smart education essay

REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.  

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

• Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
• Survey the best available evidence on interventions that match those conditions—the evidence; and
• Closely monitor the results of innovations before they are scaled up—the prognosis.

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The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

• Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
• Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
• Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

• Scaling up quality instruction, such as through prerecorded quality lessons.
• Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
• Expanding opportunities to practice.
• Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas.  The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

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• Published: 31 March 2016

A research framework of smart education

• Zhi-Ting Zhu   ORCID: orcid.org/0000-0001-5540-5863 1 ,
• Ming-Hua Yu 2 &
• Peter Riezebos 2  

Smart Learning Environments volume  3 , Article number:  4 ( 2016 ) Cite this article

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The development of new technologies enables learners to learn more effectively, efficiently, flexibly and comfortably. Learners utilize smart devices to access digital resources through wireless network and to immerse in both personalized and seamless learning. Smart education, a concept that describes learning in digital age, has gained increased attention. This paper discusses the definition of smart education and presents a conceptual framework. A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21 st century learning. The smart pedagogy framework includes class-based differentiated instruction, group-based collaborative learning, individual-based personalized learning and mass-based generative learning. Furthermore, a technological architecture of smart education, which emphasizes the role of smart computing, is proposed. The tri-tier architecture and key functions are all presented. Finally, challenges of smart education are discussed.

Introduction

With the exponential technological advances, anything could be instrumented, interconnected, and infused with intelligent design, so is education. Smart education has gained significance attention in recent years. Educational projects focused on smart education have been performed globally in recent years (e.g. Chan 2002 ; Choi and Lee 2012 ; Hua 2012 ; IBM 2012 ; Kankaanranta and Mäkelä 2014 ). In 1997, Malaysia first carried out a smart education project, Malaysian Smart School Implementation Plan (Chan 2002 ). Smart schools, which are supported by government, aim to improve the educational system in order to achieve the National Philosophy of Education and to prepare work force that meets the challenges of the 21 st century. Singapore has implemented the Intelligent Nation (iN2015) Master plan since 2006, in which technology-supported education is an important part (Hua 2012 ). In the plan, eight Future Schools that focus on creating diverse learning environments are established. Australia collaborated with IBM and designed a smart, multi-disciplinary student-centric education system (IBM 2012 ). Their system links schools, tertiary institutions and workforce training. South Korea had the SMART education project, the major tasks of which are reforming the educational system and improving educational infrastructures (Choi and Lee 2012 ). New York’ Smart School program emphasizes the role of technology integrated into the classroom (New York Smart Schools Commission Report, 2014 ). They focus on enhancing student achievement and prepare students to participate in 21 st century economy. Finland also realized a smart education project that is on-going systemic learning solutions (SysTech) in 2011. The project aims at promoting 21 st century learning with user-driven and motivational learning solutions (Kankaanranta and Mäkelä 2014 ). United Arab Emirates (UAE) began to invest a smart learning program named Mohammed Bin Rashid Smart Learning Program (MBRSLP) in 2012, which aims to shape new learning environment and culture in their national schools through the launch of smart classes. Overall, the smart education focus and developments has become a new trend in the global educational field.

In the following sections, the related research topics of smart education development are reviewed; The concept of smart education and a conceptual framework for research are proposed; Also a research framework on smart education is depicted. Furthermore, the technological architecture of smart education is mentioned and the role of smart computing is depicted. Finally, the challenges of facilitating smart education are presented to inspire researchers and educators who are interested in smart education design and development.

Literature review

The evolution of smart learning.

As a new educational paradigm, smart learning bases its foundations on smart devices and intelligent technologies (Lee et al. 2014 ; Kim et al. 2011 ). As identified and heavily studied over the last decennia, technology can be implemented and utilized in helping learners learn. This is described as technology-enhanced learning (TEL). TEL is used to provide flexibility in the mode of learning. Technologies can be as media or tools for accessing learning content (Daniel 2012 ), inquiry, communication and collaboration, construction (Bruce and Levin 1997 ), expression (Goodman 2003 ), and evaluation (Meyer and Latham 2008 ) in TEL.

With the development of mobile, connected and personal technologies, mobile learning has become a major TEL paradigm. Mobile learning emphasizes the utilizing of mobile devices and focuses on the mobility of the learner, in contrast to the static traditional educational types. In addition to that, the supporting of ubiquitous technology has caused further changes that moving learning style away from the mobile learning toward to the ubiquitous learning which emphasizes learning can take place anytime and anywhere without the limitations of time, locations, or environments (Hwang et al. 2008 ).

Recently, many research begin to pay attention to the importance and necessity of authentic activities in which learners work with problems in the real world (Hwang et al. 2008 ). In order to situate students in authentic learning environments, it is important to design learning that combine both real and virtual learning environments. Seamless learning, which overlaps with some aspects of mobile learning and ubiquitous learning, is expounded as an one-to-one TEL model which learners can learn across time and locations, and they can convert the learning from one scenario to another conveniently encompassing formal and informal learning, individual and social learning through the smart personal device (Chan et al. 2006 ).

Also other intelligent technologies, such as cloud computing, learning analytics, big data, Internet of things (IoT), wearable technology and etc., promote the emergence of smart education. Cloud computing, learning analytics and big data, which focus on how learning data can be captured, analyzed and directed towards improving learning and teaching, support the development of the personalized and adaptive learning (Lias and Elias 2011 ; Mayer-Schönberger and Cukier 2013 ; Picciano 2012 ). With these adaptive learning technologies, learning platform reacts to individual learner data and adapts instructional resource accordingly based on cloud computing and learning analytics, and it can leverage aggregated data across mass learners for insights into the design and adaptation of curricula based on big data (NMC 2015 ).

In addition, the IoT and wearable technology support the development of contextual learning and seamless learning. The IoT can connect people, objects and devices. Learners carrying smart devices can benefit from various related information that is pushed to them from their surroundings (NMC 2015 ). Wearable technology can integrate the location information, exercise log, social media interaction and visual reality tools into the learning.

The concept of smart learning

There is no clear and unified definition of smart learning so far. Multidisciplinary researchers and educational professionals are continuously discussing the concept of smart learning. Still, some crucial components have been discussed in literature. Hwang ( 2014 ) and Scott & Benlamri ( 2010 ) consider that smart learning is context-aware ubiquitous learning. Gwak ( 2010 ) proposed a concept of smart learning as follows: first, it is focused on learners and content more than on devices; second, it is effective, intelligent, tailored learning based on advanced IT infrastructure. The technology plays an important role supporting smart learning, but the focus should not just on the utilization of smart devices. Kim et al. ( 2013 ) considered that smart learning, which combines the advantages of social learning and ubiquitous leaning, is learner-centric and service-oriented educational paradigm, rather than one just focused on utilizing devices. Middleton ( 2015 ) also stipulates on the learner-centric aspects of smart learning and how it benefits from the use of smart technologies. The personal and smart technologies make learners engaging in their learning and increase their independence in more open, connected and augmented ways by personally richer contexts.

Also, others attempt to indicate the features of smart leaning. MEST ( 2011 ) presented the features of smart learning that is defined as self-directed, motivated, adaptive, resource-enriched, and technology-embedded. Lee et al. ( 2014 ) proposed that the features of smart learning include formal and informal learning, social and collaborative learning, personalized and situated learning, and application and content focus.

• Smart learning environments

Generally, smart learning environment is effective, efficient and engaging (Merrill 2013 ). The learner is always considered as the heart of smart learning environment. And the goal of smart learning environment is to provide self-learning, self-motivated and personalized services which learners can attend courses at their own pace and are able to access the personalized learning content according to their personal difference (Kim et al. 2013 ). Koper ( 2014 ) proposed that smart learning environments are defined as physical environments that are enriched with digital, context-aware and adaptive devices, to promote better and faster learning. Hwang ( 2014 ) specified that the potential criteria of a smart learning environment include context-aware, able to offer instant and adaptive support to learners, and able to adapt the learner interface and subject contents. Smart learning environment not only enables learners to access ubiquitous resources and interact with learning systems anytime and anywhere, but also provides the necessary learning guidance, suggestions or supportive tools to them in the right form, at the right time and in the right place.

Learning can take place anytime and anywhere via the utilization of smart devices. The context-aware aspect plays an important role in smart learning environments that can support to provide proper learning service to learners. Kim et al. ( 2011 ) designed a smart learning environment based on cloud computing. The smart learning service provides context-awareness supporting push smart learning content to learners through collecting and analyzing their behaviors. It aims to provide personalized and customized learning services to learners. Scott and Benlamri ( 2010 ) built a smart learning environment, which is learner-centric and service-based, based on semantic web and ubiquitous computing. The learning environment is composed by ubiquitous collaborative learning spaces, which transform traditional learning spaces into intelligent ambient learning environments through context awareness and real-time learning services. Huang et al. ( 2012 ) considered a smart learning environment is high-level digital environment that realizes learning context awareness, recognizes learner’s characteristic, provides adaptive learning resources and convenient interactive tools, records learning process automatically and evaluates learning outcomes. Its goal is to support easy, engaged and effective learning for learners.

