Effects of virtual learning environments: A scoping review of literature

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  • Published: 06 October 2021
  • Volume 27 , pages 3683–3722, ( 2022 )

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  • Laura Caprara 1 &
  • Cataldo Caprara 1  

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The purpose of this scoping review is to isolate and investigate the existing data and research that identifies if the synchronous face-to-face visual presence of a teacher in a virtual learning environment (VLE) is a significant factor in a student’s ability to maintain good mental health. While the present research on this explicit interaction among VLE implementation and student mental health is limited, the material suggests a framework for strong utilization of VLEs. Overall, our research has shown that authentic, high quality VLEs are ones that have as their primary focus the communication between students and their teachers and between students and their peers. This communication is best generated through synchronous connections where there exists the ability to convey the student’s immediate needs in real-time. Our research results and discussion will outline how a team approach that brings together teachers, students, administration, counsellors, mental health support staff, instructional designers, and ICT specialists is necessary to create a genuinely enriching VLE where both learning and social-emotional needs can be met. The authors present a case for further study in order to reveal the nature of the interaction among VLEs and student mental health.

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1 Introduction

Increasingly educators are turning to digital online tools and resources to supplement and enhance their teaching in the classroom. Independent of the move to online learning as a result of the COVID-19 pandemic, there is a clear upward trend to the prevalence and variety of online learning opportunities available to students of all ages. In their meta-analysis, Means et al. ( 2009 ) show that the rise of online learning is for a variety of reasons:

Online learning has become popular because of its potential for providing more flexible access to content and instruction at any time, from any place. Frequently, the focus entails (a) increasing the availability of learning experiences for learners who cannot or choose not to attend traditional face-to-face offerings, (b) assembling and disseminating instructional content more cost efficiently, or (c) enabling instructors to handle more students while maintaining learning outcome quality that is equivalent to that of comparable face-to-face instruction. (p. 22)

In Ontario, online learning can be accessed through traditional school boards using eLearning Ontario and Brightspace or through available online private schools such as These virtual learning environments (VLEs), online spaces that facilitate the delivery of curriculum content, assessment, and evaluation activities – deliver said curriculum in an asynchronous manner, that is “learning that is not delivered in real time. Asynchronous learning may involve students watching pre-recorded video lessons, completing assigned tasks, or contributing to online discussion boards” (Ontario Ministry of Education, 2020 , PPM 164). This is different than synchronous learning which is defined as “Learning that happens in real time. Synchronous learning involves using text, video, or voice communication in a way that enables educators…to instruct and connect with students in real time” (Ontario Ministry of Education, 2020 , PPM 164). It is believed that “synchronous learning supports the well-being and academic achievement of all students…by providing educators and students with an interactive and engaging way to learn. It helps teachers provide immediate feedback to students and enables students to interact with one another” (Ontario Ministry of Education, 2020 , PPM 164). Despite this fulsome definition of synchronous learning, little research-driven policy or infrastructure exists whereby educators can be trained and supported in creating an ideal VLE for their students’ educational and social-emotional needs (Jones, 2015 ; Kent et al., 2018 ). This was certainly the case for all Ontario educators when, in March 2020, schools were closed due to the SARS-COVID-19 global pandemic and learning shifted to online-only instruction as contextualised in the following section.

1.1 Background

On Thursday, March 12, 2020 at 4:00p.m. we tuned into the local news channel to hear that the Ontario Provincial Government was putting the province into lockdown to avert the upcoming threat of the novel Coronavirus, SARS-COVID-19. In this same news conference, we learned that Friday, March 13, 2020 would be our last day in schools; schools would be closed for two weeks after the March Break which began the following Monday. We spent the day photocopying, distributing, and explaining work packages for our students while ourselves wondering how we would negotiate the transition to distance learning and the new expectation to work – teach – from home. After these two weeks, a continuation of the lockdown was announced and Ontario educators were tasked with connecting with students and families procedure to gauge student’s communications technology readiness, to monitor their social-emotional needs arising from the lockdown and to explain that basic emergency learning was now in place.

At the same time, educators were told to create virtual learning environments (VLEs) to deliver this emergency instruction. Hours of instruction guidelines were provided by the Ontario Ministry of Education (April 2020): for JK-Grade 6, no more than six hours per week; for Grades 7–8, no more than eleven hours per week and for Grades 9–12, no more than three hours per course per week. During this time, normal attendance procedures were paused and replaced with anecdotal teacher monitoring and the mode and delivery of teaching was not standardized via a specific virtual platform. Guidance from Ontario teacher unions was to avoid videoconferencing and webcasting (OECTA, 2020 ) to maintain the privacy of both teacher and student.

The unprecedented scope and unforeseen turmoil caused by the COVID-19 crisis resulted in a delayed and mixed response for how educators could meet the daily social-emotional needs of their students who were now being taught in ways that were not conducive to learning such as having no face-to-face communication. Part of this mixed response included the ministry directive that students’ marks and grades would be frozen to the date prior to the initial closure (Miller, 2020 ), though opportunities for improvement would be provided. While the intentions of policymakers were rooted in compassion and wishing not to amplify any feelings of stress, anxiety, or hardship already being caused by the pandemic, it did not serve to inspire students to be continually engaged in their educational community or to feel wholly supported by said community. Overwhelmingly, students in both of our schools stopped engaging in course content; knowing that their grades could not change truly impacted their motivation, diligence, and general commitment to learning. When this disconnect occurred, teachers were advised to continue to call the student and encourage them to login to their VLEs, but this consistent check-in by four teachers at least once a week to each family began to feel at best like nagging and, at worst, an intrusion. This disconnect had another negative effect; now that students were not explicitly required to complete work for school, businesses were free to demand that they work throughout the day. Many students shared that they had taken on full-time hours at their part-time jobs. Had the student–teacher-classroom relationship continued throughout the closure somehow through routine, synchronous, live-video or live-voice communication, perhaps students and families would have felt a social and emotional pull to preserve their work habits and would have benefitted from helping to maintain the class community.

Essentially, our students went from daily, face-to-face interaction that built relationships and promoted positive social-emotional skills to the bare minimum interaction of a posting or an email a few times a week. Compounding this growing detachment was the social isolation that being in lockdown necessitated both from friends and family members who were at work. Students were at home, cut off from teachers, school, and friends, alone with the computer screens.

When schools reopened toward the end of September 2020, protocols and safety standards had been improved and implemented in ways that would encourage some families to opt for in-person learning while for others, the risk was still too great. For these families, school boards in Ontario created whole virtual schools or adopted a form of hybrid learning that would accommodate student from Junior Kindergarten through to Grade 12.

Our personal experience of in-person learning after the initial lockdown was atypical: though there was slight excitement the first day, this waned quickly and by the end of the week students were behaving very differently than they had been in March. Classes were silent; students were not speaking to each other, and group discussion or student–teacher conferences were very difficult to craft and nurture. In cases where students were priorly familiar with one another and the teacher, as was the case in one author’s classroom, students remained quiet, distanced, and generally withdrawn. These personal observations were similarly attested by several colleagues. For one author who had been assigned to the new virtual school, similar observations were made. Here, students from all over the city were being placed in VLEs together, disconnected from their home schools. Every attempt was made to create positive community bonds but still, save a handful of eager students, online classrooms were quiet both auditorily and in the chat box.

Unfortunately, COVID-19 continued to spread rapidly through the province and by the winter of 2021, the threat of the new, more infectious variants of COVID-19 forced the hand of the Ontario Ministry of Education to close schools once again for the remainder of the school year. The most marked difference in this lockdown is the continuation of the collection of attendance – students are at least driven to login to their VLEs to have their attendance recorded to avoid the consequences of truancy. Many students have shared with the authors that this second lockdown had evaporated their hopes of any social-emotional normalcy for their educational experience. They have attested that they are tired of feeling alone, overly challenged at the thought of having to continue their learning experiences solely through their computer screen.

2 The path to inquiry

In the summer of 2020, The Hospital for Sick Children ( 2019 ) released a report that detailed how online learning and increased screen time could result in an increase of negative mental health effects. In addition, the prevailing advice of Public Health Ontario (2015) for the total number of hours of screen-time children and youth should be engaged in had not moved from the recommended “no more than 2 hours per day”. Instead of heeding the warning and advice of these public institutions, the Ontario Ministry of Education Policy/Program Memorandum No. 164 ( 2020 ) defining synchronous learning as “using text, video, or voice communication” in real time, set in place the following expectations for daily synchronous learning: 180 min for Kindergarten and 225 min for Grades 1–12.

Our desire to study the mental health effects of online learning takes its root in the experience of students and their guardians who have directly voiced difficulty with engagement in online schooling. Specifically, our students shared that their experiences in courses facilitated by teachers who did not utilize real-time video for the delivery of their synchronous lessons were difficult, hard to manage, and at times felt alienating.

Certainly, the few noted anecdotes above cannot constitute a direct cause for this review. Nonetheless, it is the view of the authors that such comments appearing from within the unique context of the mandated and sweeping switch to online learning warrant an investigation of what research might exist to establish acceptable standards of VLE implementation. In the case of the COVID-19 context, it must be understood that the changes to educational architectures were motivated by the physical requirement of stopping the spread of disease rather than what research has shown to be the best mode of education for the learner and their mental health needs. Further, even within the confines of mass VLE implementation, deeming the use of real-time video as strictly optional instead of prescribing a definitive modality for engaging in best practice displays a gap in an understanding of VLE significance. Further still, as online learning continues to be a the only viable option for many families, educators in Ontario must ensure that the creation of a VLE and the curriculum delivered through it is guided by the Ontario College of Teachers (OCT) Ethical Standards of Care , where it is understood that Ontario Teachers ought to avoid practices that may not support the welfare of all students placed in their care.

The Ontario College of Teachers ( 2020 ) defines the ethical standard of care as follows: “Members express their commitment to students' well-being and learning through positive influence, professional judgment and empathy in practice.” Further, the Ontario Education Act, 1990 Sect. 264(1c) clarifies that part of a teacher’s duty is to impress the “highest regard for…humanity…and all other virtues” to their students. In addition to the duty of a Principal to provide “assiduous attention to the health and comfort of the pupils” (Ontario Education Act, 1990 , Sect. 265(1j)) they must do so in such a way that they set the standard for the teacher. Without daily, live interaction with students, how is it possible to wholly meet these expectations?

Interestingly, the concept of all persons having to remain at home during the new COVID-19 online education paradigm seemed to create a vague understanding of whether it was even possible for the professional liability of care to take place. In consideration of the area of attendance, it must be understood that the traditional taking of attendance has a layered purpose: to ensure the “safe arrival” of students, to give proof of student attendance for funding allocation, and to link a student to the legal liability of a teacher as well as to allow teachers to exercise interventions for students who are truant. In the first stage of lockdown, teachers were asked to not take attendance and so were unable to ensure regular student contact. In this way, it became much more difficult to distinguish which students were not able to keep up with their studies and who required appropriate interventions for their learning, development, and wellness.

Tronick et al. ( 1978 ) showed that when the face-to-face interaction between infants and their mothers becomes distorted in such a way whereby the mother is unresponsive and still, “infants reacted with intense wariness and eventual withdrawal” (p. 1). They concluded that it was vital to have “interactional reciprocity” (Tronick et al., 1978 , p. 1) to learn how to regulate their own emotional reactions. In their meta-analysis of this Still-Face Paradigm, Mesman et al. ( 2009 ) found that not only had the paradigm been consistent through multiple studies in infants, but its negative effects could also be found in both youth and adults. Experiments with adults resulted in “quite severe disrupting effects on social interactions, making people angry, confused, or upset…[where] [t]he perceived necessity for following the ground rules of social interactions is likely to stem from the evolutionary roots of human social life” (Mesman et al., 2009 , p. 156). These results would suggest that part of the educator’s duty of care to a student is to maintain this quintessentially human behaviour of consistent face-to-face interaction. Without this fundamental interaction, it seems that humans are unable to properly regulate their emotional expression. Certainly social-emotional learning and the formation and maintenance of positive relationships with others is a core part of the care mandate of teachers.

3 Purpose of scoping review

While it could be surmised that the guiding education authorities’ combined lack of clarity in declaring best practice directives and providing systems of care contributed to negative student experiences, the notion of how these organizational failures might have impacted student mental health must be drawn into focus. Indeed, the most notable shift in education has been toward the use of the online classroom and it is foreseeable that the continuation of this paradigm will be unavoidable. However, with the ambiguous messaging that surrounds that which defines quality implementation of a VLE, there lies a discernible gap in gauging the importance of teachers using live video in the delivery of synchronous instruction and if significant mental health implications are present in the decision to do so.

The purpose of our scoping review is to isolate and investigate the existing data and research that identifies if the real-time visual presence of a teacher in a VLE is a significant factor in a student’s ability to maintain good mental health. Such research might reconcile the void of definitive directives educational authorities have offered concerning teachers’ utilization of real-time video in a VLE and, more importantly, provide students with a higher standard of care while enduring the context of increased vulnerability the pandemic has introduced.

In short, this scoping review aims to answer two questions:

Does the synchronous face-to-face and interactive presence of a teacher in a VLE contribute positively to student learning and mental health and well-being?

What are the characteristics of a VLE that meets the social-emotional care requirements and needs of the student?

4 Method and parameters

This scoping review works to capture the current “size and location of the literature” (Anderson et al., 2008 , p.7) to better define the state of understanding of our research questions. Further, it is our hope that this scoping review might work to ascertain common threads among the research and note any gaps to inspire further inquiry in the topic. All of the material will be charted so it can be viewed in tandem for the above purposes.

4.1 Boundaries of the review

Our search was limited to English-only peer-reviewed research literature that investigated the efficacy of online teaching and VLEs for educating the whole student. More specifically, we completed a search of all research articles published between 1 January 2010 and 31 January 2021 (an 11-year period). The rationale for focusing on this span of just over a decade was to draw our attention to the most recent research evidence related to our question. This date range also reflects the time span in which we had secured permanent work as educators; tracing the development and evolution of online learning since that point is of great personal interest.

Our search utilized the following terms: virtual learning environment (in all text) OR online instruction (in all text) AND mental health (in all text). These search terms were selected as they provided the widest catchment for the screening process required for this scoping review; in particular, the search term virtual learning environment was selected to identify research that was specific to an interactive context of online learning, as opposed to, for instance, online learning structured as a correspondence course. Databases that we utilized include ProQuest, ProQuest/ERIC, APA PsychINFO, and SAGE Journals Online because together they encompass a comprehensive and diverse catalogue of education-related literature as well as research regarding the psychological aspects of education. These database searches occurred between January 31, 2021, and February 7, 2021.

4.1.1 Special considerations of the topic search: defining a student

Ideally, the search terms could have been narrowed to refer to students specifically in a high school setting, using common platforms such as Google Classroom, but these terms did not yield any results. In an effort to compile the most useful sources for our research, a broadening rather than narrowing approach to our search terms was employed as it was discovered that limited resources existed in the field we were exploring. Considering this, the limits we set required special considerations of demarcation. For instance, many sources pertaining to the student mental health in a VLE context dealt exclusively with nursing students (e.g., Shea & Rovera, 2021 ). These studies offered excellent data sets but were omitted because the data could not be understood as congruous with a universalized definition of what constitutes a student. That is, the specialized nature of these studies involved students who had the responsibility of dealing with patient care, using VLEs to interact with their patients. In this way, the nursing paradigm muddied the demarcation between the VLE experience of a student and that of an authority.

