped students to:

• Notice learning concerns and self-capabilities;
• Raise awareness about learning strategies and develop self-efficacy;
• Better communicate needs and challenges when completing a task;
• Highlight the importance of time management and teamwork;
• Recognise that asking for help is key to monitor their understanding and learning;
• Reflect upon research skills and persona growth

Inspired, I adapted a subset of the questions (see Table 1, organised according to the metacognitive processes of planning, monitoring , and evaluating ) to three iterations of an undergraduate linguistic morphology course I offered between 2021 and 2023. These were implemented as reflection questions 1 that accompanied an assignment, and accounted for a nominal percentage of the continuous assessment marks, graded on a “Complete/Incomplete” basis. There were no word-count restrictions and I emphasised to the students that there were no “correct” or “wrong” responses.   

Table 1 Reflection questions

Unlike Vallejos and Rodríguez-González (2021), who administered their survey only once in their courses, I was curious if the repeated administration of the survey over a semester would have any additional benefit to the students. Hence, I administered my survey four times over the semester: once accompanying each of the three homework assignments, and once with the final term project. In the 2022 and 2023 iterations, the final survey included two additional questions:

• Do you agree with this statement? “The reflection questions have benefited me in my learning and self-growth.”  
• What (positive or negative) impact has responding to these reflection questions over the semester had on your learning and self-growth?

Due to space constraints, I will not discuss the students’ responses to the questions in Table 1 here, and focus instead on responses to these latter two questions.

A small minority (<9%) who read the course in 2022 and 2023 felt that they did not benefit from the reflections. The reasons provided suggested that these students either already possessed a high degree of self-awareness or were not invested in reflecting:

• “I expected myself to face these challenges and I know my strengths especially since this is my last semester of uni.”
• “(…) maybe because I don’t really put too much effort into it (…) But I think it helped me reflect on the journey that I had in this module, which might have helped in my learning and self-growth, but I’m not introspective enough to know.”

Other negative impacts of responding to reflection questions were along the lines of:

• “(…) sometimes it’s really hard to come up with an answer, maybe I am just not self-aware at all… But maybe that is what this is for??”
• “I think a negative impact may just be worrying whether I answered the reflection question uniquely enough from the last time I did it or wrote long enough.”

Nonetheless, an overwhelming majority (91%) agreed that they benefited. Positive impacts identified by the students included the following:

• “The reflection questions did help me to think back on how I approached each assignment and allowed me to use what worked and discard what didn’t in previous assignments.”
• “Thinking about the instructor’s goal helped me reflect on the purpose of the assignments and to illuminate how they were reinforcing my learning no matter the stress and suffering of going through them. I understood how they were helping me to apply what I learnt as knowledge progressed.”
• “it provides a feedback channel for me to convey my learning concerns to [the instructor] and I saw my concern being addressed in one of the assignment feedbacks.”
• “I think that they helped me to more or less process my thought processes (…) figure out what exactly I had struggles with (…) it also helped me to process my disappointments in my work, but allowed me to consider how I can perhaps do better in other modules or what good strategies there could possibly be.”

Students also saw benefits in completing multiple reflections over the semester:

• “I was able to reflect on my own growth throughout the module as well as work on improving my own personal workflow and productivity habits.”
• “The fact that the questions are the same caused me to compare myself in relation to the periods I did each survey (…) this allowed me to improve myself…”
• “I became more aware of how I learn. I made changes and adopted new strategies in order to improve. I will try to keep those in mind and reflect on my future work as well.”

Overall, while a small minority did not see any benefits to the reflections, an overwhelming majority of students did and appreciated the exercises. There is evidence that implementing regular reflections on assignments over the semester can help develop students’ metacognitive awareness, and that this effect is not simply limited to the assignment or course at hand, but has the potential to be more long-term and benefit students’ learning elsewhere, more globally. This is consistent with previous studies that have studied the effect of reflection on understanding and transfer of learning (see e.g. Lin & Lehman, 1999 and references therein).

• One can conceive of many different types of “reflections”. The questions used here did not require students to reflect on the content of what they were learning, but on their learning process .

Flavell, J. H. (1979). Metacognition and cognitive monitoring. A new area of cognitive-developmental inquiry. American Psychologist , 34 (10), 906-11. https://psycnet.apa.org/doi/10.1037/0003-066X.34.10.906

Lin X., & Lehman, J. D. (1999). Supporting learning of variable control in a computer-based biology environment: Effects of prompting college students to reflect on their own thinking. Journal of Research in Science Teaching , 36 (7), 837-58. https://doi.org/10.1002/(SICI)1098-2736(199909)36:7%3C837::AID-TEA6%3E3.0.CO;2-U

National Research Council. (2000). How People Learn: Brain, Mind, Experience, and School: Expanded Edition. The National Academies Press. https://doi.org/10.17226/9853 .

Tanner, K. D. (2012). Promoting student metacognition. CBE–Life Sciences Education , 11 (2), 113-20. https://doi.org/10.1187/cbe.12-03-0033 .

Vallejos, R., & Rodríguez-González, E. (2021). The impact of metacognition in linguistics courses . [Poster Presentation, organized session on Scholarly Teaching in Linguistics in the Age of Covid-19 and Beyond]. 2021 Annual Meeting of the Linguistics Society of America. https://lingscholarlyteaching.org/2021/01/05/poster-b7/ .

The case for metacognitive reflection: a theory integrative review with implications for medical education

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• Published: 12 February 2024

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• Jerusalem Merkebu   ORCID: orcid.org/0000-0003-3707-8920 1 ,
• Mario Veen   ORCID: orcid.org/0000-0003-2550-7193 2 ,
• Shera Hosseini 3 &
• Lara Varpio   ORCID: orcid.org/0000-0002-1412-4341 4  

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The concepts of metacognitive reflection, reflection, and metacognition are distinct but have undergone shifts in meaning as they migrated into medical education. Conceptual clarity is essential to the construction of the knowledge base of medical education and its educational interventions. We conducted a theoretical integrative review across diverse bodies of literature with the goal of understanding what metacognitive reflection is. We searched PubMed, Embase, CINAHL, PsychInfo, and Web of Science databases, including all peer-reviewed research articles and theoretical papers as well as book chapters that addressed the topic, with no limitations for date, language, or location. A total of 733 articles were identified and 87 were chosen after careful review and application of exclusion criteria. The work of conceptually and empirically delineating metacognitive reflection has begun. Contributions have been made to root metacognitive reflection in the concept of metacognition and moving beyond it to engage in cycles of reflection. Other work has underscored its affective component, transformational nature, and contextual factors. Despite this merging of threads to develop a richer conceptualization, a theory of how metacognitive reflection works is elusive. Debates address whether metacognition drives reflection or vice versa. It has also been suggested that learners evolve along on a continuum from thinking, to task-related reflection, to self-reflection, and finally to metacognitive reflection. Based on prior theory and research, as well as the findings of this review, we propose the following conceptualization: Metacognitive reflection involves heightened internal observation, awareness, monitoring, and regulation of our own knowledge, experiences, and emotions by questioning and examining cognition and emotional processes to continually refine and enhance our perspectives and decisions while thoughtfully accounting for context. We argue that metacognitive reflection brings a shift in perspective and can support valuable reconceptualization for lifelong learning.

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Introduction

Medical education is rife with concepts that have traveled into our field from medical science, psychology, humanities, and social and educational sciences (Veen et al., 2020 ). These traveling concepts (Bal, 2009 ) are often revised and reconstructed as they move into and across our field; the denotative meaning (i.e., the dictionary definition) and connotative value (i.e., the socially informed significance) of these concepts shift along these trajectories (Kreidler, 1998 ). One such concept is metacognitive reflection. Recent literature reviews have revealed persistent use of this concept in research addressing reflection or metacognition (Hargreaves, 2016 ; Jalali et al., 2015 ; Sandars, 2009 ). However, these three concepts—metacognitive reflection, reflection, and metacognition—are distinct but have undergone shifts in meaning as they migrated into medical education (Veen & Tuin, 2021 ).

This variability can create problems for medical educators because conceptual clarity is essential to the construction of both the field’s knowledge base and its educational interventions. For instance, the Accreditation Council for Graduate Medical Education’s (ACGME) common program requirements mandate that residents demonstrate the ability “to continuously improve patient care based on constant self-evaluation and lifelong learning.” To address this mandate, graduate medical education research often focuses on questions of training and testing residents’ abilities to engage in reflection (Winkel et al., 2017 ), metacognition (Mitchell et al., 2009 ), and metacognitive reflection (Gillon & Radford, 2012 ). Are these investigations addressing ACGME’s self-evaluation and lifelong learning skills from foundationally different directions? Have these terms been conflated, making it difficult to tease apart which findings relate specifically to one concept or another?

Given that reflection, metacognition, and metacognitive reflection are essential skills for lifelong learning (Rhem, 2013 ), we set out to understand the differences between these concepts and to construct conceptual clarity for each. There are long traditions of research into reflection and into metacognition; therefore, we first review the current state of knowledge about these concepts, highlighting how they overlap while remaining distinct. Since the concept of metacognitive reflection has more recently appeared in the literature, we conducted a theory integrative review (Battistone et al., 2023 ; Cornoldi et al., 2014 ; Kuiper & Pesut, 2004 ; Verplanken et al., 2007 ) to examine this concept. Before describing the methods used for this synthesis, we begin with an overview of the literature on reflection and metacognition followed by the intersection of metacognitive reflection. This summary describes the theoretical framework shaping our study.

Reflection: an overview of the concept

While different descriptions are used across the literature, reflection is commonly framed as an ongoing systematic, disciplined, back-and-forth mental activity of observing, questioning, analyzing, exploring, and refining thoughts/actions for gaining clarity in understanding and achieving productive outcomes (Bright, 1996 ; Cole & Knowles, 2000 ; Dewey, 1933 ; Fat’hi & Behzadpour, 2011 ; Killion & Todnem, 1991 ; Nguyen et al., 2014 ; Osterman & Kottkamp, 2004 ). Inherent in reflection is an inquiry disposition—i.e., an openness to discovery and exploration (Larrivee & Cooper, 2006 ; Nguyen et al., 2014 ). Dewey, the pioneer philosopher and educator for this concept, described reflection as the ability to think critically and reciprocally between two opposing points while suspending judgment (Dewey, 1933 ). He posited that reflection is an essential part of the learning process because it allows individuals to actively engage with and make important meaning out of their experiences. According to Dewey, the processes involved in igniting reflection include experiencing perplexity/doubt and wanting to investigate (e.g., to corroborate or refute) the matter in question. Reflection involves critically analyzing and evaluating experiences to gain deeper insights and understanding. For Dewey, reflection requires work: “The building blocks of reflection comprise discipline…since these habits are not a gift of nature.” Through the work of reflective inquiry, individuals can identify the underlying assumptions and beliefs that shape their actions and decisions, and they can assess the effectiveness of their actions in achieving desired outcomes. Dewey argued that reflective thinking is not limited to academic or intellectual pursuits but can be applied to any aspect of life, including moral dilemmas. In fact, Dewey also underscored the importance of action, of not simply being locked in cycles of reflection: “Application is as much an intrinsic part of genuine reflective inquiry as is alert observation or reasoning itself. There is such a thing as too much thinking, as when action is paralyzed by the multiplicity of views suggested by a situation.” He maintained that, by engaging in reflective thinking, individuals can develop more intentional approaches that balance process and product. For Dewey, then, reflection is an active and dynamic ordered process that intricately governs our actions:

Reflection involves not simply a sequence of ideas, but a consequence a consecutive ordering in such a way that each determines the next as its proper outcome, while each in turn leans back on its predecessors. The successive portions of the reflective thought grow out of one another and support another; they do not come and go in a medley. Each phase is a step from something to something. Each term leaves a deposit which is utilized in the next term.