Based on interactive resources and services, smart learning environment is learner-initiated and collaborative (Noh et al. 2011 ). Spector ( 2014 ) considered that smart learning environment supports planning and innovative alternatives for learners and instructors, and should be effectiveness, efficiency, engagement, flexibility, adaptivity, and reflectiveness. And these features might include support for collaboration, struggling learners and motivation.

Through reviewing these literatures, we can find that smart learning environment emphasizes learner-centric, personalized and adaptive learning service, interactive and collaborative tools, context-aware and ubiquitous access. And smart learning environment aims to support to realize the effective, efficient and meaningful learning for learners.

The meaning of smart in smart education

Globally many countries have participated in projects focused on smart education. Malaysian smart schools aim to help their country to foster the workforce of 21 st century by utilizing and enabling the leading-edge technologies into schools. And the smart schools not only focus on stimulating thinking, creativity, and caring for the students, but also considering the individual differences and learning styles among their learners. The smart education in Singapore also emphasizes the role of technology. Their goal is to foster engaging learning experience to meet the diverse needs of learners, through the innovative use of information and communications technology (Education and Learning Sub-Committee, 2007 ). In order to realize this, Singapore created an enriching and personalized learner-centric environment, and additionally created a nation-wide education and learning architecture for educational institutions and life-long learning. Korea carried out the smart education project to provide the customized and adaptive learning for students to foster self-directed learning ability and have fun to use various resources and technology. Individualized instruction and creativity-centered education is considered as the main keyword of smart education. Australia aims to build a smart, multi-disciplinary student-centric education system using the following strategies: adaptive learning programs and learning portfolios for students, collaborative technologies and digital learning resources for teachers and students, computerized administration, monitoring and reporting, and online learning resources. New York proposed the keys for achieving Smart School as following: embracing and expanding online learning, utilizing transformative technologies, connecting every school using high-speed network, extending connectivity between inside and outside of the classroom, providing high-quality, continuous professional development, and focusing on foster 21 st century skills (New York Smart Schools Commission Report 2014 ). Finnish smart education aims at using user-driven and motivational learning solutions to promote 21 st century learning (Kankaanranta and Mäkelä 2014 ). They proposed a pedagogical network of educational institutions called “value network” that is the central of program. It has five categories as following: to understand user experience and usability, to receive expert feedback, to indicate learning outcomes, effects and quality of learning, to develop skills and expertise (Mäkelä et al. 2014 ). United Arab Emirates (UAE) aims to advance their education system to student-centric through the application of world-class teaching science and latest technology. They encourage learner to develop creativity, analytic thinking and innovation. Their approach encompasses learning both inside and outside the classroom. The students can control and active participant into their own learning process in interactive, engaging and enabling learning environments.

Through analyzing these smart education projects, we can find some generalities as follows. The goal of smart education is to foster workforce that masters 21 st century knowledge and skills to meet the need and challenge of society. Intelligence technology plays an important role in the construction of smart educational environments. In smart educational environments, learning can happen anytime and anywhere. It encompasses various learning styles, such as formal and informal learning, personal and social learning, and aims to realize the continuity of learning experience for learner. In this learners are provided with personalized learning services as well as adaptive content, and according to their (learning) context and their personal abilities and needs. So generally, ‘smart’ in smart education refers to intelligent, personalized and adaptive. But for different entities and/or educational situations, the meaning of ‘smart’ has different definitions.

For learner, ‘smart’ refers to wisdom and intelligence. Wisdom is defined as the ability to use your knowledge and experience to make good decisions and judgments. According to Confucius who is the most famous educator of China, wisdom can be achieved by three methods: reflection (the noblest), imitation (the easiest) and experience (the bitterest). In addition, the intelligence is the ability to solve problems that are valuable in one or more cultural settings (Gardner 2011 ). According to the concepts of wisdom and intelligence, we comprehend that smart for learner means an ability enabling people to think quickly and cleverly in different situations.

For educational technology, ‘smart’ refers to accomplish its purpose effectively and efficiently (Spector 2014 ). The technology includes the hardware and software. For hardware, ‘smart’ refers to the smart device much smaller, more portable and affordable. It is effective to support learner take place the learning anytime and anywhere with smart devices. And some hardware (e.g., smartphones, laptop, Google glass, etc.) has functions to recognize and collect the learning data to engage the learner into contextual and seamless learning. For software, ‘smart’ refers to adaptive and flexible. It is efficient to carry out personalized learning for learner according to their personal difference, with adaptive learning technologies (e.g. cloud computing, big data, learning analytics, adaptive engine, and etc.).

For educational environment, ‘smart’ refers to engaging, intelligent and scalable. Smart educational environment can provide tailored and personalized learning service (e.g. context awareness, adaptive content, collaborative and interactive tool, rapid evaluation and real-time feedback, etc.) to engage the learner into effective, efficient and meaningful learning. And the open system architecture is required to better support the integration of increasing interfaces, smart devices and different learning data.

Research framework of smart education

Based on the generalities of different countries’ smart education and the meaning of smart, the concept of smart education is proposed. Zhu and He ( 2012 ) stated that “the essence of smart education is to create intelligent environments by using smart technologies, so that smart pedagogies can be facilitated as to provide personalized learning services and empower learners, and thus talents of wisdom who have better value orientation, higher thinking quality, and stronger conduct ability could be fostered “(p. 6).

And based on this definition of smart education, a research framework is proposed in Fig.  1 . This framework describes three essential elements in smart education: smart environments, smart pedagogy, and smart learner. Smart education emphasizes the ideology for pursuing better education and thus had better to be renamed as smarter education, which address the needs for smart pedagogies as a methodological issue and smart learning environments as technological issue, and advances the educational goals to cultivate smart learners as results. Smart environments could be significant influenced by smart pedagogy. Smart pedagogies and smart environments support the development of smart learners.

• Smart learners

Learning is conventionally defined as the process of acquiring competence and understanding. It results in a new ability to do something, and an understanding of something that was previously not understood. Competence is sometimes described in terms of possessing specific skills, understanding in terms of possessing specific knowledge.

The 21 st century demand skills and competence from people in order to function and live effectively at work and leisure time. Education needs to prepare workforce for the demand. So the goal of smart education is to foster smart learners to meet the needs of the work and life in the 21 st century.

There are many organizations developing the 21 st century skills independently. The Organization for Economic Co-operation and Development (OECD) have organized ten 21 st century skills into four categories which include ways of thinking, tools for working, ways of working and ways of living in the world (Ananiadou and Claro 2009 ). Partnership for 21 st century (P21 2015) skills proposed a framework for the 21 st century learning and indicated that the 21 st century student should master these knowledge and skills as follows: key subjects and 21 st century themes; learning and innovation skills; information, media and technology skills; life and career skills. North Central Regional Educational Laboratory (NCREL) proposed that digital-age literacy, inventive thinking, effective communication and high productivity compose the 21 st century skills (Burkhardt et al. 2003 ).

Based on these researches, we propose four level of abilities in smart education that students should master to meet the needs of the modern society. These abilities are basic knowledge and core skills, comprehensive abilities, personalized expertise and collective intelligence. These are grouped under knowledge, skills, attitudes, and values. The four levels of abilities are presenting in detail as following.

Basic knowledge and core skills. Basic knowledge and core skills referring to knowledge and skills in core subjects such as STEM, reading, writing, art and etc. Mastery of these core subjects is essential to students’ success (P21 2015). Jenkins ( 2009 ) also considered that the reading, writing and mathematics are core capabilities for 21 st century.

Comprehensive abilities. Comprehensive abilities refer to abilities to critical think and solve real-world problem. Most of the 21 st century skills frameworks raise the demands of thinking ways for people (Ananiadou and Claro 2009 ; Burkhardt et al. 2003 ; P21 2015). These abilities let student use appropriate reasoning and comprehensive thinking in different complex situations. Based on analyzing and making judgments and decisions, students should solve different problems and produce better solutions.

Personalized expertise. This level ability demands the students to master information and technology literacy, creativity and innovation skills. Information and technology literacy demands students master ICT skills that include using different ITC applications and combining cognitive abilities or higher-order thinking skills for learning (Ananiadou and Claro 2009 ). Creativity and innovation skills demand students to think and work creatively with others, and can act creative ideas to make contributions to the field in which the innovation will occur.

Collective intelligence. The ways of working are important which need communication and collaboration. Collective intelligence refers to knowledge that built up by a group of people via communication and collaboration. After the previous work with information and knowledge, students need to reflect about the ways to share and transmit the results or outputs to other people (Ananiadou and Claro 2009 ). So students need to communicate clearly and effectively in various ways. Also collaboration demand students work effectively and respectfully in diverse teams (p21 2015).