4.2 Inclusion criteria

In initially assessing and screening the results of the database searches, we ventured to remove sources that did not pertain to the contexts of VLEs and the student’s experience of those VLEs. Here, a primary list of 268 articles was redacted to a sum of 63 articles via an evaluation on the content of the title and abstract of each article. The inclusion of the remaining 63 articles was based on their titles and abstracts having expressed: (1) the use of an online modality that would implicitly involve the visual presence of an educator, and (2) the interest in the contexts of the learners’ experiences being a result of the instruction provided. Sources that were concerned with the experience of post-secondary students were only included if they involved Year 1 students as part of their purview. This limit was imposed on the research as we perceived this domain of data as relevant to the prospect of constructing an information-set surrounding the transitional stage of Grade 12 into post-secondary. Further, this inclusion might allow for a greater basis of insight into the still emerging reality of widespread online education. Notable exclusions from the research that might have helped develop such a picture were many articles that focused on the VLE experiences of graduate and post-graduate level students (e.g., Chugani & Houtrow, 2020 ; Gardner, 2020 ; Shawaqfeh et al., 2020 ) and articles concerned with the mental health of educators who utilized VLEs (e.g., Watermeyer et al., 2021 ; Rowe et al., 2020 ; Ault et al., 2020 ; Alkarani & Thobaity, 2020 ; Schlesselman, 2020 ). The subsequent 63 articles were read in their entirety to determine whether their content would be appropriate for this scoping review. This stage of assessment resulted in the inclusion of 38 articles.

4.3 Manual inclusions

Additionally, a reference check of all 38 articles yielded the inclusion of a meta-analysis authored by Cavanaugh et al. ( 2004 ) we deemed relevant insofar as it provided an overview of the effects of online learning in a strictly academic sense allowing a possible comparison between achievement and positive mental health. A manual search conducted prior to the exercise of prescribed database search-terms warranted the inclusion of one other meta-analysis authored by Mesman et al. ( 2009 ) which is noted in the introduction section of this scoping review. The specific data and information presented in the work of Mesman et al. ( 2009 ) serves as a correlational tool in observing the trends apparent in the included articles. As a final article addition, a second manual journal search was conducted where two studies were found to include data that encompassed student perceptions of their experiences with VLEs, thus, resulting in a total sum of 42 articles to be included in this scoping review. The total review process is noted visually in Fig.  1 .

figure 1

Review process and results

5 Summary of research findings

The purpose of this scoping review is to isolate and investigate the existing data and research that identifies if the real-time visual presence of a teacher in a virtual learning environment (VLE) is a significant factor in a student’s ability to maintain good mental health. Overall, our research has shown that authentic, high quality VLEs are ones that have as their primary focus the communication between students and their teachers and between students and their peers. This communication is best generated through synchronous connections where there exists the ability to convey the student’s immediate needs in real-time. The demands placed upon an individual educator to facilitate an online education for students, inclusive of the creation and maintenance of an effectual and engaging VLE often serving as a proxy to a face-to-face (F2F) base-school, “require[s] extensive time commitments” (Wingo et al., 2016 , p. 437) which are far beyond the standard workload formulas calculated by many institutions (Wingo et al., 2016 ). Indeed, the complexity of this task while also providing sufficient social-emotional services for students and families is not fully understood in terms of student and teacher equity. Our research results and discussion will outline how a team approach that brings together teachers, students, administration, counsellors, mental health support staff, instructional designers, and ICT specialists is necessary to create a genuinely enriching VLE where both learning and social-emotional needs can be met.

Summaries of the 42 articles pertaining to this research are provided in Table 1 . In addition to information related to authors, years of publication, countries (i.e., where the research was conducted), participant profiles, intervention programs and timelines (where available) and data sources, we have also summarised the researchers’ aims, research designs, findings, and conclusions.

Our research provided studies that had been conducted from nearly every continent of Earth, save South America and Antarctica. Notably, many of the articles were completed by researchers in the United States ( n  = 16). Fifteen were completed by researchers in Europe (Belgium, Cyprus, Georgia, Germany, Greece, Poland, Portugal, Slovenia, The Netherlands, Turkey, Ukraine, and the United Kingdom), and six were completed by researchers in Asia (India, South Korea, Taiwan, and Vietnam). The remaining research was conducted in Australia, Canada, Jordan, and Kenya. The research designs employed by the various researchers in our summary are quite varied and sometimes particularly nuanced and range from case study to meta-analysis. However, a preference for experimental, quasi-experimental, qualitative analysis, and mixed-method correlational analysis did emerge. Overall, few articles had a singular focus, which can be attributed to the expansive field of VLEs and their many intricate pieces. Some central concepts were highlighted, however, in the research: fifteen observed the effectiveness, benefits or challenges of some aspect of VLE (i.e., using virtual reality simulations or synchronous video); ten investigated student readiness, either on a social-emotional basis, technology know-how, or academic; six focused on the mental health of the learners in VLEs; six gathered data on the experiences and perceptions of either students, students’ families, or faculty, and four researched the current infrastructure and available policies for online learning. The participant profiles of our research were also quite varied, identifying elementary and secondary school-aged children, both undergraduate and graduate students, school faculty, and student family members, sometimes all within the same study.

Our research results have been organized in Table 1 below. It has been constructed in such a manner whereby an informational narrative that reflects the essential themes found within the research can be revealed.

6 Results and discussion of themes

The articles included in Table 1 represent the most current and relevant research in considering the embedded inquiry of this scoping review which involves uncovering the nature, implications, and best iterations of practice within VLE contexts. In our reading and review of the data therein, the themes of insufficient data surrounding VLEs, VLE benefits, the challenge of VLE readiness, and that which constitutes the ideal VLE emerged as pivotal. The objective of this section is to elucidate these themes, thereby, providing a modest basis for recommendations regarding VLE implementations and, perhaps, a view to offer directionality for future research.

6.1 Insufficient data

A key note thread found within many of articles was the self-admission of insufficient data. This theme of insufficient data is expressed in varying capacities that range from claims of there being a limited or even non-existent body of research, to more systemic causes for the insufficiencies. While the lack of data is often presented as a cautionary device for the demarcation of limits to implementation outside the context of the studies and provide exhortation for further research to be conducted, the admissions of insufficient data also point to the novel nature of the area of inquiry in question. Kumar and Owston (2015) begin their study on e-learning accessibility by stating that their field of inquiry had “not been explored, nor have methods to generate data” (p. 264) expressing that there is “a dearth of studies'' (p. 268) in the literature, and concluding that “[c]ontinued work in the area of developing methods to evaluate e-learning accessibility is thus urgently needed” (p. 280). Archambault et al. ( 2013 ) also identified their research scope of basic virtual school policies as being novel in nature, having no representation in the existing literature. Many researchers make note of the existing data as being too insufficient to draw more universal conclusions (Barbour & LaBronte, 2019 ; Cavanaugh et al., 2004 ; Engelbertink et al., 2020 ; Gillis & Krull, 2020 ; Ho et al., 2014; Jena, 2016 ; Zhu & van Winkel, 2016 ). In addition to this paucity of research, the attrition of study participants is noted as being a barrier to gathering full data sets (Manthey et al., 2016 ).

Some systemic issues which led to shortages in the available data are noted in Johnston et al. ( 2014 ) where school districts are slow to institute policy. Cavanaugh et al. ( 2004 ) mentioned a similar dynamic in considering that common goals are needed in policy making to identify the effectiveness of an intervention and policy makers and evaluators are exhorted to work together in partnership to ameliorate this. A further systemic barrier to data production that is noted is the problem of implementation of programming without conducting research (Cavanaugh et al., 2004 ).

6.2 Benefits of VLEs

In response to our first research question regarding the benefits of a wholly synchronous VLE experience, the research is generally favourable toward academic achievement with some degree of attestation to its social-emotional benefits. The benefits to VLEs and their implementation are assumed among most of our research in how they can be potential vehicles delivering some form of meaningful intervention or program within a given context. Further, some of the articles underline fundamental goods that can be uniquely exploited via VLEs. Driscoll et al. ( 2012 ) cites VLEs as an opportunity to better promote a constructivist framework for learning in saying that it inherently “creates a structural impetus for this style of learning that is not automatically present in F2F classrooms” (p. 314). Cavanaugh et al. ( 2004 ) provides multiple examples of how the institutional advantages of virtual schools “represent the best hope for bringing high school reform quickly to large numbers of students” (p. 22). Building upon the pervasive benefits to VLEs as a concept, Roblek et al. ( 2019 ) frame VLE dynamics as an essential component of human advancement where “social relations will be formed through the building of collective intelligence” (p. 96). Similarly, VLEs and their relation to ICT literacy as a global objective is observed throughout the research (Blayone et al., 2018 ; Cavanaugh et al., 2004 ; Crea & Sparnon, 2017 ; Davies, 2014 ; Gibson & Smith, 2018 ; Huang et al., 2011 ; Hursen, 2019 ; Jena, 2016 ; Mallya et al., 2019 ).

The strengths of specifically synchronous VLEs emerge in the research with highlighting synchronous learning as an essential component to student engagement with technology, peers, and educators. Concerning technology fluency, even in a blended learning context, synchronous VLEs offered a unique opportunity to implement technology in a meaningful way (Ho et al., 2016 ). Using a device in a synchronous context meant that students felt more engaged with material, subsequently feeling more confident with presenting work using technology, and students enjoyed being able to revisit an interactive lesson digitally after the synchronous session was over (Davies, 2014 ; Driscoll et al., 2012 ; Kumar & Owston, 2016 ). In terms of supporting engagement among classmates, synchronous learning was seen to offer increased avenues for peer-to-peer learning while allowing for teacher involvement throughout, thus increasing effectiveness (Crea & Sparnon, 2017 ; Johnston et al, 2014 ). Synchronous VLEs that include video also offer opportunities to be present to a class setting in a way that attends to learning retention, academic engagement, resiliency, and self-regulation (Archambault et al., 2013 ; Driscoll et al., 2012 ). When VLEs employ best-possible real-time communication, education processes can be more active, constructive, cooperative, and more attentive to a student’s meta-cognitive abilities than the traditional classroom (Cavanaugh et al., 2004 ). These latter points concerning real-time visual instruction potentially align with a foundational dynamic noted by Mesman et al. ( 2009 ) where it is stated that an “infant needs an external regulator to achieve optimal arousal levels and will show disorganization of emotion and behaviour when the regulator is absent or non-optimal” (p.122). Such a relationship becomes apparent in the work of.

Baker et al. ( 2019 ) which observed quiz results decrease among those students whose instructor withdrew communication and synchronous availability after originally being quite attendant to their needs and in the work of Engelbertink et al. ( 2020 ) where student motivation dropped significantly when the teacher no longer demonstrated an interest in the student’s homework. Throughout the research, it is evident that student engagement and achievement is well-supported in a synchronous VLE.

6.3 The barrier to a VLE: the challenge of readiness

Across all our research, it became clear that one of the primary factors curtailing the effectiveness of any VLE or LMS was the various states of readiness of the institution, the teacher, and the student.

At an institutional level it can be said that most schools are not equipped to create VLEs where students can thrive, even those schools that are virtual by design. The infrastructure required to create a holistic learning experience for the student, and one that embodies fair and equitable working conditions for the online educator, requires a considerable investiture of human resources and technological tools (Archambault et al., 2013 ; Cairns et al., 2020 ; Jones, 2015 ). Many LMSs that institutions use for online learning are bulky and inefficient (Gillis & Krull, 2020 ; Jones, 2015 ; Kumar & Owston, 2016 ; Lee et al., 2016 ) which can lead to their being used as places where information is simply disseminated, rather than genuine VLEs where the design and curriculum content can come together to connect students with each other for interaction and collaboration (Jones, 2015 ; Stone, 2019 ). Elementary schools, for instance, can be said to provide many opportunities for families to increase their informal social capital and high schools, colleges and universities often provide a student with guidance and counseling services not easily accessible elsewhere. In moving to online learning, these institutions must not forget their “organizational brokerage” (Domina et al., 2021 , p. 4) in facilitating and maintaining these social connections lest their students suffer in isolation (Crea & Sparnon, 2017 ). Ultimately, the VLE experience begins with the institution; if there is no commitment to ensuring the use of a high-quality LMS and no focus on securing and maintaining the human resource social supports that students and families have come to rely on the school to provide, then the mental health and academic achievement of its students can deteriorate (Cairns et al., 2020 ; Cavanaugh et al., 2004 ; Domina et al., 2021 ; Gillis & Krull, 2020 ; Jones, 2015 ; Lee & Oh, 2017 ; Merlin-Knoblich et al., 2019 ; Rogowska et al., 2020 ; Stone, 2019 ; Xavier et al., 2020 ; Zhu & van Winkel, 2016 ).

As Blayone et al. ( 2018 ) points out, vital to the VLE experience is “high quality activity design, strong environmental and motivational supports, and competent online facilitators” (p.15). Teacher readiness for both the technological scope of VLEs and for the new expectations that they are the sole social-emotional support for students and families (at the very least a proxy to such supports) is generally low. Training is essential for educators who are navigating new technologies and creating resources that provide meaningful opportunities for knowledge construction, reflection, and practice (Davies, 2014 ; Gibson & Smith, 2018 ). Teachers must also be taught how to “adjust and find their own rhythm, providing sufficient presence while avoiding feeling perpetually ‘on call’” (Jones, 2015 , p. 227). Teachers lacked access to suitable training and felt ill-prepared to offer and provide to students with special needs or disabilities the appropriate accommodations within the VLE (Kent et al., 2018 ). Substantial professional development is needed to ensure that teachers know how to provide social opportunities in the VLE that encourages group work, formal and informal interactions, and peer-to-peer cooperative learning (Cavanaugh et al., 2004 ; Johnston et al., 2014 ; Zhu & Van Winkel, 2016 ). Cultivating this social-emotional component is an essential task of the online educator; when a student can trust their teacher and their classmates, their self-efficacy and motivation increases and generally so does their performance and progress (Johnston et al., 2014 ). To accomplish this, institutions must increase their efforts in training and supporting their faculty to be ready for online instruction (Crea & Sparnon, 2017 ).

Jena ( 2016 ) defines student learning readiness as “the body of skills needed by learners to learn” (p. 950). This body of skills and aptitudes includes, but is not limited to, motivation, self-regulation, perceived usefulness, confidence with using various technology, attitude, self-efficacy, computational abilities, communication skills, and research and critical thinking competence (Baker et al., 2019 ; Blayone et al., 2018 ; Du et al., 2019 ; Hursen, 2019 ; Johnston et al., 2014 ; Jones, 2015 ; Mallya et al., 2019 ). Beyond these attributes of learning readiness is also a strong necessity for a certain level of social-emotional maturity, most especially if the online learning was a result of the COVID-19 pandemic or of illness (i.e., not a free choice). Soft qualities such as resilience, flexibility, and positivity (Lee & Oh, 2017 ) made it more possible for students to survive the transition from the routine and collaboration of a physical classroom to the more solitary and independent learning space of the VLE (Crea & Sparnon, 2017 ; Gibson & Smith, 2018 ; Jena, 2016 ). In addition to these crucial factors, is the technology-readiness of students. Students may not have access to their own personal device to do their schoolwork, and if they do, there is no guarantee that it is a device equipped with the sufficient technological specifications to handle the resource heavy online tools or that the student has access to high-speed internet to allow full and equal participation in the lesson and VLE (Domina et al., 2021 ; Gillis & Krull, 2020 ; Hursen, 2019 ). It cannot be assumed that because students use technology at very high rates for personal relationships and entertainment that they can directly transfer those skills to the sophisticated and critical digital literacy necessary and conducive to learning in a VLE (Blayone et al., 2018 ; Roblek et al., 2019 ). Indeed, the various online tools that are familiar to institutions and educators are rarely in the purview of students, though when the need arises, students do want to be taught how to use the many programs and LMSs available to them effectively (Stone, 2019 ) and thus system readiness, student readiness, student inclusion, student achievement and teacher readiness are inseparable (Huang et al., 2011 ; Kumar & Owston, 2016 ; Pryjmachuk et al., 2012 ; Yilmaz, 2019 ).