While Dewey was a foundational and influential scholar in this area, other conceptualizations of reflection have been offered. Reflection has also been described as higher-level thinking (Lasley, 1992 ), as cognitive risk-taking (Schon, 1987 ), and as a tool for posing thoughtful and significant questions to enhance the quality of decisions (Robinson et al., 2001 ). As this diversity illustrates, there is no single operational definition for reflection (Fat’hi & Behzadpour, 2011 ). Despite the varied definitions, there are common premises behind research into reflection. For instance, one tenet is that reflection is not about developing certainty , but is instead focused on exploring and questioning one’s own thinking. Larrivee and Cooper ( 2006 ) suggested that reflection is exploration for the purpose of understanding; this view foregrounds reflection’s orientation as being focused on curiosity. Similarly, Dewey posited that by operating in a mode of protracted inquiry, reflection enables the individual to unearth blind and opaque spots in one’s thinking, to bring to light the hidden structures of one’s thinking that lie beneath consciousness (Dewey, 1933 ). Curiosity can serve as a tool for exposing unconscious mental models.

Another premise underpinning reflection is that distorted perceptions must be challenged and rejected because they can negatively impact the quality of one’s decision. Dewey declared that reflection enables the individual to become aware of limitations, gaps of understanding, and partial absences that exist even as they strive to make meaning (Dewey, 1933 ). Shapiro and Reiff proposed that reflection supports the discovery of such weaknesses and forms the basis for considering alternative perspectives (Shapiro & Reiff, 1993 ).

Finally, reflection is also steeped in the fundamental assumption that all ideas are subject to questioning, and none are exempt (Cole & Knowles, 2000 ; Nguyen et al., 2014 ). This mode of thinking encourages bringing to light embedded assumptions and requires critically challenging any established beliefs. This is particularly relevant for escaping psychic prisons (Morgan, 2007 )—i.e., favored ways of thinking that become inescapable traps. Reflection confronts entrenched mental models and tests dogmas by consistently asking questions related to each phenomenon.

With these three premises as a foundation, we can see that reflection is focused on making meaning of experiences from within the situated contexts in which the experience occurs (Boud, 1999 ; Dewey, 1933 ; Kinsella, 2010 ; Schon, 1987 ). Reflection supports the development of knowledge informed and formed by reflective practice, allowing the individual to perpetually expand to a wider range of possibilities (Larrivee & Cooper, 2006 ). Schon proposed that one can frame and reframe problems until insightful discoveries are generated through the continuous spiral of reflection (Schon, 1987 ). In this recursive process, the reflective thinker generates and tests optimal solutions and pursues action to deliberately incorporate new or enhanced understanding into action (Copeland et al., 1993 ).

Metacognition: an overview of the concept

Metacognition differs from reflection. Metacognition has been part of the works of the most prominent figures in psychology (e.g., Piaget & Kamii, 1978 ) and, across its history, it has undergone several evolutions in its conceptualization. Flavell, an early leading researcher studying metacognition, defined it as “any knowledge or cognitive activity that takes as its object, or regulates, any aspect of any cognitive enterprise” (Flavell et al., 1993 ). Today, metacognition is still commonly articulated as thinking about thinking or cognition about cognition (Dimmitt & McCormick, 2012 ; Flavell, 1979 ) . While this simple summary is widely used, a coherent definition has yet to be widely adopted (Dimmitt & McCormick, 2012 ; Dinsmore et al., 2008a ; Schraw, 2001 ; Schunk, 2008 ; Veenman et al., 2006 ; Winters et al., 2008 ). However, two commonalities across the literature act as foundational premises for research into metacognition.

One premise, which is aligned with Flavell’s seminal work ( 1979 ), posits that metacognition consists of both knowledge and experiences. Metacognitive knowledge is the individual’s acquired beliefs about his or her own and others’ cognitive orientations about people, tasks, and/or strategies; the function of metacognitive knowledge is to assess the quality of any information presented (e.g., trustworthiness, coherence). To meet the cognitive demands at hand, the individual needs to assess and manage the variations in metacognitive knowledge. Metacognitive experiences are the conscious cognitive or affective experiences that accompany any intellectual activity (Flavell, 1979 ); they interact with metacognitive knowledge to inform and shape cognitive or metacognitive goals. These metacognitive experiences can occur at any time—i.e., before, during, or after a cognitive activity—and can take many different forms—i.e., short to lengthy, simple to intricate. In sum, then, metacognition consists of both metacognitive knowledge and metacognitive experiences that co-exist and mutually inform each other.

Another commonly held proposition is that metacognition involves the planning, regulating, monitoring, and controlling of cognitive processes (Martinez, 2006 ). This echoes Schraw and Dennison’s argument that metacognition consists of knowledge about cognition (i.e., procedural, declarative, and conditional knowledge) as well as regulation of cognition (i.e., evaluating, monitoring, debugging, and managing information) (Schraw & Dennison, 1994 ).

These premises are evident in Nelson and Narens’s widely employed model of metacognition which describes the interaction between two levels of processing: object-level and meta-level (Nelson et al., 1994 ). Object-level monitoring involves being aware of cognitive processes, being aware of strategies used during a task, and evaluating them for effectiveness. Meta-level control refers to the strategies and actions employed to regulate and adjust cognitive processes based on the results of monitoring results. Nelson and Narens’s functional approach to metacognition provides a structure for understanding how monitoring precedes control through feedback loops. These authors point out that metacognition makes learning more effective by influencing behavior at various stages of processing.

The concept of metacognition grew primarily within the domain of cognitive sciences (Cornoldi et al., 2014 ). Steeped in that orientation, metacognition is the awareness of and regulation of (a) our beliefs about our own and others’ thinking processes, and (b) our cognitive and affective experiences of our own thinking processes. The concept is limited to addressing the individual’s cognitive activity and the thinking about knowledge and experiences that happens within the individual (Azevedo, 2020 ; Cornoldi et al., 2014 ; Desautel, 2009 ).

The overlap between reflection and metacognition

Both reflection and metacognition address psychologically oriented phenomena that govern reflectively thinking about thinking, but they do so in very different ways. Reflection is grounded in the active work of meaning-making in which both individuals and groups engage (Gash, 2014 ; Veen & Croix, 2017 ). It focuses on developing knowledge through constant questioning. That knowledge is never firm because reflection encourages constant curiosity and so rejects any notion of true, unequivocal knowledge. In contrast, the thinking about thinking addressed by metacognition pays careful attention to the processes of the individual. Instead of focusing on the work of meaning-making, metacognition focuses on the awareness of and regulation of cognition. There is one cognitive reality in which the individual is engaged; via metacognition, the individual strives to develop an ever more accurate understanding of that cognition.

While this distinction is present in the literature, the programs of research that address these concepts clearly illustrate how clear definitions or distinctions have consistently failed to frame their investigations (Azevedo, 2020 ; Kinsella, 2010 ). In fact, Ford and Yore (Ford & Yore, 2012 ) warned that “the fuzzy borders that exist between metacognition and reflection are converging.” These fuzzy borders are further complicated because metacognitive reflection is a term that is increasingly used across these bodies of literature but is also ill defined.

Similarly, the fuzzy borders between reflection and metacognition are evident in the health professions education (HPE) literature. For instance, the overlap between the two constructs is demonstrated in this definition of regulation of cognition offered by Medina et al.: “Regulation of cognition corresponded to knowledge about the ways that students plan, implement, and monitor their learning through self-reflection” (Medina et al., 2017 ). Here, we see that reflection is equated with the aspects of metacognition. Recently, Linsenmeyer and Long, studying participants in undergraduate medical education, presented metacognition as a type of reflection: “When students conduct reflection, they foster a way of seeing and being, a metacognitive stance toward their own thinking, and toward the structures and forces that are shaping their professional identity” (Linsenmeyer & Long, 2023 ). Here, metacognition is also cultivated by reflection. Significantly, much of the HPE literature offers assertions that reflection is a metacognitive process and/or the constructs subtly blend together at the margins (Cale et al., 2023 ; Cloude et al., 2022 ; Cui et al., 2019 ; Cutrer et al., 2013 ; González & Ruiz, 2012 ; O’Loughlin & Griffith, 2020 ; Sandars, 2009 ). Such conflations are common in the field, suggesting that the fuzzy borders between these terms that is seen in the psychology and education literature is also evident in HPE. The lack of clarity between these terms is problematic for HPE since metacognition and reflection are regarded as holding significant value, allowing students and practitioners to critically analyze their own thought processes, to identify strategies that lead to effective learning, and to make necessary adjustments for further improvement (Asadzandi et al., 2022 ; Medina et al., 2017 ; O’Loughlin & Griffith, 2020 ; Pusic et al., 2022 ). Metacognition and reflection are also valued in HPE for supporting individuals’ ability to identify and rectify biases, misconceptions, or faulty reasoning that may be hindering optimal clinical reasoning across diverse contexts (Cloude et al., 2022 ; Cutrer et al., 2013 ; González & Ruiz, 2012 ; Kosior et al., 2019 ; Kuiper & Pesut, 2004 ). Clearly, the problem that plagues other disciplines is evident in the HPE literature as well: although reflection and metacognition are distinct concepts, our research often fails to delineate between them. This problem is heightened because of the emergence of research into metacognitive reflection—another separate concept that intentionally combines its constituent concepts in specific ways.

Metacognitive reflection: a concept in the making

There is rising interest in the integration of metacognition and reflection and in the use of the term metacognitive reflection in the medical education literature and beyond (Graber et al., 2012 ; Hargreaves, 2016 ; Hodges, 2015 ; Sandars, 2009 ); therefore, it is important to have a clear understanding of how these three terms align and diverge. We need clear definitions of these terms and of the conceptualizations underpinning them to guide research in our field. It may be that our research will require us to revise definitions. It may be that disagreements in conceptualizations can be the source of productive knowledge development. However, if we do not begin with explicit definitions and conceptualizations, we risk building more confusion than insights. The concept of metacognitive reflection is increasingly present in research addressing reflection and metacognition, but is rarely defined and often used interchangeably with its two constitutive concepts. Given this lack of clarity, we set out to analyze the literature addressing metacognitive reflection—manuscripts that offer definitions of the term and/or that offer a theory into the concept—and to synthesize the different conceptualizations found therein.

To realize this analysis and synthesis, we engaged in a theory integrative review (TIR) (Battistone et al., 2023 ), a type of literature review that helps define a concept and synthesize theories when many variations exist (Torraco, 2005 ). TIRs are designed “to critically examine theories which address a particular phenomenon, bringing two or more theories into conversation with each other in order to reformulate, integrate, or purposefully synthesize the conceptualizations offered” (Battistone et al., 2023 ). As a form of knowledge synthesis developed within the constructivist tradition, TIRs build a subjectively informed aggregation of the theories (and their associated definitions) addressing a particular phenomenon. We followed the four-step process for TIRs described by Battistone et al. ( 2023 ).

Step 1: Define the phenomenon

The phenomenon of interest was metacognitive reflection. As part of our work to define this phenomenon, we have reviewed the literature on reflection and metacognition. Our research question asked: What is metacognitive reflection? To address this question, we also explored several subquestions: Is metacognitive reflection the inherent overlap of reflection and metacognition? What features distinguish metacognitive reflection from its named components? How can metacognitive reflection be best conceptualized to advance the purposes of medical education?