Smart pedagogies

With the rapid development of technologies, increasingly flexible and efficient learning methods for students are developed. Research in cognitive science has indicated that knowledge and skills are closely intertwined (Scardamalia and Bereiter 2006 ). It needs mixing content knowledge and process skills to produce understanding which learners need. Then learners execute their understanding in practice to produce their performances. The critical thinking and learning skills are very important, but these skills cannot be taught independently and some appropriate factual knowledge need to be taught in particular domain and context (Ananiadou and Claro 2009 ). Using the deliberate instructional or learning strategies can be related to cultivate the knowledge and skills for learners. So in order to fostering different abilities of smart learners, we searched the literatures about related pedagogies or learning strategies using conventional subject searching method in some databases. Through analyzing the literatures, we summarized and adopted relevant practical methods.

Students usually accept basic knowledge and core skills in the classroom. Learning goal and process always are the same for each student in traditional classroom. But students with different backgrounds have different needs. Every student deserves a strict education matched with content and performance standards that promote the understanding (Tomlinson and McTighe 2006 ). The classroom should be differentiated and responsive to vary learners’ readiness levels, interests and learning profiles (Tomlinson and Kalbfleisch 1998 ). Differentiated instruction emphasizes the different needs of each individual student and cultivates the basic knowledge and core skills for students.

Whether learning happens in the classroom or online, students who have different performances often need to learn together in-group or team to fulfill common task or achieve common goal. In collaborative process, learners can be fostered comprehensive abilities including critical thinking and solve problem ability (Gokhale 1995 ; Stahl 2006 ). Students in cooperative teams can keep knowledge longer through sharing information and engage in discussion at higher levels of thought to take responsibility for their own learning (Totten et al. 1991 ).

Learning processes should be tailored according to the students’ learning needs that include requirements, background, interests, preferences, etc (Sampson et al. 2002 ). In particular, personal interest is more important than external motivation because it is driven by students’ own passion (Malone 1981 ). Interest-driven personalized learning emphasizes the interests of students and can fosters intrinsic motivations, and then promote the personalized expertise for students (Atkins et al. 2010 ).

Intelligence is an ability to get things done that matter. Sternberg ( 1999 ) describes the three basic aspects of successful intelligence that include analytical thinking, creative thinking and practical application. As mentioned before, we facilitate abilities including problem solving, decision making, creative thinking and interest-driven learning for learners. We need to integrate these abilities to generate intelligence. It is similar to the transfer of learning, or something in which we have been learned in specific situations that are intentionally applied in other different related conditions (Barnett and Ceci 2002 ). Learning is a generative process. In such a process, the learner is an active recipient of information who works to construct meaningful understanding of information found in the environment (Wittrock 1974 ). Generative learning can enable learners to flexible apply the intelligence what they have learned and generated to various relevant future situations (Engle 2006 ; Fiorella and Mayer 2015 ).

So, in order to foster the learners’ performances, we propose four instructional strategies as demonstrated in Fig.  2 . These strategies include class-based differentiated instruction, group-based collaborative learning, individual-based personalized learning (interest-driven predominantly) and mass-based generative learning (through online interactions predominantly). All these strategies encompass formal and informal learning, in both the real and the digital world. The four levels of smart strategies are presented in detail as following.

Four-tier architecture of smart pedagogies

Class-based differentiated instruction. Differentiated instruction is a process to approach teaching and learning for students with different abilities in the same class (Hall 2002 ). And it can coexist with standard-based education (McTighe and Brown 2005 ). The classroom is considered as a community that the students are treated as individual learners (Lawrence-Brown 2004 ). Teachers set different levels of expectations for learning task completion within a lesson or unit through differentiated instruction (Waldron and McLeskey 2001 ). Under differentiated instruction, all the students have tailored learning preferences and learn effectively.

Group-based collaborative learning. Collaborative learning is a situation that two or more people learn or attempt to learn something together (Dillenbourg 1999 ). Teachers design the collaborative learning process to make meaningful learning experiences and promote students’ thinking through solving real world problem. With the development of technology, computer-supported collaborative learning (CSCL) has emerged using computer and information technology to improve learning (Stahl et al. 2006 ). Koschmann ( 2002 ) presents that CSCL is “a field of study centrally concerned with meaning and the practices of meaning making in the context of joint activity, and the ways in which these practices are mediated through designed artifacts“. (p. 18) CSCL can engage students in joint problem solving by designing software to support meaning making, focus on the students’ methods of problematization, and promote intersubjective meaning making when students learn in small groups (Stahl 2006 ).

Individual-based personalized learning. Personalized learning is defined as adjusting pace (individualization), adjusting approach (differentiation) and connecting to the learners’ interests and experiences (Atkins et al. 2010 ) to meet the student’s needs and provide supporting to foster learning ability among individual students (Bentley and Miller 2004 ). In the personalized learning process, students achieve goals or explore interests based on their motivation. But it is not enough, the essential of personalized is that content is flexible to meet the interests of particular students. When students interact with the personal learning environments, their information and technology literacy will be enhanced. They can be engaged in learning activities and their creativity can be inspired in the learning process (Järvelä 2006 ). There are four key issues to enabling personalized learning through information technologies as follows: make informed learning decisions by students, develop and diversify different knowledge and skills, create various learning environments, and focus the evaluation and feedback from students (Green et al. 2005 ).

Mass-based generative learning. The fundamental concept of generative learning involves the creation and refinement of personal mental constructions about the environments (Ritchie and Volkl 2000 ). Engle ( 2006 ) proposed a theoretical framework for generative learning that combines content and context analysis. The goal is to let students participate in the construction of the transferred content and to frame the learning and transfer contexts to create intercontextuality. When students are learning online, they are able to link new information to old, acquire meaningful knowledge and use their metacognitive abilities (Bonk and Reynolds 1997). These activities can promote the students to active participate in constructing relevant content. Also online learning allows students to collapse time and space limitation (Cole 2000 ). It has high interactivity, collaboration and authenticity. These features can support to frame time and participation to create intercontextuality. Then in the leaning process, the abilities especially the communication and collaboration should be facilitated generate for students.

The traditional learning paradigm has been criticized for being too artificial, rigid and unresponsive to the needs of today’s society (Kinshuk and Graf 2012 ). With the development of new technologies and the emergent of new pedagogies in digital age, the use of technologies to facilitate learning and engage learners has become a universal phenomenon. Piccoli et al. ( 2001 ) define and expand the dimensions of learning environments, which include space, place, time, technology, control and interaction. So it is possible to design new learning environments, both technically and pedagogically.

From the technical perspective, ambient intelligence (AmI) is growing rapidly as a new research paradigm recently (Shadbolt 2003 ). In AmI environments, devices support people in executing their daily life activities and tasks in an easy and natural way by using intelligence and information from the network. Devices can interact and communicate independently without coordination with people and make decisions based on a series of factors, including people’s preferences and other people’s presence in the neighborhood (Preuveneers et al. 2004 ). Most students today are digital natives, who have been immersed in the use of smart mobile devices and digital resources for communications, learning, and entertainment in everyday life (Bennett et al. 2008 ).

From the pedagogical perspective, learning analytics as underlying methods enables institutions to support learners making progress and to enable rich and personalized learning (Siemens and Long 2011 ). The general goal of learning analytics is to monitor the learning process and then use the data analysis to predict the future performance of students as well as to find their potential problems (Siemens 2013 ; Zhu and Shen 2013 ). It is possible for teachers to offer informative feedbacks to learners through virtualized learning dashboards via learning analytics. It is beneficial to have a general view of the learners’ activities and how these are related to their peers or other actors in the learning experience through visualizations for learners and teachers (Duval 2011 ).

Smart learning environments supported by technologies should not only enable learners to digital resources and interact with the learning systems in any place and at any time, but also actively provides them with the necessary learning guidance, supportive tools or learning suggestions in the right place, at the right time, and in the right form (Hwang 2014 ). There are many different types of technologies used to support and enhance learning, which include both hardware and software. Hardware include those tangible objects such as interactive whiteboard, smart table, e-bag, mobile phone, wearable device, smart device, sensors which using ubiquitous computing, cloud computing, ambient intelligence, IoT technology, etc. Software include all kinds of learning systems, learning tools, online resources, educational games which using social networking, learning analytics, visualization, virtual reality, etc.

Based on the support of various technologies, we consider that the goal of smart learning environments is to provide rich, personalized and seamless learning experience for learners. To provide seamless learning experience, smart environments can encompass formal and informal learning. To realize personalized learning experience, smart learning environments can provide accurate and rich learning services by using learning analytics. So based on smart education demand, we propose ten key features of smart learning environments as following.

Location-Aware: Sense learner’s location in real time;

Context-Aware: Explore different scenarios and information of activity;

Socially Aware: Sense social relationship;

Interoperability: Set standard between different resource, service and platform;

Seamless Connection: Provide continuous service when any device connects;

Adaptability: Push learning resource according to learning access, preference and demand;

Ubiquitous: Predict learner’s demand until express clearly, provide visual and transparent way to access learning resource and service to learner;

Whole Record: Record learning path data to mine and analyze deeply, then give reasonable assessment, suggestion and push on-demand service;

Natural Interaction: Transfer the senses of multimodal interaction including position and facial expression recognition;

High Engagement: Immersing in multidirectional interaction learning experience in technology-riched environment.