6.4 The ideal VLE

Among the reviewed articles, the answer to our second research question concerning the criteria of an ideal VLE emerged. VLEs which supported students both academically and emotionally and whereby online educators were engaged and motivated were highly organized and inventive, and if given that no barriers of readiness existed, could be implemented in every school system willing to pivot to this necessary focus. Firstly, policies and procedures that focus on the progress and social-emotional needs of the student must be in place (Archambault et al., 2013 ). This can only be achieved if a full set of human resources such as guidance teachers, attendance officers, counsellors and special education resource teachers are available both on a central campus and online (Johnston et al., 2014 ) offering “inclusion, communication, connection with others and proactive institutional support” (Stone, 2019 , p. 7) by way of a school-home mentorship model (Barbour & LaBonte, 2019 ). In this way, the student’s isolation is lessened and, united with the educational team, the VLE teacher can focus on lending their subject and pedagogical expertise to their students (Driscoll et al., 2012 ; Du et al., 2019 ; Engelbertink et al., 2020 ; Wingo et al., 2016 ; Zhu & van Winkel, 2016 ). Secondly, the VLE must be easy to use, accessible, flexible, and innovative. Institutions must select uncomplicated LMSs for teachers to use to deliver their program. The expectations of use must also be communicated to all faculty to ensure a seamless experience for students (Jones, 2015 ). As well, in either a synchronous VLE or BLM, having easy access to recorded lessons is crucial, especially for students with disabilities or who are still learning the language (Davies, 2014 ; Dommett et al., 2019 ; Kumar & Owston, 2016 ). Investment in innovative tools and technologies is necessary to keep the VLE from becoming stagnant for students and, depending on the technology, can promote healthy, rich, and meaningful student interactions (Du et al., 2019 ). There is promising research in the use of tools such as AR, VR, 3DVR and 3DVE to create experiences and spaces that allow students to attend to one another virtually. These tools help to cultivate positive relationships, academic and personal confidence, and good mental health (Huang et al., 2019 ; Lan et al., 2018 ; Papanastasiou et al., 2019 ; Stone, 2019 ). Thirdly, there must be, at best, a live-video synchronous component to the VLE, or at minimum, the availability of synchronous office-hours (Stone, 2019 ; Wingo et al., 2016 ; Zeren, 2015 ; Zhu & van Winkel, 2016 ). When students and teachers were engaged face-to-face, body language and tone could be better understood and relationship markers such as trust and care could be better perceived (Driscoll et al., 2012 ; Johnston et al., 2014 ; Wingo et al., 2016 ). Finally, the VLE must engage students in becoming digital citizens together. VLEs that provide opportunities for students to engage formally and informally enable students to increase their academic self-efficacy, increase their learning outcomes, and mitigate any mental health issues that may result from the perceived isolation of online learning (Driscoll et al., 2012 ; Du et al., 2019 ; Engelbertink et al., 2020 ; Johnston et al., 2014 ; Stone, 2019 ; Yilmaz, 2019 ; Zhu & van Winkel, 2016 ).

6.5 Discussion of gaps and limitations in the research and suggestions for further inquiry

The attempt to study any observable intersection of VLE implementation and student mental health presents unique logistical and philosophical queries that remain unquelled. Such wonderings involve the state of how participant numbers are determined, the founding modalities in which self-reported qualitative data is obtained, the rationale, or lack thereof, of why specific LMS platforms were used in the existing studies, and the generally perceived evolving nature of VLEs. Taken together, the various streams of inadequate information fret deeply and, perhaps, create quite significant gaps. In the following discussion of these gaps, we will humbly aim to make moderate suggestions for further inquiry that could enrich the current available research.

Concerning the limitations in obtaining meaningful participation, a key area that remained challenging among the research was ensuring that participant profiles were not assembled out of simply convenient contexts of implementation. Indeed, quality research is exhorted to communicate, as narrowly as possible, the contexts in which they are situated. However, our search yielded a number of studies that were isolated case studies (e.g., Archambault et al., 2013 ; Johnston et al., 2014 ; Jones, 2015 ; Kumar & Owston, 2016 ) or were relegated to being singularly quasi-experimental (e.g., Blayone et al., 2018 ; Davies, 2018; Driscoll et al., 2012 ; Ho et al., 2016 ; Huang et al., 2011 ; Lee et al., 2016 ) in nature due to the fact that their implementation was imposed upon pre-existing participant groupings – those who happened to be enrolled in the class that was chosen for intervention. In extension to this, adequate control conditions were not always apparent, especially those which considered many factors that were changed in the experience of intervention groups. That is all to say that the interventions themselves were multifaceted, and one could surmise a possible inability to distinguish which key facet or combination was pivotal in the intervention. This issue may be considered a specific function of the sheer complexity of studying VLE implementations themselves. It is further compounded in the noting of pre-existing intervention groupings as it is perhaps the result of simple pragmatism in observing VLE implementations where they are available to be observed. This point recognizes that VLEs require specific access to resources that may be limited, making widespread and universally approachable studies a challenge. Here, it is possible that an underlying dynamic exists in the research where actioning any opportunity for study, however limited, is better than conducting no study at all. In our view, further inquiry into VLE efficacy and its relation to the mental health of students, should endeavour to include randomized trials, whereby there is no observed previous relationship between the intervention group and the researcher.

Another limitation to this scoping review related to participant selection is the scale and size of many of the studies. Several studies combined the type of participant, blending the experiences of students, faculty, and education support staff, thus limiting a focus on the unique perspective of the student as the end-user (e.g., Crea & Sparnon, 2017 ; Dommett et al., 2019 ; Engelbertink et al., 2020 ; Johnston et al., 2014 ; Stone, 2019 ; Wingo et al., 2016 ). Additionally, some studies that reported findings concerning students directly were of an extremely small student sample size of thirty or less (e.g., Cairns et al., 2020 ; Dommett et al., 2019 ; Engebertink et al., 2020 ; Hursen, 2019 ; Johnston et al., 2014 ; Kumar & Owston, 2016 ; Lan et al., 2018 ; Wingo et al., 2016 ; Zeren, 2015 ; Zhu & van Winkel, 2016 ). We note this small sample size in order to frame the perceived usefulness of these studies in the Ontario education context noting that Ontario Regulation 484/20, s. 4(14.1) states that “the average size in a school year of a board's online learning classes shall not exceed 30”. It is our view that findings of studies with a less than thirty sample size should be interpreted cautiously, as the dynamics and pressures conspicuous in an average sized class cannot be accurately measured. For further inquiry, we would suggest research that included groups of whole divisions across multiple school boards allowing for parallel interpretation and consistency.

A further limitation of this scoping review is the lack of consistency in LMS research. In as many facets as teachers differ so too do the online VLE tools that may be utilized to deliver programming and the effects of each LMS’s nuances can be difficult to account for and isolate as non-contributing factors within the studies. Several studies looked specifically at the Blackboard LMS (e.g., Crea & Sparnon, 2017 ; Davies, 2014 ; Du et al., 2019 ; Engebertink et al., 2020 ; Kent et al., 2018 ; Lee et al., 2016 ; Pryjmachuk et al., 2012 ) noting that in most cases its use was pragmatically chosen as it was already in use by the hosting institution. As well, multiple studies looked at either outdated programs, such as Facebook and RSS feeds (e.g., Huang et al., 2011 ; Hursen, 2018; Yilmaz, 2019 ) or expensive and new technology, such as 3DVR and iPads (e.g., Davies, 2014 ; Huang et al., 2019 ; Lan et al., 2018 ), that would be quite financially out of reach for most Ontario school boards to implement in any widespread and equitable fashion. Overall, researchers instead focused on studying only the perceptions of online learning in general or one specific piece of the online learning experience (for instance, the posting of recorded lectures or an asynchronous discussion board section) without giving any precise attention to the LMS used to create the VLE. In fact, it can be seen that the largest gap in the research is ignoring the LMS as a true, unto itself environment. We find this to be crucial to our research focus of determining how the perceived humanity of the VLE affects the mental health of the student; after all, it is the space in which the student will be spending most of their learning hours.

Many of the studies relied upon qualitative data gathered via surveys during the intervention which required participants to self-report on their perceived well-being and mental health. This paradigm of understanding reads as akin to consumer-based research where one who is satisfied with a product is more likely to repeat consumption regardless of whether the product ultimately increases quality of life. Just as the terms pleasure and contentment are not interchangeable, in the context of the research, it remains unclear if a participant’s self-reporting on perceived levels of anxiety is congruent with clinical definitions of the terms. While studies which utilized this form of data collection are free of equivocation by way of maintaining qualifying language such as “perceived level of anxiety” and not simply state “anxiety”, the question of the results being meaningful still remains. A suggestion for further inquiry may entail the implementation of standardized data sources such as the PSS-10 , the CES-D , and the MBI-SS utilized by Lee and Oh ( 2017 ). Caution must be employed when developing questionnaires, interview questions, and surveys that provide opportunities to participants for open-ended self-reporting.

A final point on the limits of VLE research rests in the concern that LMSs may evolve at rates that do not allow for consistent implementation and ample research to be conducted in a timely manner. In an earlier section of this scoping review, we referenced the lack of research as a product of the field being relatively novel in nature. However, for several years now, LMSs have offered functionalities that extend into the realm of real-time collaboration that is inclusive of visual presence with teachers as well as being fully capable of allowing students fluid peer-to-peer real-time engagement. In considering this it is astonishing that our research showed such a vast range in how VLE operated in relation to the available technology. This has led us to wonder if the technology is available, why is it not being utilized and studied in a way that reflects its full capabilities? Aside from the concepts of readiness that have been earlier itemized and discussed, the level of investment that an institution is willing to make of a platform and the rate of making that LMS available with a fully optioned suite, becomes the pivoting element. Further, if a gradual investment model is employed, where not all features of the LMS are available at the onset, each time a new feature is doled out, it becomes a potential point of relearning and creates inefficiencies if there are not ample professional development opportunities for educators.

While determining our final research terms for this scoping review, we had initially searched for studies that were exclusively for the K-12 sphere and unsuspectingly this did not prove to be fruitful. We noted earlier that a peculiar number of studies had been published using nursing students as the study participants, reporting on their role as student and practitioner. We believe that the near exclusivity of undergraduate students in the research limited our ability to present a complete picture of the current state of VLEs for all students. In The Human Face of Mental Health and Mental Illness in Canada , the Government of Canada ( 2006 ) reported that “two-thirds (68.8%) of young adults aged 15–24 years with a mood or anxiety disorder reported that their symptoms had started before the age of 15” (p. 34). Boak et al. ( 2016 ) support these conclusions, showing in their report that “one-third (34%) of students indicate a moderate-to-serious level of psychological distress (symptoms of anxiety and depression)” (p. iv) and “one-in-eight (12%) students had serious thoughts about suicide in the past year” (p. iv), a statistic that has remained consistent over the past fifteen years of reporting. Given these unsettling statistics, we would have thought that the rich and varied K-12 arena would have been a sphere where there would be a surfeit of mental health research related to VLE utilization. Instead, our search results yielded studies that disproportionately represented participant groups outside of our desired range of inquiry; just six included the experiences of K-12 students, and in each of those studies, the students’ caregivers were the primary data source. K-12 students often spend more than half their waking hours in school environments and VLEs; it is notably unclear why most of the studies observed in this scoping review are unilaterally disinterested in exploring an identified area of need for mental health support. We believe it would be prudent to prioritize research on the mental health of K-12 students engaged in VLEs which Domina et al. ( 2021 ) has shown can be isolating, psychologically disruptive, and exhausting experiences.

7 Suggestions for educator practice

Though there is variety in the identified gaps detailed above, our research maintained a consistent thread that related to the criteria of the ideal VLE for both the success of the educator but also the well-being and dignity of the student. From this work, we endeavour to make a few moderate suggestions for online educators:

Where possible and where privacy concerns can be mitigated, conduct lessons and office hours using live videoconferencing. Whether the VLE is a secondary component in a BLM arrangement, or it is the primary mode of program delivery, maintaining a personal face-to-face connection is an essential component to a student’s feelings of connectedness and motivation.

Avoid viewing the VLE as merely a space to bank work packages and collect evaluations. Rather, aim to create a space where both formal and informal interactions can occur, synchronously and asynchronously. Many LMSs have the capability to incorporate a variety of third-party online teaching and learning tools to aid educators in creating a multi-modal experience for students.

Be vulnerable and take advantage of learning opportunities when they become available. It takes an enormous amount of energy and resources to run stimulating programs that speak honestly to curriculum content, allow for individual learning needs, and that are cognizant to the social-emotional well-being of students. The responsibilities and conditions of an online educator are well-primed for strain; be mindful of the added pressure and allow the professional development that is available to inform practice but not make hurried demands of that practice.

8 Conclusion

If educational research involves an ethical component, it would be incumbent on institutions to see that research reflects areas of need within communities. It is our hope that this scoping review might provide modest insight into the current state of research that concerns student mental health in VLE contexts, while casting light on the need for new research initiatives to be undertaken in the K-12 sphere. As it stands, there lies the strong possibility that K-12 students are experiencing VLE implementations that do not actively partake in the qualities of a VLE that soundly offer best practices, working to support the mental health needs of students. To build strong VLE’s for K-12 students, research campaigns ought to offer architectures that are universalized in their implementation and fundamentally repeatable. This requires a commitment beyond that of the researchers involved, but also a willingness of the institutions who serve the participants of such a sweeping study to abide by the research. Without such research, institutions which utilize VLEs can only continue on sometimes arbitrary perceptions of how best to serve student wellness. Persisting in the status quo as such leaves students vulnerable to practices that might institutionally under-serve them and have potential generational implications. Interestingly, one might argue that, without such research, institutions who offer VLEs might garner the ability to omit themselves of the direct responsibility to provide those qualities of VLEs that would be found to support mental health and exclude those qualities that are found to diminish mental health.

As a closing thought and to return to the experiential modus and inquiry of this review, we adjure future research to be guided by the question of how the student encounters their teacher within the VLE. Emmanuel Levinas, a philosopher who wrote extensively on the innate ethical experience that is garnered through face-to-face interaction, took a rare moment in his writing to offer insight on the dynamics of education. In Levinas’ Totality and Infinity, he notes that in being called to respond to the Other, “[teaching] designates an interior being that is capable of a relation with the exterior and does not take its own interiority for the totality of being” (Levinas, 1969, as cited in Zhao, 2016 , p. 324). Here, Levinas may appear to point to the disposition of the educator as one that offers the presence of self for the sake of the students’ being. This sentiment, taken along with the intriguing meta-analysis offered by Mesman et al. ( 2009 ) may do little to establish the “how” of education as conveyed through this inquiry, but certainly makes a tremendous stride in the realm of the “why” that institutions ought to work to expound among the current VLE modalities that they are imposing upon learning communities.