Step 2: Create the research team

We constructed our research team to reflect specific interests and ensure that a variety of motivations informed the research. Our team consisted of medical education scholars with a range of interests and expertise. All four members of the research team actively engage in qualitative research focused on medical education. JM’s training in educational psychology and expertise in reflection and metacognition ensured that a broad range of literature was explored to inform the synthesis and that contradictions in the literature were regularly considered to shape the team’s analysis. An expert in philosophy and interdisciplinary research, MV’s expertise guided the team’s efforts to ensure that the ontological and epistemological roots of each theory and definition included in the analysis were respected and maintained. As a senior member of the medical education community, LV focused on ensuring methodological rigor, considering implications of the findings for related bodies of research, and ensuring the ontological and epistemological consistency from the original theories through to amalgamation outcomes. SH is a social scientist and education evaluation researcher with a background in psychology and so also offered expertise in metacognition. Finally, at an earlier stage we consulted with AdlC, a medical education researcher and expert on reflection. The team’s research meetings often involved discussions about the nature and depth of the interpretations we were making about reflection, metacognition, and metacognitive reflection. We also debated how the synthesis could be relevant to and influential for the field of medical education.

Step 3: Explore and analyze the data

To identify all pertinent literature for this review, we searched PubMed, Embase, CINAHL, PsychInfo , and Web of Science databases. Our search included all peer-reviewed research articles and theoretical papers published as well as book chapters that addressed metacognitive reflection. The search was not restricted by date, language, or country of publication. We did not restrict the search to HPE literature; instead, we searched across disciplines and fields of inquiry since metacognitive reflection is a relatively new concept being used in the literature. (According to our search, the term metacognitive reflection was first used by Karmiloff-Smith in 1979, while Dewey’s descriptions of reflection date back to the early 1930s.) Recognizing that it can take decades for conceptualizations of complex concepts like metacognitive reflection to become stable, we looked across domains in hopes of seeing how the term is stabilizing (or has stabilized) in any domain. With assistance from a university research librarian, a search of electronic databases was conducted in October 2020 and was rerun to capture new publications in September 2022. Search terms included “metacognitive reflection,” “metacognition” AND “reflection.”

This search identified 1133 papers; after deduplication, 733 articles remained in the corpus. Since the purpose of the review was to synthesize theories and definitions of metacognitive reflection, our initial review process involved determining which articles contained sufficiently rich descriptions to act as data for analysis. Articles that did not offer explicit conceptualizations or definitions of metacognitive reflection were excluded. For instance, we excluded articles that used the term metacognitive reflection as an adjective describing a different term (Seppanen, 2022 ) and papers focused solely on metacognition (Larkin, 2009 ; Pressley, 2005 ) or reflection (Grushka et al., 2005 ; So et al., 2018 ) that failed to directly or indirectly discuss metacognitive reflection. We also excluded articles addressing Metacognitive Reflection and Insight Therapy (MERIT)—a psychotherapeutic approach used with patients with mental illnesses.

With these exclusion criteria in mind, 30 articles from the corpus were sampled and reviewed by JM and AdlC, resulting in 14 conflicts. Resolving these conflicts began through conversation with MV, who read these 14 manuscripts. This high number of conflicts highlighted the high variability in the use of the term metacognitive reflection across the corpus and the implications of that variability—e.g., if we adhered too closely to one author’s definition, then many other authors’ conceptualizations (and their manuscripts) would be removed from the corpus; if we incorporated some definitions, then there was no difference between metacognitive reflection and either reflection or metacognition. LV then joined these conversations, working collaboratively with the team to analyze definitions across the 30 articles to understand core aspects of the descriptions of metacognitive reflection. Once consensus was achieved, 9 of the 30 articles were identified for full review and an approach for reviewing the titles and abstracts for inclusion markers was established. Next, two authors (JM, SH) appraised the title and abstract of all remaining papers in the corpus, excluding 636 articles that failed to meet the inclusion criteria, thereby leaving 97 articles for full-text review. During full-text review, 12 manuscripts were excluded from the corpus but, via hand searching of references and updating the search, 7 articles were added. Ultimately, 87 articles comprised the corpus for the TIR.

Step 4: Integrate the literature

The full research team was involved in analyzing each paper in the corpus to identify the definition of metacognitive reflection presented and any underlying theory (i.e., premises that connected in a logical manner to address metacognitive reflection). In keeping with Parse’s criteria for studying theory (Parse, 2005 ), we focused on the structure of these theories (i.e., historical origins, foundational assumptions, principal conceptualizations, and relational statements) and their processes (i.e., the coherence, integrations, and heuristic potential of the theories). In this integration work, the research team sought to make clear how some authors’ definitions clustered, and the key aspects of those definitions that separated different clusters. This work involved looking for illustrative cases of each cluster and contrasting them with negative cases which then became the foundation for identifying a cluster that had a notably different definition. We also focused on different structures and processes of the theories offered in each of these clusters. Once these definitions, structures, and processes were made clear, we then engaged in analysis across these clusters to identify key features, principles, and premises of metacognitive reflection.

Our analysis revealed that (a) metacognitive reflection has been used to address the ways in which the concepts of reflection and metacognition overlap; and (b) researchers are increasingly interested in understanding this overlap (Alt & Raichel, 2020 ; Barley, 2012 ; Lonie & Desai, 2015a ; McCabe & Olimpo, 2020 ; Sawicki & Wegener, 2018 ). This overlap consists of attention to the examination of metacognition and of internally oriented thinking—i.e., reflectively thinking about thinking. However, as illustrated in (Table  1 ; Candy et al., 1985 ; Davis, 2000 ; Dinsmore et al., 2008b ; Hargis & Marotta, 2011 ; Sandars, 2009 ; Seifert, 2007 ; Siddiqui & Dubey, 2018 ), many scholars have conflated the terms reflection and metacognition in their research (i.e., they use the term reflection to define metacognition and vice versa). Further complicating this situation is that some researchers offer disparate conceptualizations, and others have even begun to informally reconceptualize reflection and metacognition as a single concept—i.e., as metacognitive reflection (Bormotova, 2010 ; Gillon & Radford, 2012 ; Hargis & Marotta, 2011 ). This makes clearly articulating metacognitive reflection as a distinct concept more challenging because much of the literature fails to account for the fact that reflection and metacognition are themselves different concepts. In other words, we contend that simply conceptualizing metacognitive reflection as the overlap between reflection and metacognition fails to capture its uniqueness (Granville & Dison, 2005 ).

The case for metacognitive reflection as a distinct concept

Our analysis of the corpus revealed that the work of conceptually and empirically delineating metacognitive reflection as a concept separate from both reflection and metacognition has begun. Verplanken et al. are among the few researchers who have described that delineation:

Metacognitive reflection refers to the appraisal, monitoring, or control of one’s cognitions or mental functioning where various types of metacognitions may be distinguished. For instance, one may reflect on the target of thoughts, the origin of thoughts, the amount of thoughts, the valence of thoughts or consequences of thoughts (Verplanken et al., 2007 ).

Here, the definition for metacognitive reflection is deeply rooted in the concept of metacognition, where individuals ascend from one layer to another by adapting their cognitions (Drigas et al., 2022a ).

We suggest that, in keeping with this orientation, it is useful to conceptualize metacognitive reflection as starting with processes of metacognition. Then, in metacognitive reflection, the individual moves beyond metacognition to engage in cycles of reflection—i.e., learners examine their thinking to uncover assumptions and constructs behind their actions to constantly question their strategies. These reflections add to metacognition an awareness and consideration of context, emotions, and other factors (Drigas et al., 2022b ; Wynn et al., 2019 ).

Therefore, to conceptualize metacognitive reflection, we can use the aforementioned definition from Verplanken et al. ( 2007 ) as a starting point, which we then enhance with work from Granville and Dison, who state:

Reflection becomes metacognitive when it involves evaluating one’s own thinking processes. Metacognitive reflection goes beyond mere information processing; it concerns awareness of the thinking and the learning; it is learning to learn, evaluate, and correct the information processing. Metacognitive reflection happens when the reflection becomes more articulated, elaborated, and creative; it goes beyond the task itself to the wider implications of the work at hand (Granville & Dison, 2005 ).

As this excerpt illustrates, Granville and Dison bring the concepts of reflection and metacognition together in an additive way to define metacognitive reflection. They take the idea of metacognition (i.e., the monitoring, regulation, and awareness of our knowledge and experiences) and add the processes of reflection (i.e., reflecting to support learning—that is beyond knowledge—via constant elaboration).

Additionally, we can enrich this definition through Cornoldi’s et al. ( 1998 ) and Grossman’s ( 2009 ) work, thereby underscoring how metacognitive reflection also involves an affective component. Cornoldi proposed:

Metacognitive reflection is not only represented by its most evident, aware, verbalizable portion; it also includes a part not so easy to verbalize that refers to affective characteristics that include: intuitions, sensations, emotions, autobiographical memories, and self-evaluations (Cornoldi et al., 1998 ).

Further enhancing this appreciation for affective characteristics, Grossman ( 2009 ) described how the individual’s mental structures change when moving from metacognitive to more intensive or transformative reflection levels. Grossman described this movement as a mature psychological space that allows inner experience (i.e., thoughts, perceptions, affect, and actions) to be an object available for responsible, self-authored, higher consciousness-driven, reflective observation which has the capacity to change one’s frame of reference. Grossman emphasized that reflecting on one’s thoughts and feelings is not a simple process of learning to make new distinctions; it requires a transformation in the way the mind is organized .

Finally, metacognitive reflection accounts for additional reflective dimensions such as context. We can harness the work of Mason et al. ( 2010 ) and Sawicki and Wegener ( 2018 ) to again enhance the definition of metacognitive reflection to account for these other factors. Mason, Boldrin, and Ariasi considered metacognition as

a reflective activity about knowledge and knowing in the finer-grained and context sensitive spaces in which they are activated … since different contexts trigger different resources.… Metacognition in context provides some preliminary evidence that high self-reflection in learning from multiple sources may also help the activation of more sophisticated beliefs in evaluating the knowledge at hand (Mason et al., 2010 ).

Similarly, Sawicki and Wegener defined metacognitive reflection as pertaining to one’s consideration of how a setting, thought, or action would affect one’s metacognitions (Sawicki & Wegener, 2018 ).

With this enhanced set of considerations in mind, it appears that metacognitive reflection does involve aspects of reflection and metacognition, but that it is also distinct from those two concepts. Metacognitive reflection can take various forms and can vary greatly depending on the factors that influence the reflection activity that follows metacognition (e.g., the emotional range in the reflection activity that colors the metacognitive work).

The missing theory of metacognitive reflection

While our integrative analysis has allowed us to merge several threads in the literature to develop a richer conceptualization of metacognitive reflection, engaging in the synthesis of insights into metacognitive reflection and theorizing how it works is a more elusive goal. Some researchers, whose arguments we have incorporated in our conceptualization, suggest that metacognition drives reflection (Grossman, 2009 ) and argue that “metacognitive activities … engage and encourage the development of reflection” (Lonie & Desai, 2015b ). Conversely, others propose that reflection promotes metacognition and, accordingly, metacognitive capacity is developed by promoting reflection (Gonullu & Artar, 2014 ; Tarricone, 2011 ). For instance, Tarricone’s work suggests that a dialectical connection exists between metacognition and reflection but that reflection is a facilitator of metacognition (Tarricone, 2011 ). Adding to this confusion, some researchers focus on metacognition being a component of reflection and vice versa (Quintana et al., 2005 ; Siddiqui et al., 2020 ; Waghmare et al., 2016 ), while others use the terms synonymously (Alt & Raichel, 2020 ; Hamm, 2014 ; Kuiper & Pesut, 2004 ; Levin, 1995 ; Lewis, 2019 ) and, finally, others acknowledge that they are distinct lines of research (Barley, 2012 ; Bartimote-Aufflick et al., 2010 ; McAdoo & Manwaring, 2009 ; Mitchell et al., 2009 ; Walters et al., 2015 ).