Technological architecture of smart education environments

Smart computing is the latest cycle of tech innovation and growth that began in 2008 (Bartels 2009 ) and an important technology in smart learning environments. It blends elements of hardware, software and networks together with digital sensors, smart devices, Internet technologies, big data analytics, computational intelligence and intelligent systems to realize various innovative applications. All these technologies can effectively support learning to happen in different situations. Above all, the advancement of computing technologies leads smart computing to a new dimension and improves the ways of learning.

In section 3, we proposed ten key features of smart learning environments in smart education. All these features make learning environments smarter. To better understand the technological architecture to support the key features, we present a technological architecture of smart education environments based on smart computing.

Tri-tier architecture of smart computing

Today’s world is moving fast towards an era of seamless networks as mobile devices are becoming smaller, smarter and more affordable. Ubiquity of such devices is an essential element for location based services and learning data transmission. Also computing is rapidly moving away from traditional devices. The tri-tier architecture of smart learning environments is essential which includes cloud computing, fog computing and swarm computing. In this tri-tier architecture, the cloud, fog and swarm are companions. Learning applications may have components running in the cloud, fog and swarm. The cloud and fog may help control and manage the resources of the swarm. Learning contents can move and be analyzed across this tri-tier architecture.

Cloud computing. The innermost layer is the cloud computing, which provides software as a service. It deploys groups of remote servers and software networks that allow centralized data storage and online access to computer services and resources. In smart learning environments, we need method to rationalize the way managing the resources. It is the infrastructure of smart learning environments and provides the platform, virtualization, centralized data storage, and educational services in education. Using cloud computing, the smart learning environments can realize smart pull, smart prospect, smart content, and smart push (Kim et al. 2011 ).

Fog computing. The middle of the tri-tier architecture is the fog computing. Nowadays in IoTs, literally anything can part of it, so very diverse types of services can be produced. This requires much better infrastructure and sophisticated mechanism. This technology is a highly virtualized platform that provides compute, storage, and networking services between end devices and traditional cloud computing data centers, typically, but not exclusively located at the edge of network (Bonomi et al. 2012 ). Through the features of fog computing, smart learning environments can realize real-time interaction, location-awareness, large-scale sensor networks, supporting for mobility and so on.

Swarm computing. The outermost layer is the swarm computing. As the computing technology continues to become increasingly pervasive and ubiquitous, we envision development of environments that can sense what we are doing and support our daily activities (Essa 2000 ). Swarm computing, is also called environment-aware computing, can execute on swarms of smart devices and the networks of sensors due to ubiquitous sensing. And these sensors’ data will transfer to data management systems to analysis.

Key functions of smart computing

In addition, smart computing allows computing technologies smarter because of five key functions of intelligence that are awareness, analysis, alternatives, actions and auditability (Bartels 2009 ). In the tri-tier architecture, the swarm computing support awareness, the fog computing support analysis, alternatives, and the cloud computing support actions and auditability. When smart computing is used in building smart learning environments or systems, it is able to support every stage of intelligent activities.

Awareness. Learning happens anywhere and anytime. We can use technologies such as swarm computing, pattern recognition, data mining, learning analytics, and other tools, capture data on students’ identity, status, condition, and location. Networks can transport this data from learner devices back to smart learning systems central servers for analysis.

Analysis. When system servers receive real-time data from learner devices, intelligence and analytical tools such as learning analytics, data mining, and big data, are used to analyze and store the learning data, and then recommend learning patterns and resources to learners.

Alternatives. Using learning flow or workflow engines, it is able to identify either automatically or through human review alternative courses of action in response to the learning patterns. Once a decision is made, it will trigger the learning action.

Actions. Using integrated links, systems can execute actions to the appropriate process applications. These process apps can be adapted to various scenarios, with specific app components pushed down to our smart devices where we can execute action, that learner receive related learning resource in the museum or acquire location information outside.

Auditability. Whether right learning action was actually taken can only be determined under the auditability. In smart education, it is important to monitor learning process and to make it more efficient. Smart learning systems need to capture, track, and analyze data of learning activities at each stage for purposes of learning evaluation and improvement.

The implementation strategies for research

The first author and his East China Normal University (ECNU) team have engaged in substantial research and development relating to smart education, only a part of efforts is mentioned here:

Developing standards for e-Textbook and e-Schoolbag. Under the leadership of the ECNU team, delegations from sixty ICT companies participate in the development of a set of standards (17 projects) since November of 2010.

Conducting pilot of using e-schoolbag. The ECNU team is invited by Minhang district to design application models of using e-schoolbags in 67 schools since 2012, about seven thousands of students are involved.

Undertaking national research project. The first author undertakes a national project on THE BUILDING OF SMART LEARNING ENVIRONMENTS AND APPLICATIONS since 2014, which is from the 12th Five Year Research Program in Educational Sciences. 300 schools from a decade of provinces over the country are planned to join the project. The ECNU team gives theoretical guidance, teacher trainings, and application assessments. It is expected that this project will help to test different architectural model of smart learning environments and to tryout the smart learning pedagogy as above-mentioned.

Case studies based on the research framework

Based on the research framework of smart education, we began to carry out some pilot studies. Here introduces two case studies of them. One is flipped classroom project that integrated smart pedagogies and constructed smart learning environments for students. Another pilot project is called “Online J classroom” that also integrated smart pedagogies into learning process to realize precision teaching.

The flipped classroom pilot project is carried out in a middle school of north China. The core idea of flipped classroom is to flip the common instructional approach (Tucker 2012 ). Instruction, which used to happen in class, is now occurred at home with teacher-created videos and interactive lessons. The project aims at fostering self-regulated and collaborative learning abilities for students. At first, there are four classes participating into the project, and then it is extended to all the classes of the school. A process model of flipped classroom based on the idea of smart pedagogies is proposed that includes two phases that include self-regulated questioning and practice showing. Self-regulated questioning phase is consisted of learning objective guiding, textbook self-regulated learning, micro-lecture assisted learning, cooperative learning, and online assessment. Practice showing phase is consisted of difficult breakthrough, practice showing, cooperative improving, evaluated guiding, and summarize reflection. Every student uses the tablet PC to support learning. Through analyzing the questionnaires and interviews data, we found that students’ learning statement, learning capacity and problem consciousness have significantly enhanced. To teachers, they began to more focus on students’ personal learning, and their professional competence has been significantly improved. To school, the overall teaching level has obviously raised.

Online J-Classroom is a district-based project that aims at providing micro-videos in pre-learning process for students. A data-driven instructional decision model is proposed for designing precision teaching interventions. Precision teaching is the educational decisions based on changes in continuous self-recording performance frequencies using the standard celeration charts (Lindsley 1992 ). The online J-Classroom platform has three major functions including resources co-building and sharing, data recording and analyzing, cooperating and innovating between teachers and students. The latest platform version is delivered in October 2015. Through monitoring and analyzing the data of learning process, platform can provide personalized instructional design including direct teaching based on problem, problem solving oriented cooperative inquiry, and task-driven self-regulated learning for students. Students can be ensured to master all the knowledge after pre-learning as well as their self-regulated learning ability should be enhanced.

Conclusion: challenges of facilitating smart education

As stated, smart education is a new paradigm in global education. The objective of smart education is to improve learner’s quality of life long learning. It focuses on contextual, personalized and seamless learning to promote learners’ intelligence emerging and facilitate their problem-solving ability in smart environments. With the development of technologies and within a modern society, smart education will confront many challenges, such as pedagogical theory, educational technology leadership, teachers’ learning leadership, educational structures and educational ideology.

In our expectation on smart education, the smart learning environments could decrease learners’ cognitive load, and thus enable learners to focus on sense making and facilitate ontology construction. Also students’ learning experience could be deepened and extended, and thus help students’ development in an all-round way (affectively, intellectually, and physically). Students can learn flexibly and working collaboratively in smart learning environments, and thus could foster the development of personal and collective intelligence of learners. Furthermore, better customize learning support could be provided for students to improve learners’ expectation.

As the concept of smart city has been paid more attention (Hollands 2008 ), the requirements of smart education based on smart city are promoted. The overall goal of smart education under smart city architecture is to provide every citizen personalized services and seamless learning experience. Learning happens in anywhere and anytime and produce lots of behavioral data of learners. How to integrate the data of different scenarios in smart cities and build data-centric smart education is a big challenge to educators in order to provide seamless learning experience and customized personalized service for learners. The interconnected and interoperable learning service and experience between smart education system and other systems of smart city are the future research focus.