Data availability

Not Applicable.

Code availability

Al Karani, A., & Thobaity, A. (2020). Medical staff members’ experiences with Blackboard at Taif University, Saudi Arabia. Journal of Multidisciplinary Healthcare, 2020 (13), 1629–1634.

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Caprara, L., Caprara, C. Effects of virtual learning environments: A scoping review of literature. Educ Inf Technol 27 , 3683–3722 (2022).

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Original research article, classification of visual and non-visual learners using electroencephalographic alpha and gamma activities.

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  • 1 Centre of Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
  • 2 Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
  • 3 Asia Pacific Neuro-Biofeedback Association, Singapore, Singapore
  • 4 Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students’ EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8–10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k -nearest neighbor ( k -NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k -NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k -NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.


Human being growing in modern societies are exposed to certain learning environment. The way an individual processes information contributes toward an individual’s learning ability ( Kim et al., 2006 ). Many attributes are important in the learning scene, such as intelligence, learning environment, learning abilities ( Stern, 2017 ) and learning style, among which learning style is the most consistently studied ( Koć-Januchta et al., 2017 ; Yazici, 2017 ). Learning style is not a new concept, as it has been a topic of discussion for years ( Koć-Januchta et al., 2017 ). Learning style is defined as an individual’s preferred way of learning ( Plass et al., 1998 ).

Researchers associate learning styles with the patterns of information processing in the brain, known as cognitive styles. The benchmarks to differentiate between the learning style and cognitive style are defined as follows ( Mayer and Massa, 2003 ): The style preferred by individuals for representing and processing information is defined as a learning style. However, the methods of representing and processing information by the brain are classified under cognitive style. Researchers hypothesize that a relation exists between the learning style and cognitive style. The outcomes of the existing research clearly shows that the information processing is linked to the preferred learning style of an individual ( Ahn et al., 2010 ). Thus, the existing studies conclude that every individual has their own preferred learning style.

Learning style is further broken down into learning style models and learning style modalities ( Ahmad and Tasir, 2013 ). A theoretical coherence and a common framework for all learning style models are lacking ( Klašnja-Milićević et al., 2016 ). However, different studies reported that learning styles and preferences are constitutionally based on four learning modalities: visual (seeing), auditory (hearing), kinesthetic (moving), and tactile (touch) ( Klašnja-Milićević et al., 2016 ). Learning using technology, for example videos, can make the learning process interesting and create an enjoyable experience for the students ( Abid et al., 2016 ). These are the reasons why most educational models are based on visual and auditory modalities.

Further, according to statistics, 65% of the population is visual learners 1 ( Zopf et al., 2004 ). Visual learners learn by visual reinforcements, such as videos contents (see text footnote 1) ( Zopf et al., 2004 ). Many researchers have explored only the visual verbal learning style of the visual modality; Felder’s theory claims that the major leaning style is visual. Felder’s theory has four dimensions. Each learner is characterized based on its preference for each of these dimensions. The first dimension discriminates between an active and a reflective way of processing information. The second dimension is sensing versus intuitive learning. The third, visual-verbal dimension differentiates learners who remember best and therefore prefer to learn from what they have seen (e.g., pictures, diagrams and flow-charts), and learners who get more out of textual representations, regardless of whether they are written or spoken. In the fourth dimension, the learners are characterized according to their understanding. Sequential learners learn in small incremental steps and therefore have a linear learning progress. In contrast, global learners use a holistic thinking process and learn in large leaps ( Felder and Silverman, 1988 ).

Learning styles, such as visual learning style, are highly associated with brain patterns. Therefore, a person with a visual learning style is observed to have lower cognitive load when processing visual information. Visual learners are further categorized into visual/verbal learners: when an individual’s brain shows lower cognitive load while learning through written material, such as words, he/she is categorized as a verbal learner. Some interesting works have been carried out to investigate the visual verbal learning style of learners with hearing impairment. In such studies, researchers have primarily explored the difference between the visual and verbal learners, which are the two components of the visual learning modality, according to Felder’s theory ( Marschark et al., 2013 ). Another study is conducted to investigate the visual attention of the learners when a lecture is delivered using a power point presentation. This study also investigates the visual and verbal aspects of the visual modality ( Yang et al., 2013 ). In addition, another study ( Kim et al., 2006 ) have reported that the number of visual learners is as high as 80% when compared to the verbal learners among college students. This study selected students with a visual learning style using the Felder–Silverman’s index of learning style (ILS) subjective measures. The above-mentioned studies are some of the examples of studies relating to visual learning and other brain activities using only subjective measures such as learning style test. The learning style tests involve self-estimation of learning style, which has bias in it. However, there exists a relationship between learning and working memory ( Hindal et al., 2009 ). To do an independent analysis we explore this relationship to identify the learning style such as visual learners and non-visual learners of the participant. Here, in our experiment the control variable is Raven’s Advanced Progressive Matrices (RAMP) fluid intelligence test. This analysis is independent of learning tests purely looking at the brain patterns.

Suggesting learning style without considering brain pattern can increase the cognitive load. The cognitive load of the learner increased when information processing become complex for the learner. Knowing the learning style can optimized the learning and make it easier for students to understand the content. Also, it is possible one thinks that they have certain learning style such as visual, but in reality it’s just a learned behavior because that’s the only method of learning known to them which might not be according to their brain patterns, thus it’s important to find suitable learning style based on brain patterns.

The electroencephalogram (EEG) ( Teplan, 2002 ) is one of the many tools that can be used for recording brain patterns while performing mental activities or while resting.

The focus of this study is to differentiate the visual learners from the non-visual learners using EEG. Here, we will discuss how the EEG recording is interpreted by the researchers. In general, the EEG signal is divided into five bands: alpha, beta, delta, theta, and gamma ( Amin et al., 2017 ). In this study, the analysis is performed using the alpha and gamma bands. The analysis of theta and delta band is the part of our future work. The focus is on alpha and gamma because there exist a strong association between alpha waves and gamma waves and learning ( Gruber et al., 2002 ; Grabner et al., 2017 ).

Alpha waves have a frequency range of 8–12 Hz. Changes in the alpha frequency are observed during visual learning tasks as well as during intelligence tasks. Alpha activity increases in the frontal region and decreases in the right parietal and right temporal regions during visual learning ( Frederick et al., 2016 ; Tóth et al., 2017 ).

Gamma waves have a frequency range of 30–80 Hz ( Tseng et al., 2016 ). Gamma waves are highly associated with high-level visual information processing, such as visual learning ( Jia and Kohn, 2011 ). A decrease in the gamma band is observed in the frontal location during visual learning. Therefore, many existing studies are focused on gamma waves ( Yao et al., 2017 ).

The objective of this study is to classify visual learner and non-visual learner. For classification some meaningful information is needed to be extracted from EEG recorded signals. Thus, features such as PSD, PSE, and DWT are extracted to feed into the classifier. We use: (1) PSD: which is useful when some key features need to be extracted from EEG data ( Hamzah et al., 2016 ). The PSD feature is used by researchers ( Amin et al., 2014 ) for cognitive task analysis such as visual learning. This feature is obtained for EEG data using the Welch technique and the Hamming window with 50% overlapping epochs ( Amin et al., 2014 ). The analysis shows that a person with low intelligence has a higher value of the lower power of alpha and vice versa ( Harmony et al., 1996 ; Jaušovec, 2000 ; Doppelmayr et al., 2002 ; Grabner et al., 2004 ; Thatcher et al., 2005 ; Micheloyannis et al., 2006 ; Huang and Charyton, 2008 ; Riečanský and Katina, 2010 ). (2) DWT features are suitable for non-stationary signals ( Jahankhani et al., 2006 ). These features are robust enough and give discriminative information to distinguish the visual learners from non-visual learners. (3) PSE features has good effect for the change of non-linear dynamic states, it is suitable for small dataset which makes it suitable for EEG signals ( Zhang et al., 2008 ). The autoregressive, adaptive autoregressive ( Akhtar et al., 2012 ; Ali et al., 2016 ) are some of the other feature extraction methods for the non-stationary EEG signals.

The next step toward analyzing the brain waves is feature selection. There exist many feature selection techniques ( Chandrashekar and Sahin, 2014 ), but due to the high dimensional nature of EEG datasets, dimension reduction technique such as PCA is used in this study for feature selection.

The selected features are then given to the classifier to classify visual learner and non-visual learner. In classification, a machine-learning algorithm is trained and tested using a certain amount of experimental data to develop a model for the new related data ( Amin et al., 2017 ). The idea is to train the set of data having observations with a known category membership, using the features as independent variables to set the target between different feature spaces. To classify the EEG dataset, the classifier must be able to handle the following two issues: (1) Curse of dimensionality: Based on the amount of data that represents different classes, increases exponentially with the feature vector dimensionality. This occurs when the training data is small, and the feature vector is large; in this type of scenario, the classifier did not give good results. Therefore, it is recommended that training samples of the class is at least five times the training samples of the class of dimensionality ( Lotte et al., 2007 ). In the case of the EEG dataset, the dataset is small compared to the high dimensionality of the feature vector, which leads to poor classification ( Lotte et al., 2007 ). (2) The bias-variance trade-off: Bias is defined as the divergence between the estimated mapping and the possibly attained mapping. The bias is dependent on the training set. To classify the data with minimum error, the bias must be low. The stable classifier has a low variance and a high bias. The simple classifier has a low bias and a high variance, which renders the simple classifier more suitable for EEG datasets, as it outperforms the complex classifiers ( Lotte et al., 2007 ; Córdova et al., 2015 ).

In this study, we present a method for classifying the visual learners from non-visual learners based on the EEG signals and employed the PSD and DWT as a feature extraction, the PCA as a feature selection technique, and the k -NN and SVM as a machine-learning algorithm for the classification of two groups, i.e., visual learners and non-visual learners. We attempt to explore the brain neuronal behavior of the visual learners as compared to non-visual learners when the information is presented according to their preferred learning modality.

The paper is organized as follows: Materials and Methods, Results, Discussion, Limitations of the Study, and Conclusion.

Materials and Methods

This section comprehensively explains the overall process implemented for this study. From the EEG data, the pre-processing, feature extraction, feature selection and development of the brain model classifying the visual learners and non-visual learners using the k -NN and SVM classifier. The input to our system is an EEG signal. The next step is feature extraction, followed by feature selection and classification. Each block of the system starting from the data set is explained in detail below.

Thirty-four healthy university subjects (age: 18–30 years, 23.17 ± 3.04) were recruited for the experiment. All subjects had normal or “corrected to normal” vision. All subjects were free from neurological disorders and medications and did not have hearing impairments. All subjects are male. All subjects signed an informed consent document prior to the beginning of the trials. This study was approved by the Ethics Coordination Committee of the Universiti Teknologi PETRONAS ( Amin et al., 2015b ). The experimental procedure is same for all the participants.

Raven’s Advanced Progressive Matrix (RAPM) Test

Raven’s advanced progressive matrix (RAPM) ( Raven, 2000 ) is used to measure intellectual ability. It is a non-verbal test that commonly and directly measures two components of a fluid’s cognitive ability ( Raven, 2000 ) defined as: (i) “the ability to draw meaning out of confusion,” and (ii) “the ability to recall and reproduce information that has been made explicit and communicated from one to another.” It has 48 series of patterns that are further divided into two sets (I and II): One for practice and the other is to assess cognitive ability. Set I have 12 patterns that are used for practice. However, Set II has 36 patterns that are used to measure cognitive ability.

The pattern of the test consists of a 3 × 3 cell structure representing a certain geometrical shape, except the bottom-right cell, which is empty, as shown in Figure 1 . Eight options are available for the empty cell. For each correct answer the user gives a score “1” and a “0” for each incorrect answer. Users are given 10 min to complete Set I and 40 min to complete Set II ( Raven, 2000 ). We use RAMP memory test as we know, Memory plays an important part in learning as in the learned information is stored in memory. Memory is the expression of what one has learned. Thus, learning and memory are related ( Passolunghi and Costa, 2019 ).

Figure 1. Example of RAPM problem ( Amin et al., 2015a ).

Two main tasks are involved: (1) the learning task, and (2) memory or information retrieval task. The material use for learning task was based on biological contents related to human anatomy. The biological content was taken from commercially available high standard secondary curriculum (grade 11∼12). The content has high quality computer animations related to the complex human anatomy concepts, functions and diseases. The duration of this animated learning material is 8–10 min. The subjects had backgrounds in mathematics and engineering and had no prior knowledge of the learning content. Therefore, this selected learning content provide new information to the subjects and is suitable for the assessment of memory skills and learning. In addition to learning task a memory retrieval task was prepared. In the memory retrieval task, 20 multiple-choice questions (MCQs) that include the learned animated contents, were presented (see Figure 2 ). Each MCQ has four possible answers, out of which one is correct. The time to answer each MCQ is 30 s within a maximum time limit of 10 min. Subjects were asked to press a numeric key on the keyboard, serially numbered #1 to #4 corresponding to each possible answer.

Figure 2. Example of multiple-choice question ( Amin et al., 2015b ).

The subjects did the test in the following order. First RAMP was done. The subjects were divided into two groups based on their RAMP score. Which is explained in detail in experimental results section. The next step was eyes open/eyes close test. The third step was showing subjects the learning content. After 30-min break retrieval task was done. The second retrieval task was done after 2 months. The EEG was recorded during eyes open, eyes close, learning task and retrieval task. The retrieval task session done 30 min after learning task session is named as recall session 1 and recall session 2 is the retrieval task done 2 months after recall session 1.

To ensure that subjects did not have any background knowledge a pre-test was conducted where the subjects were asked to solve 10 questions related to learning the animated content. The exclusion criteria were based on the results of the pre-test; if the subject manages to answer more than 10% of the pre-test questions correctly, he was excluded from the experiment. Each subject was briefed on the procedure. At the end of the learning session, a 30-min break was given, after which the subjects took retrieval task to assess their learning performance. Each learning task was presented on a 42-inch TV screen at 1.5 m from the subject. All tasks were implemented with the E-Prime Professional 2.0 (Psychology Software Tools, Inc., Sharpsburg, PA, United States) ( Schneider et al., 2002 ).

Electrophysiological (EEG) Recording

The EEG continuously recorded the subject’s responses via 128 scalp loci using the HydroCel Geodesic Sensor Net (Electrical Geodesic Inc., Eugene, OR, United States) (shown in Figure 3 ). The notch filter is applied during EEG recording, to eliminate the power-line noise in the recorded EEG. All electrodes referenced a single vertex electrode, Cz (which is the standard configuration of the net), from which raw signals were amplified with the EGI NetAmps 300 amplifier’s band-pass filter (0.1–100 Hz). The impedance was maintained below 50 kΩ, and the sampling rate was 250 Hz. Impedances indicate electrode performance, it’s better when lower impedance value and poorer when higher impedance value. The high values of impedance introduce noise in the EEG signals that is why it is recommended to use low impedance values. To record good quality noise free EEG, the electrodes of EEG cap must have impedances within certain range. The range is defined as follow (0–50 kΩ) good quality, (50–100 kΩ) acceptable range, (100–200 kΩ) high value. We record EEG within the range of (0–50 kΩ) by keeping the impedances below 50 kΩ.