These are just some of the ways reflection and metacognition are conflated. No clear premises cut across the literature to help us understand and theorize metacognitive reflection as a separate concept. In other words, sometimes the term metacognitive reflection is inferred to capture elements of both constructs (Cacciamani et al., 2012 ; Desaute, 2009 ; Lysaker et al., 2019 ; Mitchell et al., 2009 ; Molesworth et al., 2011 ; Sawicki & Wegener, 2018 ; Scoresby & Shelton, 2014 ), and other times it is mentioned but not defined, in the absence of both constructs (Makalela, 2015 ; Mason et al., 2010 ; Salovich & Rapp, 2021 ). There are variations in definitions; for instance, Becker et al. (2023) conceptualized metacognitive reflection as “guiding people to systematically reflect on their decision-making strategies,” whereas Moshman characterized it “as an awareness of one’s own inferences and to recognize inference as a distinct source of knowledge” (Moshman, 1991 ). To date, the clearest work in this space has come from Granville and Dison, who suggested that learners evolve along on a continuum from thinking, to task-related reflection, to self-reflection, and finally to metacognitive reflection (Granville & Dison, 2005 ). Therefore, Granville and Dison’s work (Granville & Dison, 2005 ) is aligned with our thinking: metacognitive reflection begins with metacognition and then is furthered by cycles of reflection that bring awareness to factors such as emotions and context.

We conducted this TIR to offer some conceptual gardening (Veen & Croix, 2023 ) by constructing a lucid conceptualization of metacognitive reflection. To answer our research question—What is metacognitive reflection?—we argue that a productive conceptualization of metacognitive reflection is one that holistically captures elements of metacognition, reflection, and the accompanying emotions involved in that work. While variability and contradiction are rife, we argue that there is a productive way forward, but it does require taking a stance to align with a subset of authors working in this area. Therefore, in keeping with the work of Wald ( 2015 ), Chick et al. ( 2009 ), Hall and Higgins ( 2005 ), Granville and Dison ( 2005 ), and Merkebu et al. ( 2023 ), we propose the following conceptualization:

Metacognitive reflection involves heightened internal observation, awareness, monitoring, and regulation of our own knowledge, experiences, and emotions by questioning and examining cognition and emotional processes to continually refine and enhance our perspectives and decisions while thoughtfully accounting for context.

We offer this conceptualization of the phenomenon to support investigations into metacognitive reflection as a distinct phenomenon. We hope that this definition can help clarify how metacognitive reflection is foundationally different from reflection and metacognition and so should not be conflated with either term. We acknowledge that we offer a limited synthesis of the definitions and theories of metacognitive reflection; however, we note that this description is limited because, unfortunately, there is much variability in the limited literature available for integration. Therefore, we suggest that the definition we offer could serve as a foundation for future inquiry that would work to build a robust theory of metacognitive reflection. Or, if future research shows that this definition does not hold up, then we offer it as a starting point for either supporting or eschewing. Whether the definition holds in the future remains to be seen, but we hope it contributes to a clearer set of future research agendas.

As Dewey remarked, “In natural growth each successive stage of activity prepares unconsciously, but thoroughly, the conditions for the manifestation of the next stage” (Dewey, 1933 ). We contend that engaging in metacognition is a necessary first step from which the individual can then engage in deeper levels of reflective meaning-making. Thus, metacognitive reflection is employed to characterize the monitoring and regulation aspects of metacognition and the subsequent meaning-making process of reflection, which also involves astute awareness of emotions and context. In other words, we propose that metacognitive reflection begins with the process of metacognition and then is enhanced with cycles of reflection that bring additional considerations into the process. This, therefore, is how metacognitive reflection brings a shift in perspective—i.e., metacognition is thinking about thinking which is then augmented with the work of reflection.

We acknowledge that our position on metacognitive reflection is most aligned with the work of many others who frame reflection as a larger and more holistic construct than metacognition and as linked to transformative learning, self-regulated learning, spiritual intelligence, faith, higher-level awareness, transcendence, moral consciousness, reflexivity, and beyond (Baumgartner, 2001 ; Bhaskar, 2013 ; Bleakley, 1999 ; Branson, 2007 ; Drigas et al., 2022a ; Hetzner et al., 2011 ; Korthagen & Vasalos, 2009 ; Merkebu et al., 2023 ; Mezirow, 1994 ; Nys, 2002 ; Smith, 2011 ; Travis & Shear, 2010 ). Our position is most aligned with others who have posited that the work of metacognition is foundational to self-reflective monitoring because “without being able to at least describe the contents of one’s own mind, a reflection on those contents may not occur” (Demick & Andreoletti, 2012 ). Therefore, we propose that metacognition is the primary mechanism which then positions the individual to be able to engage in reflection. If we can imagine that metacognitive reflection involves an ascension of reflective activity, then we can conceive that researchers are desiring to capture what happens when metacognition is enhanced by the multitude of considerations that are part of the work of reflection (Merkebu et al., 2023 ).

Theoretical and practical implications

Based on prior theory and research, as well as the findings of this TIR, we suggest that when we cross the thresholds from cognition to metacognition to reflection, we move into a space where we can effectively regulate our thoughts and emotions (Merkebu et al., 2023 ). In this space, individuals can engage in deeper levels of reflection, enabling the development of awareness and novel insight. From this standpoint, metacognitive reflection is a whole-person perspective that considers both metacognitive regulation and wider reflective perspectives. An important consequence of this proposition is that it promotes a directionality to the work of metacognitive reflection: this work starts from basic metacognition and moves to more in-depth and intensive transformational levels. This orientation recognizes the need for authentic growth and transformation (Drigas et al., 2022a ; Grossman, 2009 ).

Given the new definition and conceptualization we offer, what are the implications for medical education? As Mieke Bal pointed out, the value of concepts is in what we do with them and how we can work with them. Metacognitive reflection can support valuable reconceptualization for lifelong learning. However, our analysis suggests that it is not appropriate for the literature to continue to conflate the terms metacognition, reflection, and metacognitive reflection. They are distinct concepts. Further, given their distinctions, our research findings suggest that medical educators should teach learners to engage in these processes at different times. For instance, perhaps it is most reasonable, at the undergraduate medical education level, to ask students to engage in metacognition. We propose that metacognition is the first step in learning how to question the knowledge we hold; it requires the learner to be aware of the ways in which we are thinking and how we are using our thinking to productively engage in learning. Then, students move through the medical education continuum and develop more advanced knowledge, skills, and attitudes, including adding reflection to their metacognition work. Perhaps when learners are at the end of their undergraduate training or entering graduate training, they can be expected to develop the ability to sustain metacognitive development and ascend from one layer to another via the complexities and nuances involved in reflection (Drigas et al., 2022a ). In this way, medical learners progress towards an end goal of being capable of engaging in metacognitive reflection: first they develop robust metacognitive skills and then they develop rich reflection skills that they harness to enhance their metacognition. In this way, the journey to becoming lifelong learners follows the journey of mastering metacognitive reflection skills.

This conceptualization of metacognitive reflection also offers medical educators the opportunity to identify why some medical learners might struggle to develop this important skill and how to engage in remediation efforts. Do learners struggle with foundational metacognition skills? If so, they might be guided to consider the differences between their metacognitive knowledge and metacognitive experiences. The educator can then help them develop skills to plan, regulate, monitor, and control their cognitive processes. However, if learners have sound metacognition skills, remediation can focus on their ability to engage in high-quality or productive reflection (El-Dib, 2007 ). Remediation effort would focus on helping learners develop the ability to make meaning of experiences from within situated contexts, continually expanding to consider a wider range of possibilities through spirals of reflection.

Finally, the role of emotion is different in metacognition, reflection, and metacognitive reflection. As argued by Shapiro, much of medical education’s hidden curriculum has encouraged students “to separate and distance themselves” from their own emotions and those of others. However, as the analysis in this paper suggests, if we understand when emotions come into play in metacognitive reflection—and indeed, in both metacognition and reflection—we can thoughtfully bring emotions back into each individual’s consideration at appropriate times. For instance, we contend that metacognition is a first step for engaging in reflective meaning-making. Therefore, metacognitive awareness of emotions is part of the very initial workings of these processes. Instead of divorcing emotions from this work, we argue that emotions are part of the primary processes, which individuals need to productively reappraise and regulate in order to embark on reflective meaning-making.

Limitations and future directions

This review addresses a gap in the metacognitive reflection literature; however, as our synthesis efforts revealed, the theory of metacognitive reflection and the processes by which it works have not been the focus of much research attention. The lack of literature in this area poses a limitation in our review. Important questions remain to be addressed: How do metacognitive processes carry over to impact engagement in reflection? Ford and Yore ( 2012 ) have cautioned that these constructs are converging “as the move toward constructivism has necessitated critical considerations of knowledge about thinking.” Should we conceptualize metacognitive reflection from an objectivist, subjectivist, or pragmatist epistemology? What would be the implications of choosing one orientation over another? Future research could address these gaps by studying what constitutes metacognitive reflection to develop robust theory. Furthermore, if the conceptualization of metacognitive reflection as we have described it is embraced by the community, research will be needed to construct instruments that measure individuals’ competency in this area, since it is a foundational competency required of practicing physicians.

This TIR offers a synthesis of the literature addressing metacognitive reflection. We offer a new definition of metacognitive reflection and highlight its salient features. We suggest a conceptualization that places metacognition as the first and foundational aspect of metacognitive reflection, from which individuals can then engage in iterative cycles of reflection. Our knowledge synthesis provides a coherent conceptualization of metacognitive reflection and proposes how this construct can be leveraged to serve medical education. Additionally, this review highlights that metacognition and reflection are not synonyms. They are related but distinct constructs that should not be used interchangeably.

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We would like to thank Anne de la Croix for her contribution to the development of the conceptual framework.

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JM full development of narrative and manuscript. LV refining narrative and discussions while taking lead on methods, analysis, and interpretation. MV contributed to the conceptual development, narrative, and discussion refinement. SH contributed to data screening, extraction, and helped with manuscript preparation. All authors contributed to the development and refinement of the manuscript.

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Merkebu, J., Veen, M., Hosseini, S. et al. The case for metacognitive reflection: a theory integrative review with implications for medical education. Adv in Health Sci Educ (2024). https://doi.org/10.1007/s10459-023-10310-2

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Validation of metacognitive academic writing strategies and the predictive effects on academic writing performance in a foreign language context

Mark feng teng.

1 Faculty of Education, University of Macau, Macau, China

Chenghai Qin

2 School of Foreign Languages, Hainan University, Haikou, China

Chuang Wang

3 Faculty of Education, University of Macau, Macau, China

Associated Data

The data that support the findings of this study are available on request from the corresponding author, Qin Chenghai. The data are not publicly available due to the possible information that could compromise the privacy of research participants.

This empirical study serves two purposes. The first purpose is to validate a newly developed instrument, the Metacognitive Academic Writing Strategies Questionnaire (MAWSQ), which represents the multifaceted structure of metacognition in an English as a Foreign Language (EFL) academic writing setting. The second purpose is to delineate the predictive effects of different metacognitive strategies on EFL academic writing performance. Data were collected from 664 students at a university in mainland China. Confirmatory factor analyses (CFA) provided evidence for the fit for two hypothesized models, i.e., an eight-factor correlated model and a one-factor second-order model. Model comparisons documented that the one-factor second-order model was a better model, through which metacognition functions as a higher order construct that can account for the correlations of the eight metacognitive strategies, pertaining to declarative knowledge, procedural knowledge, conditional knowledge, planning, monitoring, evaluating, information management, and debugging strategies. Results also provided evidence for the significant predicting effects of the eight strategies on EFL academic writing performance. The empirical evidence supports the transfer of metacognition theory from educational psychology to interpreting EFL academic writing.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11409-021-09278-4.