K. Ananiadou, M. Claro, 21st century skills and competences for new millennium learners in OECD countries . OECD Education Working Papers, vol. 41, 2009

Book   Google Scholar  

D. E. Atkins, J. Bennett, J. S. Brown, A. Chopra, C. Dede, B. Fishman, B. Williams, Transforming American education: Learning powered by technology. Learning. 114 (2010)

S.M. Barnett, S.J. Ceci, When and where do we apply what we learn?: A taxonomy for far transfer. Psychol. Bull. 128 (4), 612 (2002)

Article   Google Scholar  

A. H. Bartels, Smart computing drives the new era of IT growth. Forrester Inc. (2009)

S. Bennett, K. Maton, L. Kervin, The ‘digital natives’ debate: a critical review of the evidence. Br. J. Educ. Technol. 39 (5), 775–786 (2008)

T. Bentley, R. Miller, Personalised Learning: Creating the Ingredients for Systemic and Society-wide Change (IARTV, Jolimont, 2004)

Google Scholar  

F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in Proceedings of the first edition of the MCC workshop on Mobile cloud computing, Helsinki, Finland , 2012

B.C. Bruce, J.A. Levin, Educational technology: media for inquiry, communication, construction, and expression. J. Educ. Comput. Res. 17 (1), 79–102 (1997)

G. Burkhardt, M. Monsour, G. Valdez, C. Gunn, M. Dawson, C. Lemke, enGauge 21st century skills: Literacy in the digital age (North Central Regional Educational Laboratory, Naperville, 2003)

F.M. Chan, ICT in Malaysian schools: Policy and strategies. ICT in Education , 2002, pp. 15–22

T.W. Chan, J. Roschelle, S. Hsi, M. Sharples, T. Brown, C. Patton et al., One-to-one technology-enhanced learning: an opportunity for global research collaboration. Res. Pract. Technol. Enhanc. Learn. 1 (01), 3–29 (2006)

J. W. Choi, Y. J. Lee, The Status of SMART Education in KOREA. World Conference on Educational Multimedia, Hypermedia and Telecommunications. 2012(1): 175-178(2012)

R.A. Cole, Issues in Web-based pedagogy: A critical primer (Greenwood Press, Westport, 2000)

J. Daniel, Making sense of MOOCs: musings in a maze of myth, paradox and possibility. J. Interact. Media Educ. 3 , Art-18 (2012)

P. Dillenbourg, What do you mean by collaborative learning? Collaborative-learning: Cognitive and Computational Approaches , 1999, pp. 1–19

E. Duval, Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, AB, Canada, 27 Februry-01 March 2011

Education and Learning Sub-Committee, Empowering learners and engaging minds, through infocomm (Ministry of Education, Singapore, 2007)

T. Elias, Learning Analytics: The Definitions, the Processes, and the Potential , 2011

R.A. Engle, Framing interactions to foster generative learning: a situative explanation of transfer in a community of learners classroom. J. Learn. Sci. 15 (4), 451–498 (2006)

IA Essa, Ubiquitous sensing for smart and aware environments. Personal Communications, IEEE 7 (5), 47–49 (2000)

L. Fiorella, R.E. Mayer, Eight Ways to Promote Generative Learning. Educational Psychology Review , 2015, pp. 1–25

H. Gardner, Frames of mind: The theory of multiple intelligences (Basic books, New York, 2011)

A. A. Gokhale, Collaborative learning enhances critical thinking. J Technol Educ. 7(01) (1995)

S. Goodman, Teaching youth media: A critical guide to literacy, video production and social change (Teachers College, New York, 2003)

H. Green, K. Facer, T. Rudd, P. Dillon, P. Humphreys, Futurelab: Personalisation and digital technologies , 2005

D. Gwak, The meaning and predict of Smart Learning, Smart Learning Korea Proceeding, Korean e-Learning Industry Association , 2010

T. Hall, Differentiated instruction (National Center on, Wakefield, 2002)

R.G. Hollands, Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City 12 (3), 303–320 (2008)

Article   MathSciNet   Google Scholar  

M.T.A. Hua, Promises and threats: iN2015 Masterplan to pervasive computing in Singapore. Sci. Technol. Soc 17 (1), 37–56 (2012)

R. Huang, J. Yang, Y. Hu, From digital to smart: the evolution and trends of learning environment. Open Educ. Res. 1 , 75–84 (2012)

G.J. Hwang, Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments 1 (1), 1–14 (2014)

G.J. Hwang, C.C. Tsai, S.J.H. Yang, Criteria, strategies and research issues of context-aware ubiquitous learning. J. Educ. Technol. Soc 11 (2), 81–91 (2008)

IBM, Smart Education (2012), https://www.ibm.com/smarterplanet/global/files/au__en_uk__cities__ibm_smarter_education_now.pdf . Accessed 20 Mar 2015

S. Järvelä, Personalised learning? New insights into fostering learning capacity. Personalising education , 2006, pp. 31–46

H. Jenkins, Confronting the challenges of participatory culture: Media education for the 21st century (MIT Press, USA, 2009)

M. Kankaanranta, T. Mäkelä, Valuation of emerging learning solutions, in World Conference on Educational Multimedia, Hypermedia and Telecommunications, Tampere, Finland , 2014

T. Kim, J.Y. Cho, B.G. Lee, Evolution to smart learning in public education: a case study of Korean public education, in Open and Social Technologies for Networked Learning , ed. by L. Tobias, R. Mikko, L. Mart, T. Arthur (Berlin Heidelberg, Springer, 2013), pp. 170–178

Chapter   Google Scholar  

S. Kim, S.M. Song, Y.I. Yoon, Smart learning services based on smart cloud computing. Sensors 11 (8), 7835–7850 (2011)

S. Kinshuk, Graf, Ubiquitous Learning (Springer Press, Berlin Heidelberg New York, 2012)

R. Koper, Conditions for effective smart learning environments. Smart Learning Environments 1 (1), 1–17 (2014)

T. Koschmann, Dewey's contribution to the foundations of CSCL research. In Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community, New Orleans, 2002, edited by G Stahl

D. Lawrence-Brown, Differentiated instruction: Inclusive strategies for standards-based learning that benefit the whole class. American secondary education , 2004, pp. 34–62

J. Lee, H. Zo, H. Lee, Smart learning adoption in employees and HRD managers. Br. J. Educ. Technol. 45 (6), 1082–1096 (2014)

O.R. Lindsley, Precision teaching: discoveries and effects. J. Appl. Behav. Anal. 25 (1), 51–57 (1992)

T. Mäkelä, M. Kankaanranta, B. Young, Q. Alshannag, In Search of Localization Criteria for Learning Solutions: Examining the localization needs of Finnish learning solutions in the United Arab Emirates. In World Conference on Educational Multimedia, Hypermedia and Telecommunications, Tampere, Finland, 23-27 June 2014

T.W. Malone, Toward a theory of intrinsically motivating instruction. Cogn. Sci. 5 (4), 333–369 (1981)

V. Mayer-Schönberger, K. Cukier, Big data: A revolution that will transform how we live, work, and think (Houghton Mifflin Harcourt, Boston, New york, 2013)

J. McTighe, J.L. Brown, Differentiated instruction and educational standards: is Detente possible? Theory Pract. 44 (3), 234–244 (2005). doi: 10.1207/s15430421tip4403_8

M.D. Merrill, First principles of instruction: Identifying and designing effective, efficient and engaging instruction (Wiley, San Francisco, 2013)

MEST: Ministry of Education, Science and Technology of the Republic of Korea, Smart education promotion strategy, President’s Council on National ICT Strategies (2011)

B.B. Meyer, N. Latham, Implementing electronic portfolios: benefits, challenges, and suggestions. EDUCAUSE Q. 31 (1), 34–41 (2008)

A Middleton, Smart learning: Teaching and learning with smartphones and tablets in post compulsory education (Media-Enhanced Learning Special Interest Group and Sheffield Hallam University, 2015)

New Media Consortium, The NMC Horizon Report: 2015 Higher Education Edition , 2015, pp. 1–50

New York Smart Schools Commission Report (2014), http://www.governor.ny.gov/sites/governor.ny.gov/files/archive/governor_files/SmartSchoolsReport.pdf . Accessed 20 Feb 2016

K.S. Noh, S.H. Ju, J.T. Jung, An exploratory study on concept and realization conditions of smart learning. J. Digit. Convergence 9 (2), 79–88 (2011)

Partnership for 21st century learning, P21 Framework Definitions (2015), http://www.p21.org/storage/documents/docs/P21_Framework_Definitions_New_Logo_2015.pdf . Accessed 20 Mar 2015

A.G. Picciano, The evolution of big data and learning analytics in American Higher Education. J. Asynchronous Learn Netw. 16 (3), 9–20 (2012)

G. Piccoli, R. Ahmad, B. Ives, Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS quarterly , 2001, pp. 401–426

D. Preuveneers, J.V. den Bergh, D. Wagelaar, A. Georges, P. Rigole, T. Clerckx, Y. Berbers, K. Coninx, V. Jonckers, K.D. Bosschere, Towards an extensible context ontology for ambient intelligence. Ambient intelligence (Springer, Berlin Heidelberg New York, 2004), pp. 148–159

D. Ritchie, C. Volkl, Effectiveness of two generative learning strategies in the science classroom. Sch. Sci. Math. 100 (2), 83–89 (2000)