Figure 3. Placement of electrodes (HydroCel Geodesic Net 128 channels with Cz as a reference).

Behavioral Data Analysis

The behavioral data (performance of the subjects) is analyzed to determine the accuracy of the information retrieved by the subjects after the learning and retrieval tasks using the visual contents. The subjects are asked 20 questions of 1-min duration. The total length of time window is 20 min × 60 s = 1,200 s. The assessment of the learning performance are based on the correct responses and the reaction time per question for each subject. The reaction time reflects the information processing speed based on intelligence. The learning performance is then measured based on the percentage of correct responses.


After the recording of raw EEG data, each subject’s continuous EEG data was pre-processed with NetStation v4.5.4 (Electrical Geodesic, Inc., Eugene, OR, United States). A brief description of the pre-processing is provided here. (a) A band pass IIR filter was applied (0.5–48 Hz, roll off 12 dB octave) to remove DC components and high frequency muscular artifacts. (c) The artifacts such as eye movement and muscle movement are corrected using the surrogate model approach of the BESA software ( Roberts et al., 2010 ). Bad channels were discarded from the segments. The clean EEG is then exported to MATLAB for further processing.

Feature Extraction Methods

The relevant information extraction from raw signals is a critical step in the EEG pattern classification, owing to its direct influence on the classification performance. In this study, the PSE, PSD and DWT methods were used to extract the EEG features from frontal, occipital and parietal region for the classification of visual learners from non-visual learners. The clean EEG signals are then divided into the alpha (8–13 Hz), beta (13– 28 Hz), theta (4–8 Hz), and delta (0.5–4 Hz) and gamma band (25–100 Hz) frequency bands.

The PSD is computed using the FFT with the Welch method and hamming window to estimate the power spectrum of the EEG time series ( Welch, 1967 ) with 2-s segments (2 × 250 = 500 points), 50% overlapping (250 points) and kept the nfft as 512 points. In addition, the PSE is obtained by implementing the procedure mentioned in Zhang et al. (2008) .

Discrete wavelet transform is another feature that is extracted. DWT is famous method for EEG non-stationary signals. It’s an estimation technique where wavelet function is used to represent the signal as an infinite series of wavelets. Based on mother wavelet, the signal is a linear combination of wavelet functions and weighted wavelet coefficients ( Akhtar et al., 2012 ).

The features are extracted from frontal, occipital and parietal regions of brain. ANOVA is applied using Matlab to see statistically the variance of features. The P -value for power spectral density of alpha and gamma is p < 0.05 showing statistically independence of two groups. That is why we choose PSD of alpha and PSD of gamma, DWT of alpha and DWT of gamma for further analysis. The total number of computed PSD features for two sessions are 34 × 16 = 544 and 31 × 16 = 496. And the total number of computed features for DWT for two session are 34 × 29 = 986 and 34 × 29 = 899.

Feature Selection

The features extracted above show the discriminative information that is used for further analysis. The feature design for this EEG study is not a straightforward task. It has challenges such as a noisy environment, multiple sources, and overlapping due to multi-tasking in the brain. The EEG signals have poor signal-to-noise ratios. The curse of dimensionality is also present. Because of the above-mentioned challenges, feature selection is important. There are many methods for feature selection ( Chandrashekar and Sahin, 2014 ). For this study the features are selected using the PCA ( Dong and Qin, 2018 ) to deal with the challenge of curse of dimensionality. The PCA can be obtained by using the following steps:

(1) The first step is data normalization performed by subtracting the mean values from the columns.

(2) The covariance of the normalized data is then calculated.

(3) The eigenvector and eigenvalues can be calculated from the covariance matrix.

(4) A vector is obtained that consists of eigenvectors.

(5) The principal component is obtained by multiplying the transpose of the selected feature vector with the original data.

(6) The selected feature vector is obtained by taking the maximum of the principal component which corresponds to largest eigen values in the data.

The extracted features are the PSD of size [34 × 16] and DWT of [34 × 29]each. However, using the PCA, the selected feature vector is of size [34 × 8] and [34 × 15]. PCA is used with 99% variance. These 8 and 15 features are representing 99% variance so there is no need to add other features.

Brain Learning Model Using Classifiers

To distinguish the visual learners from the non-visual learners, a brain-learning model is developed using the k -NN and the SVM. The k -NN is a widely used technique for classification problems. In the k -NN, the k value is the value of the nearest neighbor. k is non-parametric; the rule of the thumb of choosing the k value is k   =   N 2 where N is the number of samples ( Altman, 1992 ). The idea is to distinguish the visual learners from the non-visual learners and therefore, we set the value of k = 3. The k value plays an important role as it draws a boundary that segregates the visual learners from the non-visual learners. For the k -NN, several options are available for the distance metric. However, for this model, the Mahalanobis distance metric is used. Since the EEG dataset is non-linear, the distance metric such as the Euclidian distance does not give good results as the Euclidean distance metric is more suitable for linear datasets. However, the Mahalanobis distance formula is the same as the Euclidian distance having a covariance parameter, which makes it a more suitable and practical option for data with non-linearities. The formula for the Mahalanobis distance is presented in equation.

Here, x is a set of observations, where μ is the mean of the observations and S is the covariance matrix.

The SVM classifier is best suitable for binary classifications. It classifies data by finding the best hyperplane that separates all data points of one class from other glasses. The best suitable hyperplane is the one with the largest margin ( Witten et al., 2016 ). SVM is another classifier used for this study.

The step taken before the model development is the randomization of data into two parts, here 10-fold cross validation is used for training and testing. The training set was used to train the model; however, testing is performed to evaluate the overall ability of the dataset’s training part. The above-mentioned method is the standard practice in machine learning used by many researchers in their work ( Zhang and Zhou, 2007 ).

The behavioral data is analyzed to measure the performance of the visual learners and non-visual learners. For learning, the correct responses and reaction times are computed for each participant. The reaction time shows the mental speed of information retrieval and is measured from the point where the MCQ is displayed until the participant presses a button for the selection of an answer. The percentage of correct responses per participant was then used to measure his learning performance. The total number of trials available per subject was 34 subjects × 20 MCQ = 680 trials. To assess the learning ability RAMP score is used. The subjects are divided into two equal groups using median score ( Amin et al., 2015b ). Based on the median score, the subjects who scored equal or above the median are considered as visual learners and those who scored less than the median are considered as non-visual learners. To classify the visual learner and non-visual learner, we analyze the retrieval task, the first retrieval task is recorded 30 min after the learning task (recall session 1) and the second retrieval task is recorded 2 months after the learning (recall session 2). To classify visual learners from non-visual learners the suitable features are selected, which can discriminate the two groups. To measure the statistical significance of the features statistical analysis is performed. First the features are extracted from frontal, occipital, and parietal regions of brain. The total number of computed features for two sessions (recall session 1 and recall session 2) are 34 × 16 = 544, 31 × 16 = 496 and 34 × 29 = 986, 31 × 29 = 899. Then one-way ANOVA with Tukey post hoc test is performed on extracted features. The results of one-way ANOVA show P -value for PSD of alpha and PSD of gamma, DWT of alpha and DWT of gamma ( p < 0.05) which shows statistically independence of two groups. However, the P -value of PSE did not show statistically significance because of that reason we use PSD and DWT feature for further analysis. For feature selection, the PCA is used to best describe the variance in the features and to reduce their dimensionality. We prefer PCA over LDA, because PCA perform better in case where number of samples per class is less. Whereas LDA works better with large dataset having multiple classes 2 .

To evaluate the model performance, the accuracy, sensitivity (true positive rate), and specificity (true negative rate) parameters are computed, and the receiver operating characteristic (ROC) curve is obtained. To calculate the accuracy, sensitivity, and specificity, the confusion matrix is first computed. A confusion matrix is used to describe the performance of a classification model on a set of data with known true values. Figure 4 shows the box plot indicating the significant pattern between a visual learner and a non-visual learner for the alpha and gamma bands of recall session 1 and recall session 2. The distinct mean level can be observed for the two groups, where the mean of the non-visual learner is higher than the mean of the visual learner for the alpha sub-band, which is similarly observed for the gamma band case.

Figure 4. Box plot of PSD for (i) alpha (ii) gamma with respect to the visual learner and non-visual learner groups. The focus of our study is the left and right brain hemispheres during the learning state. (A) Recall session 1 ( N = 34). (B) Recall session 2 ( N = 31).

Tables 1A,B show the average of the confusion matrix for the PSD of alpha and gamma waves obtained from different iterations, respectively. From Tables 1A,B , two predicted classes for the visual learner (L) and non-visual learner (NL) with n = 34 can be seen, meaning that 34 subjects were tested. From the results, we observed that the classifier predicted the Ls 16 times and the NLs 1 time for the alpha waves. For the gamma waves, the predicted Ls are 16 and the NLs are 1. However, there are actually 17 Ls and 17 NLs, where TP is a true positive, TN is true negative, FP is false positive, and FN is false negative. The TPs are the instances where the predicted visual learners are actually visual learners. The TNs are the instances where the predicted non-visual learners are actually non-visual learners. The FPs are when the visual non-learners are predicted as visual learners. The FNs are when the visual learners are predicted as non-visual learners.

Table 1. Confusion matrix and performance matrix of PSD of alpha and gamma band using k -NN classifier (recall session 1).

From the confusion matrix shown in Tables 1A,B , the accuracy, sensitivity, and specificity are calculated. Mathematically, the accuracy, sensitivity, and specificity parameters are shown in equations.

Table 1C shows the specificity, sensitivity, and accuracy corresponding to visual learners and non-visual learners for the PSD of alpha and gamma waves.

The ROC is generated by plotting the TP along the y -axis and the FP rate along the x -axis. Figure 5 shows the ROC curve of the k -NN classifier for the PSD of alpha and gamma waves of threshold value (0.5, 1, 1) for the PSD of alpha waves, and (0.5, 0.75, 0.75) for the PSD of gamma waves, with area under the curve (AUC) values of 0.97 and 0.94 for the PSD of alpha and gamma waves, respectively, as shown in Figure 5 . The ROC analysis is useful for many reasons: (1) It evaluates the discriminatory ability of continuous predictor for correctly assigning classification of two groups. (2) It gives the optimal cut-off point selection to eliminate the misclassification of two classes. (3) It shows the effectiveness of predictor. Tables 2A,B show the average of the confusion matrix for the PSD of alpha and gamma waves obtained from different iterations. From Tables 2A,B , two predicted classes for the L and NL with n = 31, meaning 31 subjects were tested. From the results, we observed that the classifier predicted the Ls 16 times and the NLs 15 times for the PSD of alpha waves. For the PSD of gamma waves, the predicted Ls are 15 and the NLs are 16. However, there are actually 15 Ls and 16 NLs.

Figure 5. (A) ROC for classification for k -NN classifier for PSD of alpha waves and gamma waves with AUC of 0.97 and 0.94 (recall session 1). (B) ROC for classification for k -NN classifier for PSD of alpha waves and gamma waves with AUC of 0.96 and 0.93 (recall session 2). (C) ROC for classification for k -NN classifier for DWT of alpha waves and gamma waves with AUC of 1 and 1 for DWT for recall session 1. (D) ROC for classification for k -NN classifier for DWT of alpha waves and gamma waves with AUC of 0.76 and 0.76 for DWT transform for recall session 2.

Table 2. Confusion matrix and performance matrix of PSD of alpha and gamma band using k -NN classifier (recall session 2).

From the confusion matrix given in Tables 2A,B , the accuracy, sensitivity, and specificity are calculated. Table 2C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the PSD of alpha and gamma waves.

The ROC is generated by plotting TP rate along the y -axis and the FP rate along the x -axis. Figure 5B shows the ROC curve of the k -NN classifier for the PSD of alpha and gamma waves of threshold value (0, 0.50, 1) for the PSD of alpha waves, and (0.5, 0.75, 0.75) for the PSD of gamma waves, with the AUC of 0.96 and 0.93 for the PSD of alpha and gamma waves, respectively. Tables 3A,B show the average of the confusion matrix for the DWT of alpha and gamma waves obtained from different iterations. From Tables 3A,B , two predicted classes for the L and NL with n = 34, meaning 34 subjects were tested. From the results, we observed that the classifier predicted the Ls 17 times and the NLs 17 times for the DWT of alpha waves. For the DWT of gamma waves, the predicted Ls are 17 and the NLs are 17. However, there are actually 17 Ls and 17 NLs.

Table 3. Confusion matrix and performance matrix of DWT of alpha and gamma band using k -NN classifier (recall session 1).

From the confusion matrix given in Tables 3A,B , the accuracy, sensitivity, and specificity are calculated. Table 3C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the DWT of alpha and gamma waves.

Figure 5C shows the ROC curve of the k -NN classifier for the DWT of alpha and gamma waves of threshold value (0, 1, 1) for the alpha waves, and (0, 1, 1) for the gamma waves, with the AUC of 1 and 1 for the alpha and gamma waves, respectively.

Tables 4A,B show the average of the confusion matrix for the DWT alpha and gamma waves obtained from different iterations. From Tables 4A,B , two predicted classes for the L and NL with n = 31, meaning 31 subjects were tested. From the results, we observed that the classifier predicted the Ls 12 times and the NLs 13 times for the alpha waves.

Table 4. Confusion matrix and performance matrix of DWT of alpha and gamma band using k -NN classifier (recall session 2).

For the DWT gamma waves, the predicted Ls are 12 and the NLs are 13. However, there are actually 15 Ls and 16 NLs. From the confusion matrix given in Tables 4A,B , the accuracy, sensitivity, and specificity are calculated. Table 4C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the DWT of alpha and gamma waves.

Figure 5D shows the ROC curve of the k -NN classifier for the DWT alpha and gamma waves of threshold value (0, 0.78, 1) for the alpha waves, and (0, 0.78, 1) for the gamma waves, with the AUC of 0.76 and 0.76 for the alpha and gamma waves, respectively.

Tables 5A,B show the average of the confusion matrix for the PSD of alpha and gamma waves obtained from different iterations. From Tables 5A,B , two predicted classes for the L and NL with n = 34, meaning 34 subjects were tested. From the results, we observed that the classifier predicted the Ls 16 times and the NLs 16 times for the alpha waves. For the gamma waves, the predicted Ls are 16 and the NLs are 16. However, there are actually 17 Ls and 17 NLs.

Table 5. Confusion matrix and performance matrix of PSD of alpha and gamma band using SVM classifier (recall session 1).

Table 5C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the PSD of alpha and gamma waves.

Figure 6A shows the ROC curve of the SVM classifier for the PSD alpha and Gamma waves with the AUC of 0.97 and 0.96 of PSD features for recall session 1.

Figure 6. (A) ROC for classification for SVM classifier for PSD of alpha waves and gamma waves with AUC of 0.97 and 0.96 for PSD for recall session 1. (B) ROC for classification for SVM classifier for PSD of alpha and gamma waves with AUC of 1 and 0.97 for PSD for recall session 2. (C) ROC for classification for SVM classifier for alpha waves and gamma waves with AUC of 0.79 and 0.82 for DWT for recall session 1. (D) ROC for classification for SVM classifier for DWT of alpha and gamma waves with AUC of 0.56 and 0.74 for DWT for recall session 2.