Introduction

Students learning English as a foreign language (EFL) struggle with academic writing. The challenges in enhancing EFL academic writing are multi-dimensional. One reason may be the lack of awareness and use of writing strategies (Ruan, 2014 ). EFL academic writing is acknowledged as a challenging component for university students (Teng, 2019a ). The challenges in EFL academic writing are exacerbated because of the limited English language input afforded to student writers. English academic writing competence has thus become a common concern in the EFL contexts. As student writers were described as having different repertoires of strategies in learning to write (Raimes, 1987 ), the level of “self-initiated thoughts, feelings, and actions that writers [used] to attain various literary goals” may have been different (Zimmerman & Risemberg, 1997 , p.76). Hence, the instruction of academic writing needs more attention.

In Chinese universities, regardless of discipline, English is a compulsory course. Students often need to take a compulsory English course that is typically limited to four hours per week. The amount of instruction time focused on English writing is limited, and students receive little practice in writing. In addition, the education system in China is exam-oriented. Facing pressures related to the national standardized test, such as the College English Test (CET), English teachers often focus on the instruction of grammar, accuracies, and paragraph structures (Woodrow, 2011 ). Indeed, students may find themselves confused with word choice, grammatical use, generation of ideas, and organization of structures (Wang & Wen, 2002 ). Due to time constraints, low motivation, and low English proficiency, writing still remains a challenging dimension in Chinese EFL teaching and learning (Reynolds & Teng, 2021 ). Teachers seem to lack incentive to cultivate students’ motivation and regulation for writing (Hall & Goetz, 2013 ). In recent years, Chinese universities are paying more attention to non-English majors’ academic writing performance to compete for global rankings. Students may feel more pressure because academic writing is not just related to the use of the English language but also involves gaining international recognition in a specific discipline.

In EFL contexts, teaching academic writing is product-oriented. For example, the curricula, syllabuses, and assessment related to academic writing are prescribed by administrative committees (Zhao, 2010 ). Related to this, students were passive and thus failed to “develop strong beliefs in the relevance and importance of writing” (Bruning & Horn, 2000 , p.26). This phenomenon may explain why student writers were not interested in having much involvement in the academic writing process, leading to the deprivation of self-regulation in academic writing. To become a proficient writer, they may exert effort to acquire knowledge of vocabulary and grammar, rather than to build an awareness of achieving high levels of self-regulation (Graham & Harris, 2000 ). Lacking “self-awareness, self-motivation, and behavioral skills” to implement knowledge for academic writing, student writers may not be able to “transform their mental abilities into academic skills” (Zimmerman, 2002 , p.65–66). Self-regulatory capacity thus becomes an important factor that may predict students’ academic writing performance.

Hence, there is a need for considering metacognition, self-regulation, and writing, for which learners need to rely on metacognitive strategies in self-regulating their writing processes. There is also a call for a need to innovate the teaching of EFL academic writing. For example, we may adopt process-oriented writing instruction, an emerging trend in EFL writing (Zeng, 2005 ), to the instruction of academic writing. We now recognize academic writing as a tremendously complicated cognitive act that requires planning, text generation, and revision (Flower et al., 1994 ). The instruction of academic writing may involve guiding students to reflect on their writing process and their use of metacognitive strategies. In realizing such a goal, student writers may face constraints in taking control of academic writing. The development of academic writing is in the hands of those who understand, plan, set goals for writing tasks and react to, and reflect on what has been written (Sasaki et al., 2018 ). In this respect, we see a potential in assessing EFL learners’ use of metacognitive academic writing strategies and the predictive effects of metacognitive writing strategies on academic writing. The purpose of the present study is thus twofold: (a) to validate a questionnaire about metacognitive strategies on academic writing; and (b) to explore the extent to which strategies predict EFL students’ academic writing performance. Findings can shed light on the understanding of metacognitive strategies on EFL academic writing. Teachers can thus gain insight on how to foster instruction of targeted metacognitive writing strategies for students. A final contribution is the potential for researchers to transfer educational psychology theory, e.g., self-regulation and metacognition, to EFL academic writing pedagogy.

Literature review

Metacognition.

Metacognition is multidimensional and domain-general in nature. Earliest stage of metacognition is developed from the theory of mind approach (Flavell, 1979 ). For example, metacognition is the practical application of theory of mind to cognitive tasks. Knowledge from theory of mind provides the conceptual underpinnings essential to the development of metacognition (Lockl & Schneider, 2006 ). Metacognition fills “a unique niche in the self-regulatory phylum,” through providing “domain general knowledge and regulatory skills that enable individuals to control cognition in multiple domains” (Schraw, 2001 , p. 7). Metacognition is thus a critical awareness of one’s own thinking processes and the executive processes in attempting to regulate their cognitive processes as a thinker and learner (Flavell, 1979 ).

Flavell ( 1979 ) delineated metacognition as serving two basic functions, namely the monitoring function and control of cognition. For example, metacognition, “through the monitoring function, is informed by cognition and, through the control function, informs cognition” (Efklides, 2008 , p.278). Efklides ( 2006 ) included metacognitive knowledge and metacognitive experiences as the monitoring function and metacognitive skills as the control of cognition. Flavell ( 1985 ) suggested person, task, and strategy knowledge comprised metacognitive knowledge. Based on Wenden’s ( 1998 ) assertions, person knowledge refers to a learner’s knowledge about his or her cognitive processes and any possible factors, such as age, language aptitude, and motivation, that may impact the learning performance. Task knowledge involves the knowledge essential to understanding the purpose, nature, and demands of various tasks. Strategy knowledge includes the knowledge of the strategies that can help achieve the goals and effectiveness of learning tasks. Paris et al. ( 1984 ) argued that metacognitive knowledge should include declarative, procedural, and conditional knowledge. Declarative knowledge refers to a learner’s skills, intellectual resources, and processing abilities. Procedural knowledge covers the knowledge required to figure out how to implement a task through deploying strategies. Conditional knowledge includes learners’ knowledge of discerning when and why to use specific strategies for a relevant task. Flavell ( 1985 ) also named metacognitive knowledge and metacognitive regulation as knowledge of metacognition and regulation of metacognition.

In addition to metacognitive knowledge, other researchers also called for attention to metacognitive experiences and metacognitive skills (e.g., Efklides, 2008 ). Metacognitive experiences refer to what individuals are aware of and what they feel when they must process information related to a coming task (Efklides, 2006 ). For example, metacognitive experiences included feelings and judgments of knowing, effort expenditure, solution correctness, task difficulties, task familiarity, and self confidence. Metacognitive experiences form a platform for an individual to build awareness of a task that needs to be performed. Metacognitive feelings are both affective and cognitive in nature and can be considered within the broader mechanism of self-regulation behavior.

Metacognitive skills were described as metacognitive strategies or metacognitive regulation, including planning, conflict resolution, error detection, and inhibitory control (Shimamura, 2000 ). Metacognitive skills comprise orientation strategies, planning strategies, cognitive processing strategies, monitoring strategies, and evaluation strategies (Veenman & Elshout, 1999 ). Brown ( 1987 ) defined metacognitive regulation as how learners identify distracting internal and external stimuli to sustain effort over time for executive functions. According to Schraw ( 1998 ), metacognitive regulation entails three skills: planning, monitoring, and evaluating. Planning refers to the ability to seek appropriate selection of strategies and adequate allocation of resources for relevant tasks. Monitoring is the ability to observe and check on the task performance. Evaluating taps into the ability to appraise the regulatory processes and learning products. Schraw and Dennison ( 1994 ) added two metacognitive strategies, i.e., debugging strategies and information management strategies. Debugging strategies refer to the ability to correct comprehension and performance errors. Information management strategies cover skills in processing, organizing, elaborating, and summarizing information efficiently. We developed the following figure to illustrate metacognition (Fig. ​ (Fig.1) 1) .

Multi-faceted elements of metacognition

The above figure provides insight into the conceptualization of metacognition. First, metacognition consists of two aspects: monitoring function and control of cognition. The feature of the metacognitive system is a kind of dominance relation. Thie metacognition system was for the flow of information. The information flow gives rise to a distinction between “control” and “monitoring” (Nelson, 1996 ). The three major stages, i.e., acquisition, retention, and retrieval, are between the control and monitoring levels. Metacognition is thus a conscious process, through which an individual is consciously aware of the monitoring and control processes. Second, the monitoring function includes metacognitive knowledge and metacognitive experiences while the control function involves initiation of metacognitive skills or strategies. Third, metacognitive experiences and skills direct learners’ ability to regulate their cognitive processes, for which we described as metacognitive regulation (e.g., planning, monitoring, evaluating). Reflection is a fundamental part of the plan-monitor-evaluate process. Finally, metacognition is an individual phenomenon that reflects one’s knowledge, experiences, and skills in metacognition. Metacognitive knowledge, metacognitive experiences, and metacognitive skills are interrelated. For example, metacognitive knowledge may influence learners’ metacognitive experiences, their feelings and judgments of writing efficacy, which in turn, influence their use of metacognitive strategies in self-regulating their writing process.

Metacognition, self-regulation, and writing

Metacognition reflects one’s ability in self-regulated learning (SRL). SRL incorporates three classes of regulation: covert (personal), behavioral, and environmental (Zimmerman & Risemberg, 1997 ). Covert self-regulation refers to the “adaptive use of cognitive or affective strategies” to reduce anxiety in learning. Behavioral self-regulation pertains to “the adaptive use of a motoric performance strategy.” Environmental self-regulation involves “the adaptive use of a context-related strategy” (p.77). When a learner can exert strategic control over the three classes of regulation, he or she can be described as a learner of metacognitive awareness (Zimmerman, 1989 ). As Zimmerman and Schunk ( 2001 ) argued, SRL includes parameters—personal processes, environmental events, and behavioral attributes—that enable a learner to identify a topic, set goals to become familiar with the topic, adopt strategies to examine the topic, and evaluate and modify the relevant strategies for a deeper understanding of the subject matter. Indeed, self-regulated learners can often draw upon different metacognitive strategies to discern how to control their beliefs, behaviors, and external environments in the learning process (Zimmerman, 2013 ), thus allowing them to regulate “learning strategies as well as internal and external learning resources (e.g., motivation, learning environment)” (Ziegler et al., 2011 , p.76). Hence, SRL reflects learners’ ability to “plan, monitor, and regulate” their learning through taking control of their “thoughts, feelings, and actions” (da Silva Marini & Boruchovitch, 2014 , p.323). SRL strategies were acknowledged as building upon cognition, metacognition, social behavior, and motivational regulation (Oxford, 2013 ; Zimmerman, 2011 ). Learners deployed different metacognitive strategies to “guide them toward being effective learners without reliance on teacher-imposed structure and control” (Andrade & Evans, 2012 , p.21). Oxford ( 2013 ) categorized strategies into four groups: metacognitive strategies that guide the planning, monitoring and evaluating process; affective strategies that manage emotions and motivation; cognitive strategies that analyze and synthesize information; and social-interactive strategies that emphasize collaboration. Those various strategies form the basis for the strategic self-regulation model. Exploring how metacognitive writing strategies influence students’ EFL academic writing performance becomes necessary. The present study thus focuses on metacognitive strategies, a central dimension of SRL strategies.