D. Sampson, C. Karagiannidis, Kinshuk, Personalised learning: educational, technological and standardisation perspective. Interact. Educ. Multimedia 4 , 24–39 (2002)

M. Scardamalia, C Bereiter (Knowledge building, The Cambridge, 2006)

K. Scott, R. Benlamri, Context-aware services for smart learning spaces. Learning Technologies, IEEE Transactions on 3 (3), 214–227 (2010)

N. Shadbolt, From the editor in chief: ambient intelligence. IEEE Intell. Syst. 18 (4), 2–3 (2003)

G. Siemens, Learning analytics: the emergence of a discipline. Am. Behav. Sci. 57 (10), 1380–1400 (2013)

G. Siemens, P. Long, Penetrating the fog: analytics in learning and education. EDUCAUSE Review 46 (5), 30 (2011)

J.M. Spector, Conceptualizing the emerging field of smart learning environments. Smart Learning Environments 1 (1), 1–10 (2014)

G. Stahl, Group cognition: Computer support for building collaborative knowledge (MIT Press, Cambridge, 2006)

G. Stahl, T. Koschmann, D. Suthers, Computer-supported collaborative learning: an historical perspective. Cambridge handbook of the learning sciences 2006 , 409–426 (2006)

R.J. Sternberg, Successful intelligence: finding a balance. Trends Cogn. Sci. 3 (11), 436–442 (1999)

C.A. Tomlinson, J. McTighe, Integrating differentiated instruction & understanding by design: Connecting content and kids (ASCD, Alexandria, 2006)

C.A. Tomlinson, M.L. Kalbfleisch, Teach Me, Teach My Brain: a call for differentiated classrooms. Educ. Leadersh. 56 (3), 52–55 (1998)

S. Totten, T. Sills, A. Digby, P. Russ, Cooperative learning: A guide to research (Garland, New York, 1991)

B. Tucker, The flipped classroom. Education Next 12 (1), 82–83 (2012)

N. Waldron, J. McLeskey, An interview with Nancy Waldron and James McLeskey. Intervention and School & Clinic 36 (3), 175 (2001)

M.C. Wittrock, A generative model of mathematics education. J. Res. Math. Educ. 5 (4), 181–196 (1974)

Z.T. Zhu, B. He, Smart Education: new frontier of educational informatization. E-education Research 12 , 1–13 (2012)

MathSciNet   Google Scholar  

Z.T. Zhu, D.M. Shen, Learning analytics: the science power of smart education. E-education Research 5 , 5–12 (2013)

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Acknowledgements

This research work is supported by the “The research on Smart learning environment construction and application” (BCA140051) from the 12th Five-year National Research Programme on Educational Sciences in China (2014).

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Zhu, ZT., Yu, MH. & Riezebos, P. A research framework of smart education. Smart Learn. Environ. 3 , 4 (2016). https://doi.org/10.1186/s40561-016-0026-2

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Smart Education Strategies for Teaching and Learning: Critical analytical framework and case studies

Information and communication technologies (ICT) have led to the reconsideration of global public policies. In this regard, the creation of universal frameworks has mobilized networks of powerful public, private, and civil society players to scaffold a global agenda on ICT in Education (ICTE), which often combines contradictory rights-based, social justice, and economic objectives. In addition, the shift towards the digitalization and smartization of education led to the necessity for the development of national public ICTE policies, which could encompass the unprecedented changes in teaching and learning. The analysis of policy texts and case studies gives a better understanding of this sector and helps to develop the tools necessary for the successful implementation of smart education.

In this context, UNESCO IITE, the Commonwealth of Learning (COL), and Beijing Normal University (BNU) combined expertise in the field and released the publication  Smart Education Strategies for Teaching and Learning: Critical analytical framework and case studies . This work was produced within the joint project of UNESCO IITE and BNU “Rethinking and Redesigning National Smart Education Strategy”.

The Publication comprises the following sections:

• An introduction that contains a description of the situation in the field of smart education and the use of digital technologies in teaching and learning.
• Methodology defining the main approaches to the research and formulating the criteria for case analysis as well as the conceptual framework.
• Analysis of 10 selected national and related supranational policy texts on ICTE and smart education policy (China, Egypt, India, Mauritius, Russia, Singapore, South Africa, South Korea, the UK, and the USA) and 15 case studies of selected policy-informed implementation projects.
• Conclusions summarizing objectives, the analysis of cases, policy and strategy influences, and a framework for smart education policy development and monitoring of its implementation.

The manuscript highlights similarities and convergences in policy and strategy influences, contexts, and policy discourses as reflected in policy texts and policy-informed practices, amid divergent socio-economic, demographic, political, and cultural settings. Additionally, it presents an overview and analysis of the national smart education policies and related case study projects in ten countries. Finally, this document presents a template for consideration in the development of smart education policy text and provides guidelines to monitor ICTE and smart education policy implementation for stakeholders.

The Report is available in English.

Publication year: 2022

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Smart education technology: how it might transform teaching (and learning).

A special issue of the New England Journal of Public Policy (Vol. 34, Issue 1, Spring/Summer 2022) featured essays on the topic of the Future of Work which were solicited by the American Federation of Teachers for a conference on the subject it jointly hosted with the Massachusetts Institute of Technology and the Albert Shanker Institute on July 13, 2022. This is the third of these essays.

In “Smart Education Technology: How It Might Transform Teaching (and Learning),” Stephan Vincent-Lancrin takes us on a journey showcasing the transformative potential already being implemented in the classroom, while also taking a deep dive into how teachers can and will be affected by smart technology.

This article highlights the importance of digitalization as a societal trend for education and discusses how artificial intelligence and learning analytics are transforming (or have the potential to transform) education practices. It showcases the opportunities of smart technologies for education systems and how the work and role of teachers could be affected, before making some forward-looking concluding remarks.

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Building ‘Smart Education Systems’

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As the unprecedented push to improve American education enters the midpoint of its third decade, reformers can claim some success. Yet no one would argue that the job is done, particularly in the nation’s cities. Even the most successful urban school districts, the winners of the Broad Prize for Urban Education, would acknowledge that they have a long way to go toward ensuring that every child receives an excellent education and develops the knowledge and skills needed for a fulfilling and productive future.

—Nip Rogers

There is no shortage of ideas for improving urban education, and there are efforts under way in nearly every city to improve schooling for urban youths: New schools are proliferating, high schools are being redesigned, new curricula are being developed and implemented, accountability systems are being strengthened, and much more. But there is also a growing recognition that improving schools and school systems, while essential, is not enough. Ensuring that every child becomes proficient and beyond will require the support and active engagement of organizations and agencies outside of schools as well.

The role of out-of-school factors in educational success has sparked heated debate. But the debate over whether in-school or out-of-school factors are more salient in children’s learning—a debate that has raged at least since the 1966 publication of James S. Coleman’s Equality of Educational Opportunity —is in many respects a false one. Both factors are important, and both must be addressed if the nation is to fulfill its 60-year-old promise of equal educational opportunity, and its more recent pledge to ensure that all children learn to high levels.

The experiences of middle-class and affluent children make this proposition clear. To be sure, relatively affluent students tend to have schooling advantages that support higher levels of learning. Numerous studies have documented the disparities in school facilities, teacher quality, and curriculum offerings that favor more-advantaged students.

Less well known, however, are the numerous out-of-school advantages that middle-class and affluent students are more likely than poorer students to have access to. From museum visits to club memberships to internships in professional offices, relatively affluent students routinely take part in activities that enhance their learning and widen the in-school disparities. If we are serious about ensuring that all children learn to high levels, we need to address both the inequities within schools and those outside of schools.

How can this be done? A number of reform efforts have attempted to address both the in-school and out-of-school needs of children and youths, but they have not succeeded in ensuring high levels of learning and development for all students. The reasons they did not succeed are instructive, and point to a solution that might be more effective.

If we are serious about ensuring that all children learn to high levels, we need to address both the inequities within schools and those outside of schools.

One set of reforms attempted to build high-level partnerships among city agencies to integrate services for children, youths, and families. One such effort, New Futures, an initiative of the Annie E. Casey Foundation, had some success in creating new relationships across sectors, but less success in developing meaningful changes that improved outcomes for young people.

Another set of reforms attempted to support students and families by grafting a range of services onto schools. For example, the Beacon program in New York City offers recreational, cultural, and family support at 80 locations throughout the city. An evaluation of the initiative by the Academy for Educational Development found that the Beacons had helped youths avoid negative behaviors, but were generally unable to link schools to noneducational services.

There are two main reasons why these and similar initiatives were less successful than they could have been. First, the academic challenges schools face overwhelm their ability to integrate services with other agencies. Second, many of the services and supports children and families need, such as opportunities to engage with professionals in the workplace, are not amenable to being located in school buildings. Community-based organizations succeed, in large part, because of their roots and connections in the community, yet they need the access to resources and power that schools can provide to become even more effective.