Tables 6A,B show the average of the confusion matrix for the PSD of alpha and gamma waves obtained from different iterations. From Tables 6A,B , two predicted classes for the L and NL with n = 31, meaning 31 subjects were tested. From the results, we observed that the classifier predicted the Ls 15 times and the NLs 16 times for the alpha waves. For the gamma waves, the predicted Ls are 14 and the NLs are 15. However, there are actually 15 Ls and 16 NLs.

Table 6. Confusion matrix and performance matrix of PSD of alpha and gamma band using SVM classifier (recall session 2).

From the confusion matrix given in Tables 6A,B , the accuracy, sensitivity, and specificity are calculated. Table 6C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the PSD of alpha and gamma waves.

Figure 6B shows the ROC curve of the SVM classifier for the PSD of alpha and Gamma waves with the AUC of 1 and 0.97 of PSD features for recall session 2.

Tables 7A,B show the average of the confusion matrix for the DWT of alpha and gamma waves obtained from different iterations. From Tables 7A,B , two predicted classes for the L and NL with n = 34, meaning 34 subjects were tested. From the results, we observed that the classifier predicted the Ls 13 times and the NLs 13 times for the alpha waves. For the gamma waves, the predicted Ls are 14 and the NLs are 14. However, there are actually 17 Ls and 17 NLs.

Table 7. Confusion matrix and performance matrix of DWT of alpha and gamma band using SVM classifier (recall session 1).

From the confusion matrix given in Tables 7A,B , the accuracy, sensitivity, and specificity are calculated. Table 7C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the DWT of alpha and gamma waves.

Figure 6C shows the ROC curve of the SVM classifier for the DWT of alpha and gamma waves with the AUC of 0.79 and 0.82 of DWT features for recall session 1.

Tables 8A,B show the average of the confusion matrix for the DWT of alpha and gamma waves obtained from different iterations. From Tables 8A,B , two predicted classes for the L and NL with n = 31, meaning 31 subjects were tested. From the results, we observed that the classifier predicted the Ls 9 times and the NLs 10 times for the alpha waves. For the gamma waves, the predicted Ls are 12 and the NLs are 11. However, there are actually 15 Ls and 16 NLs.

Table 8. Confusion matrix and performance matrix of DWT of alpha and gamma band using SVM classifier (recall session 2).

From the confusion matrix given in Tables 8A,B , the accuracy, sensitivity, and specificity are calculated. Table 8C shows the specificity, sensitivity, and accuracy corresponding to the Ls and NLs for the DWT of alpha and gamma waves.

Figure 6D shows the ROC curve of the SVM classifier for the DWT of alpha and gamma waves with the AUC of 0.56 and 0.74 of DWT features for recall session 2.

Figure 5 gives the ROC of k -NN classifier for PSD and DWT of alpha and gamma waves of recall session 1 and recall session 2. From Figure 5A , we observe that the PSD of alpha for recall session 1 has an AUC of 0.97, which is high. For recall session 2, the AUC slightly decreases at 0.96 but still remains high. For the PSD of gamma, the AUC is 0.94 for both sessions. This shows this model has a good class separation capacity. The model is robust since the AUC remains high for recall session 2 although it is conducted 2 months after recall session 1.

For DWT features of recall session 1 and recall session 2 the AUC is 1 and 0.76, respectively, for both DWT of alpha and gamma waves. This shows that DWT has great capabilities of class separation but does not show robustness since the AUC drops from 1 to 0.76 for recall session 2.

Similarly, Figure 6 shows the SVM classifier for PSD and DWT of alpha and gamma waves of recall session 1 and recall session 2. Here the PSD of alpha and gamma has the AUC of 0.97 and 0.96 for recall session 1 and AUC of 1 and 0.97 for recall session 2. This shows about more the robustness of PSD is class separation since the AUC values remain high for both sessions, although the classifier has been changed.

For DWT of alpha and gamma the value of AUC is 0.79 and 0.82 for recall session 1 and 0.56 and 0.74 for recall session 2. Here, also the AUC is slightly high in case of recall session 1. Compared to PSD, the AUC values for DWT are much lower and also the decrease between recall session 1 and recall session 2 is more considerable. That show that DWT has less class separation capabilities and is less robust.

The results show that PSD feature can be more reliably used for distinguishing visual learners from visual non-learner.

In this study, the learning styles are studied by analyzing the EEG signals, and the machine-learning classifier is used for the classification of visual learners from non-visual learners ( Figure 7 ).

Figure 7. The topographical map of a visual learner and non-visual learner. Brain wave activation was found very high in left posterior temporal and left frontal region in visual non-learner. However comparable activation, but lower in visual learner scenario.

According to the authors’ knowledge, none have distinguished a visual learner from a non-visual learner using video contents as stimuli along with EEG signals. Human has exceptional learning abilities, which allows individuals at learning stage to adapt to different learning environment. However, there is a difference in learning abilities of individual which become obvious when they progress in life. There are different models and learning theories that explain how individual learns ( Stern, 2017 ). Many researchers have reported findings based on learning theories to identify the learning style of the learners. The Felder and Silverman theory is one of the learning theories that is used by most studies that explore the learning style of the learners ( Klašnja-Milićević et al., 2017 ). In Marschark et al. (2013) and Kim et al. (2015) , the researchers explored static media, such as pictures and words, to categorize the visual learners and the verbal learners. In these studies, they accurately classified a visual learner from a verbal learner based on the decreased value of theta, and the increased value of beta in the case of a visual learner, and vice versa for a verbal learner. Their drawback is that the analysis is based on static media, which does not include all the aspects of visual modality. However, in our study, videos content (dynamic media) is used to include all the aspects of visual modality along with studying the brain responses via EEG recordings and thus eliminating the drawback of the above-mentioned studies ( Kim et al., 2015 ).

Recent studies based on Kolb’s model, another learning theory based on the learner’s internal cognitive processes, used an online test for the subjective measure, and EEG with only the eyes closed condition ( Ali et al., 2016 ). Kolb’s model with the EEG eyes closed condition does not fully represent the relationship with a visual learner because the online questioner asks the students to select their suitable learning style according to their viewpoint, which is the main limitation of Kolb’s theory ( Kelly, 1997 ). Kolb’s test is based solely on the way learners rate themselves. It does not rate the learning style preferences through standards or behaviors. The reported findings were based on the resting state of the EEG recordings, which does not explain the whole picture of the neuronal responses during learning and/or during the retrieval of learned information. The present study recorded the EEG during the resting states, learning tasks, and information retrieval tasks. Thus, in this study, the decision of being a visual learner or non-learner is based on the neuronal responses recorded during the information retrieval tasks combined with the machine-learning algorithm, rather than using a subjective questionnaire, such as one reported in the resting state EEG ( Ali et al., 2016 ).

As mentioned above, the existing studies do not used videos as a stimulus to classify the visual learning style, especially in engineering disciplines. Identifying a learner as a visual learner using videos is an important component of the visual modality. Previous results are based on only one component of the Felder theory, which is visual-verbal, as reported in Felder and Silverman (1988) . All these previous works neglect the important aspect of the visual learning modality, such as videos (dynamic media), and focus only on pictures and words (static media).

Considering these limitations, we have attempted to present a classification-based model to identify visual learners using video (dynamic media) contents for learning; videos are more reliable and include all the aspects of visual learning modalities and is a more realistic way of presenting information in a relatable manner. Here, the EEG recordings are recorded while performing the learning and memory tasks. We have computed the PSD and DWT for the alpha and gamma frequencies of the EEG, recorded during the learning tasks and retrieval tasks and discriminated the subjects between groups, i.e., visual learners and non-learners. Since, we are distinguishing visual learner and non-visual learner. A separate analysis of alpha and gamma waves is required. That’s why we did not use all the frequency band together. But we saw the effect of alpha-gamma together. The results show that combining alpha-gamma did not increase the accuracy of our classification model. According to Arnaldo et al. (2018) , variations are observed in the alpha and gamma bands during cognitive tasks, such as the visualization of learning tasks. The results of our study identified a methodology to classify visual learners from non-visual learners. This study confirmed that the learning styles of individuals influence the neuronal electrical potentials generated during the learning and retrieval of learned information. Based on our results, we conclude that subjects who are visual learners learned better using videos (dynamic media), which is easier to understand and relatable. Thus, the further development of learning modalities can be considered as future work. In addition, for further studies, the authors believe that the brain neuronal signals will facilitate the understanding of the impact of changes in the learning modalities.

Limitations of the Study

The study has few limitations. The small sample size is not enough to predict the learning style of the students. However, future studies can be conducted to predict learning style of the students. In addition, this study investigates the learning style of university students only. Finally, the learning material used in this study was related to human anatomy and physiology contents; thus, learning style cannot be generalized to link with learning ability of all types of academic learning contents or memory recall ability. Also, EEG is the only modality we use to record the brain signals. Although EEG is considered one of the favorable methods it has few caveats. It has excellent temporal resolution but poor spatial resolution. Volume conduction is also one of the limitations.

In this work, we have proposed a brain-learning model for identifying visual learners and non-visual learners using EEG signals. The neural activity of the subjects is observed in all regions of the brain. Three different features are extracted and tested to find the stabilized features for the task in hand. To distinguish the visual learners from the non-visual learners, the PSD and DWT features are extracted over 16 scalp sites such as frontal, occipital, and parietal regions which play active role during visual learning, and fewer features were selected using PCA to train and test the classifier. The k -NN classifier with the Mahalanobis distance and SVM are used to classify the visual learners and non-visual learners. The classification accuracies were recorded as 97% and 94% for the PSD alpha and gamma bands for the recall session 1, and 96% and 93% for the recall session 2, for the visual learners and non-visual learners, respectively. For DWT features using k -NN classifier 68% and 100% accuracy rate for recall session 1 and 100% accuracies rate for the recall session 2 for the alpha and gamma band is recorded. For PSD alpha and gamma band 97% and 96% accuracies rate for the recall session 1, 100% and 95% accuracies rate for recall session 2 using SVM classifier are reported. Similarly 79% and 82% accuracy for recall session 1 and 56% and 74% accuracy for recall session 2 for DWT features using SVM classifier are reported. From above results, we concluded that PSD for SVM shows the best results for this study.

The EEG alpha and gamma bands showed good agreement with the learning process. The analyses of these frequency bands indicate the clear difference between the visual learners and non-visual learners. Therefore, our proposed brain-learning model will be helpful in academic applications, such as to help university students to identify visual learners and non-visual learners. This work can be extended by conducting further experiments to explore other learning modalities, such as audio and kinesthetic, using EEG signals.

Ethics Statement

The protocol of this study was approved by the ethics coordination committee, Universiti Teknologi PETRONAS, Malaysia, and by the Human Research Ethics Committee of the Universiti Sains Malaysia.

Author Contributions

HA and AM designed the study protocol and collected the data. SJ and IF pre-processed and analyzed the EEG signals. All authors contributed in the preparation of the manuscript and reviewed it.

This research is supported by Ministry of Education Malaysia (MoE) under Higher Institution Centre of Excellence (HICoE) Scheme awarded to Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Malaysia. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : EEG study, learning styles, visual learner, feature extraction, classification

Citation: Jawed S, Amin HU, Malik AS and Faye I (2019) Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities. Front. Behav. Neurosci. 13:86. doi: 10.3389/fnbeh.2019.00086

Received: 08 November 2018; Accepted: 11 April 2019; Published: 07 May 2019.

Reviewed by:

Copyright © 2019 Jawed, Amin, Malik and Faye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ibrahima Faye, [email protected]

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The Fight Over Academic Freedom

Amid spiraling campus speech debates, many professors are rallying in defense of a bedrock principle. But can they agree on just what it means?

Inscribed on a gate at Harvard are the words “Open ye the gates that the righteous nation which keepeth the truth may enter in.”

By Jennifer Schuessler

Academic freedom is a bedrock of the modern American university. And lately, it seems to be coming under fire from all directions.

For many scholars, the biggest danger is at public universities in Republican-controlled states like Florida, where Gov. Ron DeSantis has led the passage of laws that restrict what can be taught and spearheaded efforts to reshape whole institutions. But at some elite private campuses, faculty have increasingly begun organizing against a very different threat.

Over the past year, faculty groups dedicated to academic freedom have sprung up at Harvard, Yale and Columbia, where even some liberal scholars argue that a prevailing progressive orthodoxy has created a climate of self-censorship and fear that stifles open inquiry.

The fallout from the Hamas-led Oct. 7 attack on Israel has upended many campuses, as college presidents have been ousted, campus protest has been restricted and alumni , donors and politicians have pushed for greater control. And it has also scrambled the politics of academic freedom itself.

In recent years, academic freedom, like free speech more generally, has become coded as a conservative cause, seen as a rallying cry for those who want to battle academia’s liberal tilt.

Now, continuing campus protest over the Israel-Gaza war has, in some cases, turned the debate on its head.

Some ask why, after years of restricting speech that makes some members of certain minority groups feel “unsafe,” administrators are suddenly defending the right to speech that some Jewish students find threatening. Others accuse longtime opponents of diversity, equity and inclusion efforts of cynically weaponizing those principles to suppress pro-Palestinian views.

The roiling debates have even opened up rifts among champions of academic freedom. Jeannie Suk Gersen, a professor at Harvard Law School and a leader of the Council on Academic Freedom at Harvard, said that the cause stands “at a crossroads.”

“Do we think about academic freedom as something that protects everyone, regardless of content and ideology and politics?” she said. Or do we “carve out an exception,” as some advocates seem to argue, and forbid speech that is considered anti-Israel or antisemitic?

A Slippery Concept

It’s a profoundly unsettled moment on many campuses, which has left many academics feeling vulnerable. And even in calmer times, academic freedom can be an esoteric and slippery concept.

The American Association of University Professors defines it as “the freedom of a researcher in higher education to investigate and discuss the issues in his or her academic field, and teach or publish findings without interference from political figures, boards of trustees, donors or other entities.”

While academic freedom is often conflated with the broader principle of free speech, it is distinct from it. Under the First Amendment, all speech is equal before the state. But academic freedom depends on expertise and judgment — “the notion,” as the legal scholar Robert C. Post has put it , that “there are true ideas and false ideas,” and that it is the job of scholars to distinguish them.

Defending the rights of academics may be a hard sell today, as trust in higher education has dropped sharply amid partisan debates about teaching and concern over debt and high college costs. But academic freedom, experts say, is not about the privileges of professors, but about protecting the university’s core purpose and social value.

“The mission of a university is to sponsor truth-seeking scholarship and provide non-indoctrinating teaching,” said Robert P. George, a professor of jurisprudence at Princeton and a founder of the Academic Freedom Alliance , a multi-campus group created in 2021.

And for that to happen, George said, “we must be free to challenge any view or belief.”

Until recently, faculty at elite private universities may have felt immune from the kind of overt political interference unfolding in Florida, where Governor DeSantis’s efforts threaten “the very survival of meaningful higher education in the state,” according to a recent A.A.U.P. report .

But concern is now surging at private universities too, as congressional investigations of campus antisemitism at Harvard and a growing number of other schools have morphed into what some see as dangerously open-ended fishing expeditions .