Metacognitive strategies influence writing. In an early model on thinking and speech (Vygotsky, 1987 ), transition from thought to word was described as a very complex process and required deliberate analytical actions. Although Vygotsky’s model did not focus on writing, based on this model, we may assume that writers need to create a web of meaning connecting prior and present experiences and knowledge to maximally compact inner speech to be understood by the readers. Translating thoughts into words requires learners to repeatedly revise their output to a standard of quality. Such demands are assumed to require varied metacognitive strategies to enhance their effectiveness in writing.

Later models of the writing process acknowledged the strategies involved in planning, monitoring, and reviewing the writing process. For example, based on an early writing model proposed by Flower and Hayes ( 1980 ), writing was conceptualized into three components, i.e., task environment, long-term memory, and the writing process. The process of writing was described to include planning the writing (e.g., generating information, setting goals, and organizing information), translating ideas into text, and reviewing the draft (e.g., evaluating and revising text). In a later cognitive model of knowledge telling/knowledge transforming (e.g., Bereiter & Scardamalia, 1987 ), two strategies, i.e., rhetorical and self-regulatory strategies, were suggested as mental subroutines that can enhance writing. The two models provided insight into the cognitive interactive aspects of writing. The cognitive act of writing spawned investigation of differences in writing between expert and novice student writers. Such differences included strategies in planning, translating and reviewing, and monitoring. Indeed, compared to experts who could employ cognitive methods to garner and sustain affective experiences and motivation, novices were less able to set writing goals, monitor their output based on their writing goals, and revise text at an organizational level (Teng & Huang, 2019 ). Hence, writing activities are usually “self-planned, self-initiated, and self-sustained,” for which learners need “self-initiated thoughts, feelings, and actions” to attain various literary goals, including “improving their writing skills as well as enhancing the quality of the text” (Zimmerman & Risemberg, 1997 , p.76). The assumption is that if skilled writers can employ metacognitive strategies to control the triadic influences in writing, novice writers can also benefit from instruction on relevant metacognitive strategies.

Based on the above models, metacognition may pervade the writing process. Learners need to rely on metacognitive strategies to plan, monitor, and regulate their writing process. Indeed, the meaning-making process is a conscious process (Flower, 1989 ). Such a process may reflect the interconnection between metacognition, self-regulation, and writing. For example, construction of meaning is a part of metacognitive development. Such an argument may reflect Hyland’s ( 2003 ) writing process paradigm, in which writing is “essentially learnt, not taught” (p. 18). We also see the value of possibly exploring metacognitive strategies for helping learners express meaning in writing.

Some empirical studies support the importance of metacognition and self-regulation in writing. For example, instruction about metacognitive writing strategies (i.e., planning, feedback handling, monitoring, and evaluating) facilitated students’ reflection, monitoring, and evaluation of the metacognitive processes (Teng, 2016). Similarly, Bui and Kong ( 2019 ) incorporated metacognitive instruction for peer review in L2 writing and claimed that their training of metacognitive strategies strengthened learners’ belief and perceptions and increased their level of engagement and collaboration for L2 writing. As argued by Sun and Wang ( 2020 ), the effectiveness of metacognitive writing strategies may be related to learners’ strengthened self-efficacy belief. Ma and Teng ( 2021 ) described different learners’ metacognitive knowledge and the relationship with writing development. Their findings showed that learners of different language proficiency demonstrated different development trajectories of metacognitive knowledge and writing development.

In a recent study (Teng, 2020 ), instruction on metacognitive strategies included two groups: group feedback guidance and self-explanation guidance. Results collected from 120 Chinese students revealed the positive effects of group metacognitive support on writing. Metacognitive awareness can help learners manage themselves and prevail over writing-related setbacks. As argued by Hacker et al. ( 2009 ), metacognitive awareness can help writers maintain a “privileged position,” in which they can “generate the thoughts they wish to write, and monitor and control that generation of thoughts… [while they] translate those thoughts into writing, and they monitor and control that translation” (p. 161). In a longitudinal study that explored metacognitive awareness, writing, and self-regulation (Negretti, 2012 ), data were collected from journals written by 18 students. Results showed that metacognitive awareness mediates learners’ perceptions of writing tasks and self-regulation. For example, metacognitive awareness facilitates learners’ ability to adapt their strategic choices to the specific writing requirements. Self-regulatory experiences lead to an enhanced awareness of conditional and personal strategies. In particular, monitoring and evaluation are associated with learners’ writing task perception and their awareness of effectiveness in metacognitive writing strategies. Teng's ( 2019b ) study focused on instruction of metacognitive strategies for self-regulated learning. Results showed the effectiveness of fostering metacognitive strategies for young learners' writing, in line with Harris and Graham’s ( 2009 ) research. In addition, promoting automaticity of metacognitive strategies resulted in “spare attentional resources that contributed to high-level processes of generating ideas and organizing them into sentences” (Teng, 2019b , p.292). As supported in a longitudinal study (Teng & Zhang, 2021 ), learners’ L2 writing development was dependent on their initial level of metacognitive knowledge.

The above studies established the basis for understanding the relationship between metacognition, self-regulation, and writing. Challenges in developing learners’ metacognitive strategies may be a reason for learners’ lack of proficiency in writing. It is essential to foster learners’ awareness of metacognitive strategies. As metacognitive strategies are multidimensional, exploring the different strategies used to gain insights into learners’ strengths and weaknesses and how such awareness can predict learners’ writing performance, appears to be essential.

Language learning strategies based on SRL and metacognition

Learning strategies are “cognitive plans oriented toward successful task performance” (Schunk & Zimmerman, 2012 , p.62), or the tools for learners to achieve active, self-directed involvement in developing learning capacity (Wenden & Rubin, 1987 ). Strategies include activities or techniques for selecting and organizing information, rehearsing learned materials, allocating new materials for information in memory, and creating and maintaining a positive learning climate (e.g., enhancing self-efficacy) (Weinstein & Mayer, 1986 ). Strategy use becomes an indispensable part of SRL because learners can employ strategies to control information processing involved with learning. In an early study (O’Malley & Chamot, 1990 ), learner strategies in second language acquisition emerged from the need to identify the characteristics and behaviors of effective learners. In relation to this, successful language learners could associate new information with existing information in long-term memory and build increasingly intricate and differentiated mental structures. One aspect that distinguishes expert learners from novice learners is more effective use of learning strategies. As documented by Oxford ( 1990 ), efficient learners are more likely to use metacognitive strategies, including planning, organizing, and evaluating, that may help them take executive control over their learning. Expert learners are also more willing to use cognitive strategies, including analyzing, reasoning, and transferring information, and summarizing, to achieve better learning outcomes. Exploring strategies that contribute to learning and ways in which strategies interact with social and psychological variables in L2 contexts appears necessary.

In recent years, some researchers revealed the nature of self-regulated writing strategies and the potential of L2 writing strategies for enhancing writing. For example, Teng and Zhang ( 2016 ) developed a self-regulated writing strategies questionnaire to understand Chinese EFL students’ use of writing strategies. The questionnaire included multiple dimensions of self-regulation, covering cognition, metacognition, social behavior, and motivational regulation. The four dimensions included nine strategies, which were correlated with each other. Specifically, strategies such as text processing, planning, monitoring, evaluating, feedback handing, emotional control, and motivation were significant predictors for EFL students’ writing proficiency. In a recent study, Teng and Huang ( 2019 ) showed that in addition to metacognitive writing strategies for self-regulating the writing process, the other two facets of metacognition, namely metacognitive knowledge and experiences, can also predict the students’ writing performance. Moreover, metacognitive knowledge and regulation collectively accounted for 62% of the variance in writing performance scores (Teng, 2019a ).

The above studies highlighted the strong association between metacognition and writing performance. Writing strategies based on metacognition theories can be applied in EFL writing settings. Although such insights can shed light on academic writing strategies and performance, the above studies failed to explore metacognitive writing strategies in EFL academic writing contexts, let alone an exploration of how the interplay of the various dimensions of metacognition can influence academic writing performance in EFL contexts. Academic writing is not just related to the use of the English language structures, as required in writing, but also involves an ability to define the intellectual boundaries of their disciplines and specific areas of expertise. Bearing in mind the lack of research on academic writing, this empirical study aims to explore the different dimensions of metacognitive academic writing strategies and document how the strategies predict EFL academic writing performance. This study intends to add knowledge to the adoption of self-regulation and metacognition theories in EFL academic writing contexts.

The present study

Despite conceptual and methodological differences in the above-reviewed studies, metacognition functions as a determining element in writing performance. The present study aims to validate a questionnaire on metacognitive academic writing strategies and to explore the predictive effects of various strategies in EFL academic writing. The focus of the present study aims to explore the relationship between each subcomponent of metacognition and EFL academic writing. We propose two structural models to understand the dimensions of metacognitive strategies in EFL academic writing. The first model is an eight-factor correlated model of metacognitive strategies in EFL academic writing. This model splits the 43 items into eight components framed with the metacognition theory. The second model is a one-factor second-order model that explores metacognitive strategies in EFL academic writing. The second model is a competing hierarchy model. In this model, metacognition functions as a single higher-order common factor. We postulate that this common factor could account for the correlations of the eight lower-order strategies. The present study aims to explore the following questions:

• What structural model best represents the dimensions of metacognitive academic writing strategies?
• To what extent do metacognitive academic writing strategies predict EFL proficiency in academic writing?

Participants

The participants consisted of 664 junior students from a university in the southwestern region of mainland China. The reason for choosing third-year students was because they attended the English for Academic Purpose (EAP) course. Although the learners had more than ten years of experience in learning English, they did not have much experience in academic writing. A total of 752 learners responded to the questionnaire, but the data from 664 students were valid after an examination for missing values, normality, and homogeneity. The participants were informed that when attending this study, they would be asked to complete a survey and perform some writing exercises. They attended the study voluntarily. The consent form stated that they would receive a coupon for attending this study. Participants provided basic information, e.g., age, gender, and years of English learning experience for the consent form. Mandarin Chinese was their first language. The mean age of the participants was 22.13. Of the 664 students, 363 were men and 301 were women.

Questionnaire development

The Metacognitive Academic Writing Strategies Questionnaire (MAWSQ) was developed for the present study through five processes: item generating, consulting references, initial piloting, psychometric evaluation, and exploratory factor analysis (EFA). At the first stage of item generation, a multi-method technique was adopted (see Appendix A ). This technique was focused on helping learners reflect on their writing practice and strategies through some writing exercises. Different academic writing exercises were listed at the beginning of item generation. The learners were then asked questions related to metacognition. According to Dörnyei ( 2014 ), involving learners in the process of generating items can ensure the suitable quality of constructs for the questionnaire. To this end, 20 students who were diverse in terms of gender, year level, and disciplinary major were involved. They were invited to describe writing practice and strategies they had possibly used after doing some writing exercises. Based on content analysis of the transcription of learners’ responses, we generated 60 items related to metacognitive academic writing strategies. The second stage involved consulting relevant literature on SRL or metacognitive language learning strategies (Oxford, 2013 ; Schraw & Dennison, 1994 ; Wolters & Benzon, 2013 ). We compared the items we generated with those in established references and selected items that fit with the metacognition and self-regulation theories. Such a procedure imparted construct validity to a questionnaire and the items were judged as appropriate based on relevant theories. The third stage involved initial piloting. We returned the questionnaire to the 20 students to check the items. For example, they checked whether items were ambiguous or not relevant. We eliminated two double-barreled items based on learners’ feedback. The fourth stage involved psychometric evaluation. Two experts in the field of language-learning strategies were invited to examine the items. They scrutinized the theoretical rationale, examined the items, and rated the degree to which the items matched the constructs defined in the present study. Based on their feedback, we eliminated five items. The final stage was an EFA analysis, which was based on another sample of 391 students of similar background. Based on EFA, we deleted 10 items that were found to have a factor-loading value of less than 0.30. The present study finally included a 43-item scale that met the minimum requirement of cases-to-variables ratio (5:1) analysis (Field, 2009 ). The final sample of 664 learners also meets the assumptions of linearity, singularity, and homogeneity. Appendix B presents the 43 items of MAWSQ.