What would a system look like that effectively supported children in school and outside of school? The Annenberg Institute for School Reform and its partners have been addressing that question since 2000. We have recognized that such a system must include both a highly functioning and effective school district—what the task force called a “smart district”—as well as a comprehensive and accessible web of supports for children, youths, and families. We refer to such a system as a “smart education system.”

To understand what we mean by “smart education system,” it is helpful to unpack each word in that phrase:

Smart . While the word “smart” has a particular educational connotation, it also has acquired a specialized meaning in the world of technology. In contrast to conventional technologies, which do one thing, over and over again, smart technologies are nimble and are able to learn and adapt to new situations. They are thus more efficient and provide the services that are needed. A smart education system, likewise, is nimble, adaptive, and efficient. It provides differential supports to different young people and families, depending on their needs. It is able to attract new partners to augment its capacity when needed. And it collects and uses data and makes adjustments depending on what is working and what needs to be changed.

Education . The range of services provided in a smart education system is rather broad—everything from after-school activities to cultural enrichment to internships in local businesses, and much in between. In addition, the services also help remove some barriers to learning many young people face. But what distinguishes a smart education system is the focus on educational services. The goal is to ensure that all young people are supported in and out of school in their learning and other areas of development (health, social skills, cultural competence, character, motivation, self-discipline, and more) that support academic achievement.

System . For the most part, the services and supports a smart education system provides already exist in most cities. But they do not constitute a system. Young people and their families must negotiate their own way through the opportunities that are available, and if they make it through at all it is almost by accident rather than design.

A system, by contrast, is aligned to the needs of the community. School districts and their partners in city agencies and private organizations—with community members acting as full partners—locate services and supports where they are needed and in ways the community wants. They coordinate such services to avoid duplication and make it easier for children and families to take advantage of them. They disseminate information about available opportunities widely. They provide transportation and other supports to make access easier. And they are accountable to the community—people know who is in charge and whom they can hold responsible for achieving excellence and equity.

A smart education system is nimble, adaptive, and efficient. It makes adjustments depending on what is working and what needs to be changed.

The kind of smart education system we envision does not yet exist, citywide, in any city in the United States.Yet the conditions for establishing such a system are dramatically better than they were even a decade ago, when previous reform efforts like New Futures got under way. For one thing, the active involvement of mayors in education, even in cities where they lack formal authority over school systems, has helped mobilize resources from civic and private organizations. And the growth of school networks operated by community groups has strengthened links between schools and community-based organizations.

As a result, nascent smart systems have begun to form in some cities. In Chattanooga, Tenn., a long-term effort to redesign the district’s central office to strengthen support for schools has improved public confidence in the district and enhanced partnerships that have broadened postsecondary options for students. In Dallas, a citywide partnership involving the city government, the school district, and the arts and cultural community has provided access to learning opportunities in the arts for all elementary schoolchildren.

In other cities, such as New York, Pittsburgh, and Sacramento, meanwhile, neighborhood groups have created webs of supports and formed links to schools while forging ties to school districts and city agencies.

Strengthening these efforts, and creating new ones in other cities, will require a new kind of infrastructure. Yet funders, both private and governmental, appear willing to address these needs. They, like other educators, municipal leaders, and community leaders, recognize that the traditional divide between in-school and out-of-school supports is no longer tolerable. By breaking down that wall and building a smart system that will function effectively for every child, we can finally address the gaps in opportunities that have produced achievement gaps, and help ensure that all young people do, in fact, learn at high levels.

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SMART Goals in Education: Importance, Benefits, Limitations

The SMART Goals framework is an acronym-based framework used in education to help students set clear and structured goals related to their learning.

The framework stands for:

• Specific – The goal is clear and has a closed-ended statement of exactly what will be achieved.
• Measurable – The goal can be measured either quantitatively (e.g. earning 80% in an exam) or qualitatively (e.g. receiving positive feedback from a teacher).
• Achievable – The goal is not too hard and can reasonably be met with some effort and within the set timeframe.
• Relevant – The goal is relevant to the student’s learning and development.
• Time-Based – A clear timeframe is set to keep you on task.

(If you’re a teacher, you might prefer to read my article on goals for teachers ).

The SMART Goals Framework in Education

The framework has had multiple variations over time. However, the most common framework is in the format: specific, measurable, attainable, relevant, and time-based.

1. Specific

Your goal needs to be specific. This means that you need to note a clear target to aspire toward rather than something that is vague.

For students, this is important to clarify exactly what it is you’re aiming for.

Some strategies for making sure your goal is specific include:

• State what, when, where, why, and how your goals will be achieved
• State what the goal will look like when it is achieved
• Focus on the “vital few” [1] things that you want to see done to have your goal achieved

Sometimes, this may also be stated as “strategic” rather than “specific”.

See our in-depth article on examples of specific goals for students to get more ideas!

2. Measurable

Your goal needs to be measurable. This ensures that you can identify improvements from the baseline as well as know when the goal has been met.

Your objectives can be formative, summative, or a mix of both.

A formative assessment is an assessment that takes place part-way through the project. It assesses where you’re at and how much more you need to do. Formative assessments allow you to pivot and make small adjustments to your action to make sure you meet the final goal.

A summative assessment is an assessment at the end of the project to see if you met your goal. This is the final measure of success or failure.

A measurable goal may also be qualitative or quantitative.

A quantitative goal will have a grade or numerative evaluation, such as 80% on a test.

A qualitative goal will be based on a subjective evaluation, such as getting a positive report card from a mentor, or, attaining the confidence to do a public speech.

See our in-depth article on examples of measurable goals for students to get more ideas!

3. Attainable

Your goal needs to be attainable. This means that it can’t be something that’s impossible to achieve. You need to know you’ll be able to reach your goals in order to sustain motivation.

This could be compared to the goldilocks principle . Goldilocks didn’t like porridge that was too cold or too hot. It had to be just right.

In education, we use the Zone of Proximal Development (ZPD) to explain how to promote student development and motivation. The ZPD refers to learnable content that is not too easy and not too hard.

In this zone, students can do tasks with the support of teachers and have the motivation to work because they know the content is attainable with some effort.

4. Relevant

Often also written as ‘realistic’, a relevant goal is one that makes sense to your situation. If you are setting goals in your class, your teacher would expect that the goal was about your education and not something irrelevant to class.

Your goal should also be one that is consistent with your life plan and will help you get to where you need to be. This will help you to sustain motivation and ensure the goal makes sense in the long term.

While having personal goals unrelated to your coursework is great, it’s not relevant to the lesson that you’re doing within the class on the day, so remember to set your goal so it’s related to your learning.

5. Time-Based

Setting a time by which you want to meet your goals helps to keep you on track and accountable to yourself. Without time-based end goals, you may delay your goals and lose momentum.

You can also set intermittent milestones to help keep yourself on track. This can ensure you don’t let other shorter-term and more pressing tasks get in the way and get you off track.

SMARTER Goals Add-On

Some scholars have provided additional steps to the framework. One common one is to add ‘ER’ [2] :

6. Exciting

You are more likely to achieve a goal if you make it exciting. This will motivate you to carry out your plan.

An example of excitement added to a goal would be to create some self-rewards if it is completed, like “If I complete the goal I will take myself out for dinner.”

The ‘E’ is also often added when the goals are for teachers or leaders who are setting goals for their students or staff. By making the goal exciting, they’ll be able to get buy-in from students and staff.

7. Recorded

The ‘R’ often stands for ‘Recorded’ and asks you to show how you are going to record progress.

This one is somewhat similar to ‘Measurable’ but expands on it by asking not only how you’re going to measure success, but how are you going to record progress. Keeping a journal, for example, can help you record progress and reflect on the process of chasing your coals.

The Importance of SMART Goals in Education

Goal setting helps students and teachers to develop a vision for self-improvement . Without clear goals, there is no clear and agreed-upon direction for learning.

For this reason, goals have been used extensively in education. Examples include:

• Curriculum outcomes
• Developmental milestones
• Standardized testing
• Summative and formative assessments

The SMART framework, however, tends to be a student-led way of setting goals. It enables students to reflect on what they want to achieve and plan how to achieve these goals.

As a result, the framework doesn’t just help students articulate what they want out of their education. It also provides a range of soft skills for students such as:

• Motivation for growth
• Reflective practice
• Self Evaluation
• Structured analytical thinking

Read Also: Examples of SMART Goals for Students

SMART Goals Advantages and Disadvantages

Benefits of smart goals.

The SMART framework is widely used because it helps students to clarify their goals and how they are going to go about achieving them. Often, students start with a vague statement of intention, but by the end of the session, they have fleshed out their goals using the SMART template.

Some benefits of the template include:

Limitations of SMART Goals

While the framework is easy to use and implement, it does face a few limitations. One major downside is that it doesn’t account for the importance of incrementalism in self-improvement. Students need to break down their goals into a series of milestones.

Some limitations of the template include:

SMART Goals Template

Get the Google Docs Template Here

SMART goals help students to reflect on what they want from their education and how to achieve it. They provide a template and framework for students to go into more depth about their goals so they are not simply vague statements, but rather actionable statements of intent.