Harvard, the nation’s oldest and richest university, has long been a prime target for critics of higher education. Since Oct. 7, it has also been the scene of colliding arguments about academic freedom — and how to defend it.

Much of the action has centered on the Council on Academic Freedom at Harvard , a faculty group founded last spring to promote “free inquiry, intellectual diversity and civil discourse.”

The group, which started with roughly 70 members, now has about 170. Politically, they range from conservatives and center-right figures to more traditional liberals, and include such prominent figures as the psychologist Steven Pinker, the legal scholars Randall Kennedy and Janet Halley, the economists Jason Furman and Lawrence Summers, the former medical school dean Jeffrey Flier and the political philosopher Danielle Allen.

The group was formed out of longstanding concerns, organizers say, though one catalyst was the case of Carole Hooven, a longtime lecturer in evolutionary biology. Hooven came under fire after a 2021 television interview in which she said that while diverse gender identities should be respected, there are just two biological sexes, male and female, which are “designated by the kinds of gametes we produce.”

The student leader of her department’s diversity task force, writing on social media, called her comments “transphobic and harmful,” and graduate students declined to serve as teaching assistants for her course on hormones and human behavior. Hooven, who did not have tenure, left her position in January 2023, after receiving what she has described as no support from the administration. (She is now a nonresident fellow at the American Enterprise Institute and an unpaid associate in Pinker’s lab.)

In an interview, Pinker said that her case, along with others , showed that Harvard had become rife with intolerance and self-censorship.

“Leftist consensus had become so entrenched,” he said, “that anything that conformed to it was self-evidently true, while anything that disagreed with it was self-evidently evil.”

In an opinion article in The Boston Globe announcing the group, Pinker and Bertha Madras, a professor of psychobiology, said it would defend reasoned debate against those who would shut it down. “When activists are shouting into an administrator’s ear,” they wrote, “we will speak calmly but vigorously into the other one.”

Free Speech, or Harassment?

The group drew a skeptical initial response from some, including faculty members who saw it as vehicle for the views of prominent members like Pinker, a critic of D.E.I. initiatives and a longtime advocate for greater “viewpoint diversity” on campus. An editorial in The Harvard Crimson accused the group of caricaturing activists and seeming to take “a one-sided view of academic freedom.”

Then came Oct. 7, which exposed fissures within the council itself.

Their email discussion group, like much of the campus, lit up with scorching debate. One heated topic was how to respond to the outcry over a letter issued by the Harvard Undergraduate Palestine Solidarity Committee immediately after the Oct. 7 attack, which declared that the Israeli government was “entirely responsible for all unfolding violence.”

The hedge-fund manager Bill Ackman, a Harvard donor, demanded that the university release the names of students affiliated with the 30 campus groups that initially endorsed the letter, so employers could avoid hiring them. A “doxxing truck,” sponsored by the conservative group Accuracy in Media, appeared in Harvard Square, with a screen showing photographs of affiliated students under the label “Harvard’s Leading Antisemites.”

To some council members , harsh criticism of the students was part of the rough and tumble of free speech, and the truck, paid for by an off-campus group, lay beyond the group’s purview. But to others, the denunciations crossed the line from legitimate criticism to personal attacks that put students in danger and chilled speech more broadly.

Ultimately, the council made no statement. Pinker, one of five co-presidents, said it was decided that the optics would be off, given what he described as Harvard’s dismal record on free speech .

Defending offensive speech “just at the moment when it involves absolving the killers and rapists of Jews didn’t seem like an auspicious first statement,” he said.

Kennedy, the law professor, believes that charges of campus antisemitism have been exaggerated and weaponized by partisans. But he agreed that criticism of the student letter was within bounds.

“People are unrealistic when they say, ‘We want free speech, we want debate, we want difficult conversations,’” he said. “But then we want all smiles.”

For some council members, however, the fracas was “a clarifying moment,” as Ryan Enos, a professor of government, put it in an interview.

Enos, who describes himself as a liberal, said he had initially agreed with conservative colleagues that the biggest threat to academic freedom at Harvard was “the political homogeneity on campus.” But after Oct. 7, he said, it was startling to see prominent council members calling on the administration to condemn or even punish student speech.

Enos quit the council, saying members were “being hypocrites.” In the face of calls to punish speech, he said in the interview, “they ran away with their tail between their legs.”

He said he was also disturbed by the council’s lack of response to threats by Republican congressmen to revoke Harvard’s tax-exempt status , which he called “a shocking affront to academic freedom.”

“Liberals at places like Harvard were having a hard time defending academic freedom anyway,” Enos said. “Now, people are going to be even more skeptical.”

Gersen, another co-president, said the group was still new and “finding its way.” She was among 700 faculty members who signed a letter in December urging the Harvard’s board not to fire the president, Claudine Gay, and has described congressional hearings in which Representative Elise Stefanik grilled Gay and two other university presidents as “a McCarthy-esque spectacle.”

Other members saw things differently. But for a group dedicated to open debate, Gersen said, disagreement — including about academic freedom — “is a feature, not a bug.”

A Multi-Campus Movement

Still, the suspicion that groups rallying under the banner of academic freedom are pushing a specific ideological agenda has extended to some other campuses.

At Yale, a group called Faculty for Yale , introduced on Feb. 13, is urging the university to “rededicate itself to its fundamental mission” and “insist on the primacy of teaching, learning and research as distinct from activism and advocacy.”

So far, the group has garnered nearly 80 public supporters. But another group of professors immediately issued a counter-letter, urging Yale’s leadership to recognize the importance of diversity and to defend American universities against attacks from donors, politicians and “members of their own faculty, who argue that universities have lost their way.”

At Columbia, leaders of the Columbia Academic Freedom Council, a faculty group formed last month, emphasize in an interview that they were not a right-wing or a left-wing group.

“We want to occupy the center,” said James Applegate, an astrophysicist.

But the politics of free speech are fraught at Columbia, where the moves to suspend two pro-Palestinian campus groups and limit faculty and student protest have been assailed by some as censorship and applauded by others.

The group has not yet made the names of its more than 70 founding members public. Jacqueline Gottlieb, a neuroscientist, said some interested junior faculty had been wary to join, lest it complicate their tenure prospects.

“This is an illustration” of the problem, she said. “People are afraid.”

At Harvard, the Council on Academic Freedom recently endorsed a broad statement of principles , which called on the university to vigorously defend academic freedom, including against “attempts to use state power to curtail” it.

The philosopher Edward Hall, a co-president, said he would have been “happy” if the group had spoken out against the so-called doxxing truck. But parsing threats to academic freedom is “an intellectually complicated question.”

“There are a range of clear cases,” he said. “But what landed on our plates were unclear cases.”

Jennifer Schuessler is a culture reporter covering intellectual life and the world of ideas. She is based in New York. More about Jennifer Schuessler

News | Sherman Oaks Center for Enriched Studies…

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News | Sherman Oaks Center for Enriched Studies repeats as LAUSD’s Academic Decathlon champ

45 lausd high schools sent nearly 700 students to compete in this year’s academic decathlon, representing nearly every corner of the district.

scholarly articles visual learners

Sherman Oaks Center for Enriched Studies won the Los Angeles Unified School District’s 43rd Academic Decathlon on Saturday, Feb. 24.

In an auditorium packed with students bouncing about with nerves and excitement, along with their proud parents and coaches, the team received the Superintendent’s Trophy — presented, aptly, by LAUSD Superintendent Alberto Carvalho himself.

Los Angeles Unified School District Superintendent Alberto Carvalho, left, hands...

Los Angeles Unified School District Superintendent Alberto Carvalho, left, hands out a medal to Apollo Colligan of Science Academy STEM Magnet during the Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Daniel Badiak of John Marshall High School accepts the Coach...

Daniel Badiak of John Marshall High School accepts the Coach of the Year award at the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Economics gold medal finalists take the stage at the 43rd...

Economics gold medal finalists take the stage at the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Sherman Oaks Center for Enriched Studies captured top team honors...

Sherman Oaks Center for Enriched Studies captured top team honors at the 43rd Annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Sherman Oaks Center for Enriched Studies captured top team honors...

Los Angeles Unified School District Superintendent Alberto Carvalho speaks during the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Apollo Colligan of Science Academy STEM Magnet is a multi-medal...

Apollo Colligan of Science Academy STEM Magnet is a multi-medal winner at the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Los Angeles Unified School District Superintendent Alberto Carvalho awards the...

Los Angeles Unified School District Superintendent Alberto Carvalho awards the trophy for top academic decathlon team honors to staff and students from Sherman Oaks Center for Enriched Studies on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Airin Avanesian of Verdugo High School reacts after receiving one...

Airin Avanesian of Verdugo High School reacts after receiving one of several medals at the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Scoring 40,392.4 points out of a possible 60,000 points, Sherman Oaks Center of Enriched Studies took home first place — for the second consecutive year.

The school snagged top honors thanks to students Eva Karamanoukian, Jaylen Patel, Jericho Milrad, Kavidu De Silva, Luke Bula, Luke Leung, Nathan Ark, Rose Ortiz and Sarah Kovalev, as well as coach Suzanna Gordon.

“These students inspire me every day to really bring it and be the best person I can be and the best educator I can be,” Gordon said. “It’s just inspiring to work with young people who will apply themselves to a task that is daunting.”

Forty-five LAUSD high schools sent nearly 700 students to compete in this year’s academic decathlon, representing nearly every corner of the district, from Sylmar to Westlake. The awards ceremony was held at Garfield High School in East Los Angeles.

Carvalho, who presented the winner’s trophy to Sherman Oaks Center for Enriched Studies, said: “Let me express to all of you academic decathletes my sincere appreciation for your determination, your superior intellect and your presence, not only here today, but in your schools, where you serve as an inspiration to other students.”

The 2023-24 school year’s academic decathlon tested students’ knowledge of the theme “Technology and Humanity.” Decathletes spent nights, weekends, lunchtimes and free time preparing for a year in 10 subject areas, including art, economics, science and math.

Throughout the year, each school’s teams competed in several events including an interview round, a speech round and Super Quiz, a live trivia event. Super Quiz was held on Feb. 3 at Edward R. Roybal Learning Center, with Verdugo Hills High School, Garfield High School and Sherman Oaks Center for Enriched Studies winning in a three-way tie. Bell High School and Maywood Center for Enriched Studies tied for second in Super Quiz and Science Academy STEM Magnet placed third.

Academic decathlon was founded in 1968 and became a nationwide competition in 1981. LAUSD’s academic decathlon program, co-run by Beyond the Bell, has a long history of achievement, with 23 state titles and 19 national championships.

“Just participating means you spent endless hours preparing,” LAUSD Board President Jackie Goldberg said in an address to the students. “You have spent the time and energy and you will never forget these things. They enrich your life.”

Per academic decathlon policies, each group of nine students was split into three divisions, based on Grade Point Average: Varsity (3.199 GPA and below), Scholastic (3.2 to 3.799 GPA) and Honors (3.8 GPA and above).

“Our teams are a very good representation of the diversity and culture and different experiences that we have at our LAUSD schools,” LAUSD academic decathlon coordinator Neena Agnihotri said. “Students of all GPA levels can participate and it’s a very unique program.”

Along with the winning teams, several individual honors and scholarships of up to $1,500 were awarded. To the sound of erupting applause, student honorees walked up to the stage with excited jumps and teary eyes.

Airin Avenesian of Verdugo High School reacts after receiving one of several medals at the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

“This is game time,” LAUSD Chief Academic Officer Frances Baez said. “This is when we get to show the best and brightest of LAUSD. Being here shows your stamina and your love of learning.”

Airin Avanesian, an 11th grade student at Verdugo Hills High, received the Super Decathlete award for the highest individual score in the district.

“I spent the whole week manifesting it,” Avanesian said. “There are so many schools with amazing decathletes, so it was a lot of competition. I worked hard for this for three years.”

Mathew Cortez from James A. Garfield High School, finished second and Sarah Kovalev from Sherman Oaks CES was third.

Daniel Badiak of John Marshall High School accepts the Coach of the Year award at the 43rd annual Academic Decathlon Awards Ceremony on Saturday, Feb. 24, 2024, at Garfield High School in East Los Angeles. (Photo by Howard Freshman, Contributing Photographer)

Daniel Badiak, an English teacher at John Marshall High School in Los Feliz, received the Coach of the Year award.

“All I try to teach them is to be nice, to have fun,” Badiak said. “If we win we win, if we don’t we don’t, but I hope we’re known as the kindest team. I hope we’re the most supportive team.”

Administrator of the Year honors went to Gabriel Duran, the principal at MaCES Magnet.

Sherman Oaks Center for Enriched Studies, along with six other high-scoring LAUSD teams will now advance to the state-level competition, which will be held in Santa Clara, March 21-24.

LAUSD schools have snared 23 state Academic Decathlon championships – more than any district in the state – as well as 19 national crowns.

“It feels pretty unreal,” Sherman Oaks Center for Enriched Studies student Jaylen Patel said. “I felt my heart racing while I sat there waiting, but it’s so special to see it pay off.”

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Title: vl-trojan: multimodal instruction backdoor attacks against autoregressive visual language models.

Abstract: Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning capabilities in a multimodal context. Recently, multimodal instruction tuning has been proposed to further enhance instruction-following abilities. However, we uncover the potential threat posed by backdoor attacks on autoregressive VLMs during instruction tuning. Adversaries can implant a backdoor by injecting poisoned samples with triggers embedded in instructions or images, enabling malicious manipulation of the victim model's predictions with predefined triggers. Nevertheless, the frozen visual encoder in autoregressive VLMs imposes constraints on the learning of conventional image triggers. Additionally, adversaries may encounter restrictions in accessing the parameters and architectures of the victim model. To address these challenges, we propose a multimodal instruction backdoor attack, namely VL-Trojan. Our approach facilitates image trigger learning through an isolating and clustering strategy and enhance black-box-attack efficacy via an iterative character-level text trigger generation method. Our attack successfully induces target outputs during inference, significantly surpassing baselines (+62.52\%) in ASR. Moreover, it demonstrates robustness across various model scales and few-shot in-context reasoning scenarios.

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Investigation of the effects of auditory and visual stimuli on attention

Associated data.

The data that has been used is confidential.

Attentional resources limit our perceptual capacities. One vital point is whether these resources are allotted severally to every sense or shared between them. We addressed this problem via means of topics to carry out a dual-task, both in the same modality or other modalities (visual and auditory). The primary task is to count the number of passes of the participants while watching the video that requires visual and auditory attention. Concurrently, they were also asked to notice the pure tones and visual events in the song during the video while counting their pass numbers. The results show that while the auditory task reduced the detection ability visual events task, the dual-task had a significant effect. Previous studies support that tasks requiring simultaneous auditory and visual attention affect each other. Our results have clear implications for showing that performance decreases in dual-task as the perceptual load increases.

Perception; Attention tasks; Visual attention; Auditory attention; Selective attention.

1. Introduction

In order to successfully interact with the stimuli in the environment, we need to process the information most relevant to our task selectively. This mechanism is usually termed “attention” ( James, 1950 ). In daily life, people often complete multiple tasks at the same time without any problems. Several studies have shown that the amount of information people can handle is very limited ( Marois and Ivanoff, 2005 ). In other words, with the help of the attention mechanism, people can only selectively pay attention to the limited information in the environment and ignore other information in the environment. When dual tasks are completed simultaneously, people often do not do one or two tasks well compared with performing one task. So attention has a capacity. The attentional Capacity Model states that attention is a finite resource and limits a person's capacity to perform a cognitive task ( Kahneman, 1973 ). However, in many everyday and expert situations, people carry out multiple tasks successfully, such as driving while conversing, playing a piano piece accurately, and expressively ( Cocchini et al., 2017 ).