The present study adopted a seven-point Likert scale, which ranged from one (Strongly disagree) to seven (Strongly agree). Such a scale could help understand the trait features of the strategies. The mean scores of each sub-component of MAWSQ were used. As learners reported very few metacognitive experiences, the MAWSQ mainly includes two main components: metacognitive knowledge and metacognitive regulation. In terms of metacognitive knowledge, three categories, i.e., declarative knowledge, procedural knowledge, and conditional knowledge, were established. With regard to metacognitive regulation, five categories, i.e., planning, monitoring, evaluating, debugging, and information management, were outlined. Cronbach’s alpha was reported to check the internal consistency of responses to items. In terms of declarative knowledge, procedural knowledge, and conditional knowledge, the Cronbach’s alphas were 0.792, 0.795, and 0.738, respectively. In terms of planning, monitoring, evaluating, debugging, and information management, the Cronbach’s alphas were 0.809, 0.826, 0.871, 0.812, and 0.809, respectively. The questionnaire was developed in a bilingual version. The participants completed the Chinese version.

Academic writing test

This is a single measure of academic writing performance. This test evaluated learners’ ability to understand the topic, provide details, outline problems, and provide arguments for a specific academic discipline based on their own knowledge or experience. The main focus of the test was to evaluate the learners’ academic writing performance in terms of linguistic competence, critical thinking, and articulation of ideas. In this academic writing test, learners were required to write an essay on a topic related to medicine and health. The topic was chosen in responding to the institutional advocate of enhancing students’ awareness of self-care needs due to the COVID-19 pandemic. Learners were presented with six pictures. The learners described what they perceived about the pictures, connected the pictures, and produced a short essay. Each picture was accompanied by some key words. For example, the first picture was about “bacteria and the flu.” The learners were provided with a list of words for writing (e.g., bacteria, pills, vitamins, anatomy, donate, chemist, mask, beaker, and petri dish). The list of words also included the Chinese meanings for better comprehension by the learners.

The marking scheme was based on four components of writing rubrics adopted in the university, i.e., task achievement, coherence and cohesion, lexical resource, and grammatical range and accuracy. The Cronbach’s alpha for the four components of this test ranged from 0.811-0.875, indicating a sound reliability. In order to ensure the face validity of the test (i.e., the accuracy of content), the study involved three researchers with expertise in L2 writing. The total possible score for the test was 24 points, with 6 marks for each component. Three experienced EFL teachers were paid to rate the writings for one class. There were 45 raters involved for the 15 classes where the participants were from. The participants’ identity was not revealed to the raters. Prior to scoring, raters had a discussion on marking and sample writings. In each class, the first two raters marked the writings, and when disagreements on marking arose, a third rater would be called upon to have a discussion. The differences were solved based on the majority’s opinion. Cohen’s kappa coefficient (κ) was employed to measure interrater reliability for the 15 classes. The values ranged from 0.71-0.79, indicating sound interrater reliability.

The questionnaire was administered and completed online. Participants scanned a QR code and completed the questionnaire at the end of the EAP course. The reason for administering the questionnaire at the end of the writing course was to elicit learners’ reflection on the use of strategies. Although a strict time limit to complete the questionnaire was not enforced, the learners spent an average of 15 min completing the Chinese version of the MAWSQ. On the following day, learners completed the academic writing test in class. The time allowed for this test was one hour, following the exam practice for writing at this university. The writing test was completed using a paper-and-pencil format. The teacher in each class helped proctor the process. They acted as the test administrators, ensuring that all testing sessions were conducted in the same manner and that participants received the same explanations.

Data analysis

After testing the plausibility through EFA, the data were analyzed through a series of confirmatory factor analyses (CFA). Data were analyzed through SPSS AMOS program, version 24. CFA is for testing a theoretical model through confirming factors, correlations, covariance patterns, and residual or error values within a data matrix (Byrne, 2016 ). The maximum likelihood (ML) estimation method was adopted to evaluate the hypothesized models. The models were also evaluated by statistical means to determine the adequacy of its goodness-of-fit (GFI) to the sample data, e.g., it can measure the hypothesized model and the observed covariance. Based on Byrne’s ( 2016 ) research, the CFA data were also based on following omnibus fit statistics, including a chi-square statistic, the degrees of freedom (df), p value, the ratio of chi-square χ 2 divided by the df, the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), the comparative fit index (CFI), and the Tucker–Lewis Index (TLI). Model fit should commonly meet most of the following criteria: the value of GFI should be over 0.90, the value of RMSEA should be less than 0.1, the value of SRMR should be less than 0.08, and the values for CFI and TLI should be equal to or larger than 0.90 (see Kline, 2011 for details). One issue to bear in mind is that we may not achieve satisfactory levels for all indices in a model. In an attempt to compare the hypothesized nested models, we used chi-square statistics to select the appropriate structural model. For example, based on Kline’s ( 2011 ) research, we examined the differences in chi-square as a ratio of the difference in df. The significant p -value indicated that the reference model may be a more appropriate model.

The final procedure was to evaluate the predictive effects of various dimensions of metacognitive academic writing strategies on academic writing performance. We adopted linear regression analysis to understand the extent to which different strategies predicted EFL learners’ academic writing performance.

Descriptive statistics and normality check

The mean scores of the eight factors ranged from 4.24 to 4.84 with standard deviations ranged from 0.96 to 1.07. The values for skewness ranged from -0.013 to 0.172. Finally, the values for kurtosis ranged from 0.166 to 0.627. According to Kline ( 1998 ), the data fit with the requirements of normality assumption. Table ​ Table1 1 presents the means, standard deviations, and normality check for the eight strategies.

Means, standard deviations, and normality check for the eight strategies

DK Declarative knowledge, PK  Procedural knowledge, CK Conditional knowledge, P Planning, M Monitoring, E  valuating, IMS Information management strategies, DS  Debugging strategies

CFA results

Figure  2 shows the standardized results for the three-factor correlated model. The CFA results revealed that the standardized estimate loadings from observed variables to the unobserved variables were all higher than 0.50, which is a benchmark value that indicates an acceptable effect size (Raykov & Marcoulides, 2008 ). The results confirmed the different variables of metacognition were distinct but correlated.

Eight-factor correlated model of metacognitive strategies for EFL academic writing with standardized regression weight ( N  = 664). Note: DK = Declarative knowledge; PK = Procedural knowledge; CK = Conditional knowledge; P = Planning; M = Monitoring; E = Evaluating; IMS = Information management strategies; DS = Debugging strategies

The next step was to check whether the model fit the data well. Results are in Table ​ Table2 2 .

Model fit indices from confirmatory factor analysis

Results in Table ​ Table2 2 showed an acceptable model fit (χ 2 664  = 2374.052; df = 832; p  < 0.001; χ 2 /df = 2.853; GFI = 0.912; RMSEA = 0.054, SRMR = 0.053; CFI = 0.917; TLI = 0.918). The results provided evidence for validity of the internal structure of the construct measured. The next step was to present the results of the one-factor second-order model (Fig.  3 ).

One-factor second-order model of metacognitive strategies for EFL academic writing ( N  = 664)

In Fig.  3 , the coefficients of the eight strategies ranged between 0.71 and 0.79 on the construct of metacognition, which indicates internal structure of the questionnaire (Kline, 2011 ). Again, the standardized estimate loadings from observed variables to the unobserved variables were all higher than 0.50, which represents an acceptable effect size (Raykov & Marcoulides, 2008). The results confirmed metacognition functioned as a single common factor that included the eight distinct but correlated factors. Results from the model fit indices are also in Table ​ Table2 2 .

Results in Table ​ Table2 2 also showed an acceptable model fit (χ 2 664  = 2506.382; df = 852; p  < 0.001; χ 2 /df = 2.942; GFI = 0.919; RMSEA = 0.055; SRMR = 0.057; CFI = 0.909; TFI = 0.908). Again, the overall results showed that the model fit the data well (Kline, 2011 ). The next step was to compare the two models.

Model comparisons indicated significant improvement from Model 1 to Model 2. The chi-square values in the two models suggested significant differences (χ 2 M2  − χ 2 M1  = 132.33; df M2  − df M1  = 20; p  < 0.001), indicating the indices of Model 2 improved significantly over Model 1. Hence, the second model demonstrated that metacognition functions as a hierarchical construct which explains the eight metacognitive strategies. This is an evidence for highlighting metacognition in the EFL academic writing classroom.

Predictive effects of metacognitive academic writing strategies on academic writing

Results for the correlation between different strategies are in Table ​ Table3 3 .

Pearson correlation coefficients for the eight metacognitive strategies

DK Declarative knowledge, PK  Procedural knowledge, CK Conditional knowledge, P Planning, M  Monitoring, E Evaluating, IMS Information management strategies, DS Debugging strategies

The Pearson correlation coefficients in Table ​ Table3 3 suggest that the DK of the metacognitive component was strongly correlated with PK ( r  = 0.661), CK ( r  = 0.597), P ( r  = 0.606), M ( r  = 0.618), E ( r  = 0.676), IMS( r  = 0.511), and DS ( r  = 0.529). The correlations were all higher than 0.50, which means that at least 25% of the variance of one component was explained by the other. Table ​ Table5 5 shows the correlation between academic writing performance and each of the strategies.

Linear regression results ( N  = 644)

Dependent variable: AWP

WP  Academic writing performance, DK Declarative knowledge, PK Procedural knowledge, CK Conditional knowledge, P Planning, M Monitoring, E Evaluating, IMS Information management strategies, DS Debugging strategies’

* p  < .05, ** p  < .01, *** p  < .001

Results in Table ​ Table4 4 revealed that each of the eight strategies were significantly correlated with academic writing performance ( p  < 0.001). In particular, AWP was strongly correlated with DK ( r  = 0.720), PK ( r  = 0.778), CK ( r  = 0.803), P ( r  = 0.798), M ( r  = 0.829), E ( r  = 0.829), IMS( r  = 0.752), and DS ( r  = 0.750). The final step was to present the regression results (Table ​ (Table5 5 ).

Pearson correlation coefficients on the eight strategies and academic writing performance

AWP Academic writing performance, DK Declarative knowledge, PK Procedural knowledge, CK Conditional knowledge, P Planning, M  Monitoring, E Evaluating, IMS Information management strategies, DS Debugging strategies

* p  < .05, ** p  < .01

Results showed that the eight metacognitive strategies as a whole explained approximately 87% of the variance in the EFL students’ academic writing scores, F (8, 635) = 529.666, p  < 0.001, R 2  = 0.87, adjusted R 2  = 0.868. The different predicting effects of each strategy on AWP are delineated in Fig.  4 .

The predictive effect of metacognitive strategies on academic writing performance

As shown in Table ​ Table5 5 and Fig.  4 , individual sub-categories of metacognition, i.e., DK, PK, CK, P, M, E, IMS, DS, all yielded significant predictions for the EFL learners’ academic writing performance ( p  < 0.001).