When multiple perceptual challenges are carried out on equal time, standard overall performance decreases due to underlying processing limitations. This occurs even for simple tasks such as detect color or identifying the pitch of a tone ( Huang et al., 2004 ; Pashler, 1992 ; Pashler & O'Brien, 1993 ). In many pieces of research in psychology and psychophysics, interference between simultaneous perceptual tasks that perceive the same sensory modality has been consistently reported ( Wahn and König, 2017 ).

Spence et al. (2000) , found that when moving lips video that simulates distracting noise is displayed simultaneously, it is far more challenging to select the sound word stream that appears simultaneously as the second sound stream (distracting). Recent studies consistently showed that the problematic task influenced auditory stimulus detection in the visual scene. There are fewer attentional resources available to detect a brief auditory stimulus than when engaged in a less demanding visual object-based attention task ( Macdonald and Lavie, 2011 ). Hein et al. (2007) studies using functional magnetic resonance imaging (FMRI) have shown that even without a competitive motor response, simple auditory solutions can disrupt the nervous system's visual processing, including the prefrontal cortex, the medial temporal cortex, and other visual areas. In summary, these results mean that visual and auditory resource constraints act on the central processing level, not on the surrounding auditory and vision.

Most of the above studies involve dual-task conditions, both of which are brief stimuli that need to be identified or distinguished. Although this is a typical requirement for many daily activities (such as reading or driving), few people consider the conditions under which a specific pattern must be continuously followed within a few seconds to perform a task ( Arrighi et al., 2011 ).

Multitasking is vital for human life, and the brain cannot perform well when the attention is split. This study seeks to understand dual-task -between auditory and visual attention-in the current people by utilizing video, pure tones, and a quantitative methodology in the form of a survey of selected people. By understanding how auditory and visual attention are affected by each other, we can try to solve problems radically. According to the hypothesis of this study, multitasking situations affect auditory and visual attention.

2. Materials and methods

2.1. study group.

Hundred and eighty females participated in this study. The members of this sample ranged in age from 17 to 24 years (Mage = 21.07 years, standard deviation [SD] = ±1.35 years). All were divided into different groups randomized. The participants did not have any hearing loss and had a normal or corrected-to-normal vision. Before conducting the research, written informed consent was obtained from all participants. Participants who have hearing loss and who do not know and like 'Despacito song' were discarded. Written informed consent was obtained from all subjects who agreed to participate before entering the study. The tests were conducted at the Audiology Laboratory, Istanbul Medipol University. Ethical approval was obtained from Istanbul Medipol University Ethical Committee.

2.2. Stimuli

In order to investigate the result of the interaction of auditory and visual tasks, we used different experiments. For visual stimuli, “Monkey Business Illusion (MBI)” was chosen. MBI was a video recording designed by Simons (2010) . In this video, some events occur in the background while few girls throw a ball at each other. The participants were asked to count how many passes the ball each other in white t-shirts. When the girls play with a ball, a gorilla walking on the stage (Figures  1 a and 1b), one girl in black t-shirts left the stage (Figures  1 c and 1d), and the curtain color was changed (Figures  1 c and 1d). We introduced five pure tone stimuli in different parts of Luis Fonsi's “Despacito” song for auditory attention. The reason for this song selection was that it is one of the most listened songs on YouTube. Acoustically mixed frequencies to the song were in octave frequencies of pure tones in between 500-6000 Hz. 500-1000-2000-4000-6000 Hz frequencies were selected because speech frequencies in daily life are comprised in between these frequencies. Also, the noise rate in the song we used in the video was measured with the sound level meter (SLM) device, and the ratio between the signal and noise (S/N) was determined as an average of 10–12 dBA for each frequency (75–90 dBA). We performed these song-edited adjustments using TechSmith Camtasia 8.

Figure 1

Photos from Monkey Business Illusion(MBI) video.

2.3. Procedure

Initially, all participants were tested for pure tone thresholds (0.5–6.0 kHz). All the participants received verbal instructions for performing the research. The research was conducted in a lighted-up room. The distance between each subject and the computer screen was set at 65 cm.

Participants were given different stimuli.

  • • While fifty-two participants watched MBI, a modified “Despacito” song with pure sound was played. Fifty-two participants requested to count the number of passing the ball to each other wearing white t-shirts in MBI as a visual task. Participants were also required to press the stopwatch whenever they detect the pure tones in the song as an auditory task. Since all pure sounds in the song are known, it was determined by the stopwatch whether the people heard the pure tone.
  • • Only forty-one participants listened to a modified “Despacito” mixed with pure tones. These participants were required to do the same auditory task.
  • • Forty-two participants watched only MBI. These participants were requested to do the same visual task.
  • • While forty-eight participants were shown MBI, only the “Despacito” song was played. Forty-eight were requested to do the same visual task, but they also listened to the “Despacito” song.

After the test, we also asked all participants to all variables in the video ( Table 1 ). Each experiment was conducted over approximately 5 min.

Table 1

In this study, three different paradigms were used in this research ( Figure 2 ).

  • 1. For the first research, music without pure tone stimuli was listened to, and a group watched the video. The other group watched only the video. (90 participants)
  • 2. For the second research, the full stimulated group was watched the MBI and listened to pure tone stimuli inside the music, while the other group was watched the MBI and listened to music without pure tone stimuli. (100 participants)
  • 3. For the third research, the full stimulated group has watched a video and listened to pure tone stimuli in the music, while the other group has listened only to pure tone stimuli in the music. This group has not watched a video. (93 participants)

Figure 2

Schematic versions of researches setup.

2.4. Analysis

Statistical Package for the Social Sciences (SPSS, Version 26, IBM Corporation, Armonk, NY) was used for data analyses. One-sample Kolmogorov-Smirnov tested the normality of data. Descriptive statistics (percentages, means, and standard deviations) were calculated for all variables. The significance level was set up at p ≤ 0.05. Respectively, non-parametric variables were compared using Mann Whitney U and Chi-square as appropriate. Mann-Whitney U test was used to test whether the number of balls obtained from comparisons differs significantly. In examining the relationship between the two categorical variables, the Chi-Square test was used, and the results are given in tables.

3.1. First research

Participants who watched the video and listened to music without pure tone stimuli were compared with those who watched the only video. As indicated in Table 2 , there was no statistically significant difference in noticing the gorilla, the curtain color, and the girl leaving the stage (p > 0.05).

Table 2

Comparison of the group without pure tone stimuli and the group with only video group stimuli.

No statistically significant difference was observed in comparing the number of balls made between the group in which there were no pure tone stimuli and the Group of only video presentation (p = 0.799) ( Table 3 ).

Table 3

Number of detected balls comparison of the group without pure tone stimuli and the group with the only video.

3.2. Second research

As shown in Table 4 , in comparing the group with no pure tone stimuli, while watching the video with the Group with full stimulation setup, there was no statistically significant difference regarding noticing the gorilla passed, the curtain color changed, and the girl left the stage (p > 0.05).

Table 4

Comparison of the full stimulated group and the group without pure tone stimuli.

A statistically significant difference was observed in the comparison of the number of balls between the full stimulated group and with Group stimuli the group without pure tone stimuli (p < 0.05) ( Table 5 ).

Table 5

Comparison of the full stimulated groups with the number of detected balls without pure tone stimuli.

3.3. Third research

As shown in Table 6 , a statistically significant difference was observed in detecting 4000 Hz compared to the full stimulated group and the group with only non-video pure tone stimuli (p = 0.025). No statistically significant difference was observed in detecting pure tone stimuli at 500 Hz, 1000 Hz, 2000 Hz, and 6000 Hz (p > 0.05).

Table 6

Comparison of the full stimulated group and the group without video.

4. Discussion

Simultaneous multitasking in both experimental and everyday tasks is to divide and distribute sources of attention effectively. It is important to note that the only tasks we performed consist of multiple components or processes and include switching or resisting brain regions ( Rothbart and Posner, 2015 ). However, in a world abundant with sensory information, it is impossible to perceive everything around us at any time. Therefore, selective attention is an essential mechanism because it allows us to focus on relevant information and give low priority to irrelevant, potentially distracting information ( Awh et al., 2012 ; Chun et al., 2011 ; Dalton and Hughes, 2014 ).

This study has ensured that visual and auditory attention sources work simultaneously while performing dual tasks, particularly relevant for everyday functioning. Since the perceptual load will increase in dual-task conditions, it may decrease performance. The reason is that when two tasks rely on shared attention resources, compared to executing each task individually, performing both tasks at the same time will cause performance degradation. Our results confirm that performance decreases when tasks that require both visual and auditory attention are synchronized.

The first research in our study looked at the effect of music on the visual task. While counting the number of passing balls by women wearing white T-shirts as a visual task, it was also checked whether the participants could follow the events in the background. One group only played this process while the other group also listen to music at the same time. Accordingly, among the situations that require visual attention; there was no statistically significant difference concerning the number of gorilla getting on a stage (p = 0.257), curtain color change (p = 0.654), girl leaving the stage (p = 0.654), and passing the ball (p = 0.799) ( Table 2 and Table 3 ). All of the participants enjoyed the song given, but this did not affect attention. It has been shown that listening to the auditory stimuli containing grammatical information such as music with everyday words in the literature has shown that it may reduce inattentional blindness ( Beanland et al., 2011 ; Peretz and Coltheart, 2003 ). In another study, it was suggested that music increases the human emotional state and therefore increases the subject's attention ( Olivers and Nieuwenhuis, 2005 ). In our study, we preferred music with a high level of awareness. Because we wanted to observe that listening to music before would give an emotional reminder in the limbic system, although the music has a positive emotional effect, attention may not be disturbed because the cognitive load increases due to the verbal music input.

Music is a complex human skill that involves and integrates various cognitive resources in the processing period ( Medina and Barraza, 2019 ). Musical training is related to changes in the auditory cortex and the subtle difference in pitch between two notes ( Bermudez et al., 2009 ; Gaser & Schlaug, 2003 ; Schneider et al., 2002 ; Tervaniemi et al., 2005 ). Musical training can improve the understanding of some subtle features of musical auditory stimuli and simultaneously affect other complex auditory stimuli ( Medina and Barraza, 2019 ). The fact that the people who participated in our study did not have professional musical training may negatively affect terms of noticing the pure tones.

In our second research, the effect of the additional auditory task on visual perception was examined. While the same video was given as a visual task, participants were asked to notice the pure tone sounds mixed with the music as an auditory task. Both participants listen to music with no pure tones and listen to music with pure tones; they both noticed the events in the background in the video in a statistically similar way. However, as indicated in Table 5 the group without the auditory discrimination task in the passed ball counting process found results close to the actual pass ball count (16) (mean = 15.63 ± 1.003) and found to be significantly different from the group which the auditory discrimination task (mean = 13.02 ± 3.208) (p = 0.000). The results of this research may derive from increased perceptual load in multiple tasks. If the perceptual load is high and all current attention capacity is allocated only to a single task, there may be no more resources to process any additional stimuli. As a result, task-related extraneous auditory stimuli can affect performance at the primary task in low-load search, but not in the case of high load search ( Tellinghuisen et al., 2016 ).

As attentive resources are limited, the stimuli demanding extra attention for a perceptual task exceed system capacity. For example, in a visual search task where an object (target) must be found in an unrelated element (interference), the response time directly increases with the number of interferences ( Arrighi et al., 2011 ). This correlation reflects the limited ability of selective attention, which prevents the viewer from controlling all elements simultaneously ( Arrighi et al., 2011 ). Our study supports this issue. The primary aim of this study was to investigate the effectiveness of auditory input on decreasing visual attention performance, as determined by accuracy in a dual-task (visual-auditory) scenario. We hypothesize that auditory and visual attention affect each other. Overall, the findings provided partial support for our hypotheses. In a study by Arrighi et al. (2011) , participants performed multiple object tracking tasks and visual or auditory discrimination tasks in dual-task design. They found that the multiple object tracking task selectively disrupted the visual discrimination task without affecting the auditory discrimination performance.

Finally, the effect of visual tasks on auditory attention was examined. Here, an additional MBI video was given to this task of discerning pure tones in music. In general, while the participants heard the pure tones of 500 Hz, 2000 Hz, and 4000 Hz better in both groups, they were less successful in detecting 1000 Hz and 6000 Hz tones. At the same time, one of the reasons why the participants failed to detect 1000 Hz and 6000 Hz was thought to be due to the frequency spectrum of the song being chosen. Participants detected pure tones more when there was no visual task, but the difference was statistically significant only at 4000 Hz (p = 0.025 ∗) ( Table 6 ). This increase had shown that when visual attention was demanding besides auditory attention, the perceptual load will be negatively affected. The difference at 4000 Hz may be that this region of the cochlea is more sensitive. While the human cochlea is less sensitive to low frequencies, it is thought to be better heard due to its maximum sensitivity at 3–4 kHz ( Behrman, 2017 ; Fastl et al., 2007 ; Gopal, 1986 ).

Macdonald and Lavie hypothesized that people who are engaged with visually demanding object attention tasks have fewer attention resources available to recognize short auditory stimuli than people who are engaged with visually less demanding attention tasks. According to performing less visually challenging tasks, they found that participants detected significantly less auditory stimuli when performing visually challenging tasks, indicating that attention resources were allocated between sensory modalities ( Macdonald and Lavie, 2011 ). In another study, in several experiments, it has been consistently shown that the recognition of auditory stimuli is affected by the difficulty of the visual search task ( Raveh and Lavie, 2015 ). Our findings in the second and third research confirm the findings of these studies in terms of attention modalities.

On the other hand, according to Wahn and König (2017) , if attentional resources are distinct, performing two tasks at the same time will not cause performance degradation compared to performing two tasks separately. Significantly, these two tasks are performed in the same sensory modality or separate sensory modalities. We used separate modalities such as visual and auditory sensory modalities in this study. Allocating attention resources to low difficulty enables free attention resources to be allocated to another task that can be performed simultaneously. However, performing complex tasks have exhausted attentional resources, and it is not allowed to reallocate attention resources to another complex task ( Wahn and König, 2017 ). In this study, participants were asked to perform complex tasks simultaneously, such as counting the number of balls and noticing pure tones while watching a video. This situation may have prevented the fulfillment of every task.

Our results are significant in showing how attention affects performance in dual tasks. Increased perceptual load in dual tasks made it difficult to sustain attention. These results can provide important information about daily life. For example, our results confirm that people are less likely to notice auditory stimuli when performing tasks with high visual exposure. It is a skill that can occur over time for athletes to perform their responsibilities without being affected by the atmosphere in sports competitions. The test paradigm we used can be applied for training purposes in situations or professions that require multiple attention tasks. It can also be used for rehabilitation in children with hearing loss and attention distorts.


Author contribution statement.

Kerem Ersin and Oğulcan Gündoğdu: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Sultan Nur Kaya: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Dilşad Aykırı: Performed the experiments; Wrote the paper.

Mustafa Bülent Şerbetçioğlu: Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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