The first purpose of the present study is to validate a self-reporting instrument, i.e., the Metacognitive Academic Writing Strategies Questionnaire (MAWSQ). The questionnaire was based on the trait features of metacognition. The results provided evidence about the factorial structure of the instrument, entailing metacognitive knowledge and regulation. The findings document the utility of MAWSQ with satisfactory psychometric properties. The eight metacognitive strategies were distinct but correlated with each other. In addition, the eight strategies were reliable in terms of conceptual and empirical grounds. Model comparisons were based on two competing models: the one-factor second-order model (Model 2) and the eight-factor correlated model (Model 1). Model 2 showed a better model fit than Model 1. The empirical findings support that metacognition functions as a theoretical construct that can account for the significant correlations of eight lower-order metacognitive strategies in academic writing. Consistent with Schraw and Moshman’s ( 1995 ) study, the construct of metacognition accounts for a “systematic structure of knowledge” that can be used to explain and predict a broad range of learning strategies (p.356). The present study also sheds light on the metacognition theory that deploys a range of strategies related to declarative knowledge, procedural knowledge, conditional knowledge, planning, monitoring, evaluating, information management strategies, and debugging strategies (Schraw & Dennison, 1994 ). These strategies are distinct but interact with one another in the metacognitive process. As Paris and Winograd ( 1990 ) explained, metacognition is a cyclical process of engaging “self-appraisal and self-management of cognition” (p.17), through which cognitive and personal reflection on knowledge states and abilities, and mental processes that orchestrate different dimensions of problem-solving function together and provide an overall view of learners’ thought processes. The sums of the eight strategies collectively indicate the learners’ overall metacognition levels. The various strategies included in the metacognition construct help in the conceptualization of learners as individuals who need to be actively engaged in the orchestration of knowledge construction for academic writing.

In the present study, the eight strategies were interpreted with reference to the two core paradigms of metacognition, i.e., metacognitive knowledge and regulation, as conceptualized in early studies (Flavell, 1979 ; Schraw, 2001 ; Wenden, 1998 ). The metacognitive knowledge dimension comprises declarative knowledge, procedural knowledge, and conditional knowledge, reflecting learners’ beliefs and knowledge about tasks, strategies, and learners themselves that affects the course and outcome of cognitive enterprise (Flavell, 1979 ). The metacognitive regulation dimension comprises planning, monitoring, and evaluating, reflecting the essential role of regulation in the SRL process (Winne & Hadwin, 2010 ). The metacognitive regulation dimension also includes information management strategies and debugging strategies, representing learners’ metacognitive control of their learning behaviors in adjusting how the material is used, the task is performed with the material, and how well the material meets the goals (Nilson, 2013 ). The metacognition dimensions included in the questionnaire conceptualized how learners “[understood] what skills, strategies, and resources [were] required to complete a task as well as how and when to use those skills and strategies” (Schunk & Zimmerman, 2006 , p.360). The findings showed a positive and significant relationship among the various strategies of metacognitive knowledge and regulation, resonating with previous studies (Teng, 2016; Pugalee, 2001 ). The strong connection between metacognitive knowledge and regulation supports the assertion that EFL students need to deploy a rich repertoire of cognitive, metacognitive, and regulatory knowledge, skills, and strategies in taking control of the learning process (Teng & Zhang, 2016 ). As argued by Wolters ( 1999 ), strategies in metacognitive knowledge and regulation can “increase students’ level of cognitive engagement, overall level of effort, and subsequent achievement within an academic setting” (p. 285). The positive correlations between metacognitive knowledge and regulation also lend support to the argument that metacognition provides a “framework for understanding one’s as well as the others’ cognition and thus guides the interpretation of situational data so that proper control decisions are made” (Efklides, 2006 , p.4). The present study provides a strong argument for viewing metacognition as a prominent facet for developing self-regulated learners (Efklides, 2008 ), as well as “social, motivational, and behavioral processes” as a student writer (Zimmerman & Risemberg, 1997 , p.76).

The second purpose of the present study is to evaluate to what extent that the different strategies related to metacognitive knowledge and regulation predict the EFL learners’ performance in academic writing. The results revealed that each of the eight parameters of metacognition was significantly correlated with the EFL students’ academic writing performance. The argument presented—at least for this given sample and the adopted test, procedural knowledge, declarative knowledge, conditional knowledge, planning, monitoring, evaluating, information management strategies, and debugging strategies—demonstrated a strong and significant correlation to the EFL students’ academic writing performance. The eight metacognitive strategies as a whole explained 87% of variance in the EFL learners’ academic writing performance, lending support to the validity of the one factor second-order model, in which metacognition functions as an integrated construct that is essential to academic writing. The findings lend support to Flower and Hayes’s ( 1980 ) cognitive writing model that acknowledges the skills in planning, monitoring, and reviewing process writing. Corresponding to the triadic personal, behavioral, and environmental influences on writer self-regulation (Zimmerman & Risemberg, 1997 ), academic writing requires the adaptive use of cognitive or affective strategies, motoric performance strategies, and context-related strategies. As argued by Karlen ( 2017 ), apart from knowledge about high-quality compositions, students who aim to achieve effective academic writing also need to acquire metacognitive knowledge to regulate and monitor the writing process and to use strategies successfully.

In particular, within the metacognitive knowledge dimension, procedural knowledge, declarative knowledge, and conditional knowledge significantly predicted academic writing scores with a large effect size in our study. Corresponding to previous studies (Brown, 1987 ; Schraw, 2001 ), students can become strategic learners when they possess declarative, procedural, and conditional knowledge. Such findings support an argument that EFL learners need to discern knowledge about what strategies are available, how to use those strategies, and how to use elaboration in studying materials to master academic writing. The findings also support studies that argued for the essential role of metacognitive knowledge in fostering active engagement in applying their knowledge about the writing process, identifying the type of strategies valuable in the writing development, and enhancing students’ writing outcomes (Kim, 2013 ; Ruan, 2014 ).

Within the metacognitive regulation dimension, planning, monitoring, evaluating, information management strategies, and debugging strategies significantly predicted academic writing scores with a large effect size in the current study. These findings support the important role of metacognitive regulation in writing (Teng, 2019a ). For example, learners who were more self-regulated in writing appeared to be more skilled in writing. Planning comprised items in goal setting, time management, and writing-resources planning. In examining the EFL learners’ use of planning strategies, we detected that Chinese EFL students could plan ahead and organize their thoughts to produce effective written essays. This strategy plays a critical role in ensuring EFL academic writing performance. Learners’ development in academic writing could be deemed as a complex process as its development is subject to student writers’ strategic behavior in seeking knowledge and adjusting planning behaviors. Learners who have planned well for academic writing would normally be the ones who possessed strong metacognitive awareness of writing-oriented goals (Zhang & Qin, 2018 ). The items for the monitoring strategy focused on textual-level processing, self-regulation from distractions, lexical resources, and writing adjustment. Corroborating previous studies that stressed the role of monitoring in sustaining writing efforts (Xiang, 2004 ), such findings supported the assertion that students who could use multiple strategies to conduct metacognitive management of their writing processes might achieve better performance in academic writing. The items in the evaluating strategy focused on self-assessment of language use, writing quality, organization, and content. The findings indicated the need to stimulate learners’ self-reflection in evaluating writing performance (Teng, 2020 ). As argued by Travers et al. ( 2015 ), learners’ metacognitive awareness of strategies, such as self-evaluation and self-reflection in strategy use, expedited learning outcomes. Items in the information management strategy focused on organizing, elaborating, summarizing, and selective focusing. The findings suggest the role of accessing, storing, and analyzing information for writing (Kenkel & Yates, 2009 ). Items in the debugging strategy involved learners’ behaviors to correct comprehension and performance errors. The findings indicated the need to develop debugging skills for writing, and learners’ reported use of this cluster of strategies might help boost their willingness to be self-reflective in correcting their academic essays.

Limitations and implications

Some limitations for the present study were present. First, even though we demonstrated high content validity through theoretical and empirical validation of the selected strategies, the strategies listed in the questionnaire were still limited. For example, we did not include metacognitive experiences—another important dimension of metacognition—in part, due to the time the learners would be willing to spend in data collection. We did not identify enough strategies related to metacognitive experiences from students’ interview data, for which we did not include this dimension in the questionnaire. Second, the present study is based on a self-reported questionnaire. The use of surveys may not accurately assess learners’ actual metacognitive awareness and behaviors as it is based on self-reported strategy use. Future studies should triangulate the quantitative data through adding qualitative data to gain a more detailed and in-depth portrayal of learners’ metacognition. Third, the academic writing genre is different from the traditional writing genre. The writing test should also cover additional types of exercises that can measure different academic writing skills. However, we only employed a single measure of writing performance. Finally, individual differences, including language learning experiences and language proficiency level, could also impact student writers’ performance (Teng & Huang, 2019 ). Future research could examine how learner variables influence metacognition and learners’ academic performance.

Despite the limitations, the present study is an innovative study that explores the intercorrelated relationship between metacognition and EFL academic writing. First, the developed instrument may be a valid diagnostic tool to be used by EFL classroom practitioners to understand learners’ metacognition in academic writing. Teachers need such information to foster and support learners’ academic writing through assessing learners’ strengths and weaknesses in strategy use related to academic writing. For example, teachers could optimize learners’ regulative capacity in monitoring and evaluating their writing process. Learners should be supported in reflecting on their strategic behavior. Second, the study provides insight for theoretical understanding of metacognition and academic writing. In particular, the present study sheds light on how different strategies related to metacognitive knowledge and regulation predict learner’s academic writing performance. Gaining insight into the interplay between these strategy types and academic writing performance could contribute towards a better understanding of why some student writers achieve outstanding academic writing performance while others struggle. Finally, self-regulatory writing competence, i.e., the adaptation of skills and strategies to learners’ writing, should be taught for learners to develop metacognitive awareness of what, how, and why certain choices apply for writing (Teng,  2019b ). Learners need to engage in critical thinking for planning, monitoring, and evaluating their own writing processes. Communicating this message to university English learners would be desirable. The present study highlights the benefit of paying attention to metacognitive regulatory skills for academic writing performance.

Below is the link to the electronic supplementary material.

Acknowledgements

This paper is supported by the Project from the Education Department of Hainan Province, Project number: Hnky2020ZD-9

Authors’ contribution

Mark Teng: Coordinated the study. Drafted and revised the manuscript.

Chenghai Qin: Data collection, drafted literature review.

Chuang Wang: Participated in the design of the study and performed the statistical analysis and data interpretation.

All authors proofread and approved the final manuscript.

Data availability

Declarations.

The authors declare that they have no conflict of interest.

Human participants.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Mark Feng Teng, Email: om.ude.mu@gnetkram .

Chenghai Qin, Email: nc.ude.unaniah@ncttocs .

Chuang Wang, Email: om.ude.mu@cgnaw .

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Title: accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning.

Abstract: Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1,000 scientific papers into an ontological knowledge graph. Through an in-depth structural analysis, we have calculated node degrees, identified communities and connectivities, and evaluated clustering coefficients and betweenness centrality of pivotal nodes, uncovering fascinating knowledge architectures. The graph has an inherently scale-free nature, is highly connected, and can be used for graph reasoning by taking advantage of transitive and isomorphic properties that reveal unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, propose never-before-seen material designs, and predict material behaviors. We compute deep node embeddings for combinatorial node similarity ranking for use in a path sampling strategy links dissimilar concepts that have previously not been related. One comparison revealed structural parallels between biological materials and Beethoven's 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. In another example, the algorithm proposed a hierarchical mycelium-based composite based on integrating path sampling with principles extracted from Kandinsky's 'Composition VII' painting. The resulting material integrates an innovative set of concepts that include a balance of chaos/order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a context-dependent heterarchical interplay of constituents. Graph-based generative AI achieves a far higher degree of novelty, explorative capacity, and technical detail, than conventional approaches and establishes a widely useful framework for innovation by revealing hidden connections.

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