A lesson where you get your students to set out their goals will often have students leaving the class with a much deeper understanding of what they want out of their education and how they might go about getting it.

Read Also: A List of Long-Term Goals for Students and A List of Short-Term Goals for Students

[1] O’Neil, J. and Conzemius, A. (2006). The Power of SMART Goals: Using Goals to Improve Student Learning . London: Solution Tree Press.

[2]  Yemm, G. (2013). Essential Guide to Leading Your Team: How to Set Goals, Measure Performance and Reward Talent . Melbourne: Pearson Education. pp. 37–39.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Durable Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 100 Consumer Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 30 Globalization Pros and Cons

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New Research

Microplastics Are Contaminating Ancient Archaeological Sites

New research suggests plastic particles may pose a threat to the preservation of historic remains

Aaron Boorstein

Staff Contributor

Today, microplastics are found almost everywhere: oceans , food , the atmosphere and even human lungs , blood and placenta s. But while they’re thought of as a modern problem, plastic particles are now appearing where one might least expect: ancient archaeological sites.

Researchers found microplastics in soil deposits 7.35 meters (24.11 feet) below the ground, according to a study published this month in the journal Science of the Total Environment . The soil samples date to the first or early second century C.E. and were sourced from two archaeological sites in York, England. Some were excavated in the late 1980s, while others were contemporary samples.

The scientists then used an imaging technique called μFTIR , which can detect microplastics’ quantities, size and composition. Across all samples, they found 66 particles consisting of 16 polymer types.

“This feels like an important moment, confirming what we should have expected: that what were previously thought to be pristine archaeological deposits, ripe for investigation, are in fact contaminated with plastics,” says John Schofield , an archaeologist at the University of York, in a statement .

Microplastics are fragments of plastic that are smaller than five millimeters long, the diameter of a standard pencil eraser . They come from a variety of sources, including laundry, landfills, beauty products and sewage sludge.

“In the last not even 100 years—mostly since the 1950s—we as humans have produced eight billion tons of plastic, and the estimate is only about 10 percent of that has been recycled,” Leigh Shemitz, president of the climate education group SoundWaters, told Yale Sustainability in 2020.

Microplastics have been found in soil samples before. In fact, almost one-third of all plastic waste ends up in soil or freshwater, according to the United Nations Convention to Combat Desertification .

But the new study provides “the first evidence of [microplastic] contamination in archaeological sediment (or soil) samples,” write the researchers. These findings could change how archaeologists protect historic sites.

“While preserving archaeological remains in situ has been the favored approach in recent years, the new findings could trigger a change in approach, as microplastic contamination could compromise the remains’ scientific value,” writes CNN ’s Jack Guy.

In situ , Latin for “in the place,” is the term used to describe archaeological objects that have not been moved from their original locations. Leaving remains in situ helps prevent site and artifact damage, preserves contextual setting and allows future researchers to gather information.

“The presence of microplastics can and will change the chemistry of the soil, potentially introducing elements which will cause the organic remains to decay,” says David Jennings , chief executive of York Archaeology, in the statement. “If that is the case, preserving archaeology in situ may no longer be appropriate.”

Now, the researchers will shift their attention toward better understanding the implications of their findings. They know microplastics could threaten the integrity of archaeological samples, but what exactly does that harm look like?

“To what extent this contamination compromises the evidential value of these deposits and their national importance is what we'll try to find out next,” says Schofield.

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Aaron Boorstein | READ MORE

Aaron Boorstein is an intern with  Smithsonian magazine.

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Smart Education and e-Learning 2021

• Conference proceedings
• Vladimir L. Uskov 0 ,
• Robert J. Howlett 1 ,
• Lakhmi C. Jain 2

Department of Computer Science and Information Systems, InterLabs Research Institute, Bradley University, Peoria, USA

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“Aurel Vlaicu” University of Arad, Arad, Romania

Faculty of engineering and information technology, centre for artificial intelligence, university of technology sydney, sydney, australia.

• Presents research works in the field of smart education and e-learning
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Part of the book series: Smart Innovation, Systems and Technologies (SIST, volume 240)

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• KES-SEEL: International KES Conference on Smart Education and Smart E-Learning

Conference proceedings info: KES-SEEL 2021.

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Table of contents (42 papers)

Front matter.

• Smart Education

Smart Education: Predictive Analytics of Student Academic Performance Using Machine Learning Models in Weka and Dataiku Systems

• Vladimir L. Uskov, Jeffrey P. Bakken, Prasanthi Putta, Deepali Krishnakumar, Keerthi Sree Ganapathi

Educational Trajectories Modeling for Practice-Oriented Higher Education

• Elena A. Boldyreva, Lubov S. Lisitsyna

A Hybrid Online Laboratory for Basic STEM Education

• Karsten Henke, Johannes Nau, Robert Niklas Bock, Heinz-Dietrich Wuttke

Approach to Relevant Data Providing for the Pedagogical Design in Knowledge-Intensive Areas

• Vadim D. Kholoshnia, Elena A. Boldyreva

Personalizing Older People Training in Modern Technologies for Successful Life in Smart Society

• Daria A. Barkhatova, Marina A. Bitner, Ekaterina V. Grohotova, Pavel S. Lomasko, Anna L. Simonova

Method of Planned Learning Outcomes Identification in Higher Education Based on Intellectual Analysis of Labor Market Needs

• Elena A. Boldyreva, Lubov S. Lisitsyna, Vadim D. Kholoshnia
• Smart e-Learning

A Smart e-Learning System for Data-Driven Grammar Learning

• Hengbin Yan, Yinghui Li

Experience in Smart e-Learning System Application When Switching to Distance Education to the Fullest Extent: The Case of the Moodle LMS

• Leonid L. Khoroshko, Maxim A. Vikulin, Alexey L. Khoroshko

Gamification Model for Developing E-Learning in Libyan Higher Education

• Entisar Alhadi Al Ghawail, Sadok Ben Yahia, Joma Rajab Alrzini

Digital Divide and Social Media Related to Smart e-Learning in Obstetrics During the Health Emergency by COVID-19 in Peru

• Yuliana Mercedes De La Cruz-Ramirez, Augusto Felix Olaza-Maguiña

Smart Education: Systems and Technology

Learning smart behaviors through digital simulations: combining individual-, firm- and system-level complexity.

• Andrea Montefusco, Federica Angeli, Nunzio Casalino

Implementing Virtual Reality in K-12 Classrooms: Lessons Learned from Early Adopters

• Espen Stranger-Johannessen, Siw Olsen Fjørtoft

Software Testing Education Experiences Using Collaborative Platforms

• Camelia Chisăliţă-Creţu, Florentin Bota, Andreea-Diana Pop

Interactive Theorem Prover Based on Calculational Logic to Assist Finite Difference and Summation Learning

• Federico Flaviani

Smart Education: Case Studies and Research

The effect of emergency remote teaching from a student's perspective during covid-19 pandemic: findings from a psychological intervention on doping use.

• Tommaso Palombi, Federica Galli, Luca Mallia, Fabio Alivernini, Andrea Chirico, Thomas Zandonai et al.
• SEEL Proceedings
• Smart Pedagogy
• Mathematical Modelling
• Systems and Technology
• Smart Teaching

Vladimir L. Uskov

Robert J. Howlett

Lakhmi C. Jain

Book Title : Smart Education and e-Learning 2021

Editors : Vladimir L. Uskov, Robert J. Howlett, Lakhmi C. Jain

Series Title : Smart Innovation, Systems and Technologies

DOI : https://doi.org/10.1007/978-981-16-2834-4

Publisher : Springer Singapore

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Hardcover ISBN : 978-981-16-2833-7 Published: 06 June 2021

Softcover ISBN : 978-981-16-2836-8 Published: 07 June 2022

eBook ISBN : 978-981-16-2834-4 Published: 05 June 2021

Series ISSN : 2190-3018

Series E-ISSN : 2190-3026

Edition Number : 1

Number of Pages : XV, 506

Number of Illustrations : 42 b/w illustrations, 114 illustrations in colour

Topics : Computational Intelligence , Artificial Intelligence , Teaching and Teacher Education , Engineering/Technology Education

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Guest Essay

Something Other Than Originalism Explains This Supreme Court

By Marc O. De Girolami

Mr. De Girolami is a law professor at the Catholic University of America. He is writing a book about traditionalism in constitutional law.

It is a sign of the polarizing nature of the current Supreme Court that even knowledgeable critics of its opinions make diametrically opposed arguments.

This week, for example, the former Supreme Court justice Stephen Breyer, in a new book, “Reading the Constitution,” chides the current court’s approach to the law, which he says fixates on the text of the Constitution and attaches too much significance to the meanings of its provisions at the time they were ratified. If only, Justice Breyer urges, justices would soften this “originalist” approach and take into account how “our values as a society evolve over time” — including by respecting the “longstanding practice” of the court and other organs of government.

Justice Breyer’s criticism follows on the heels of that of another judge, Kevin Newsom of the U.S. Court of Appeals for the 11th Circuit. In a talk last month at Harvard Law School, Judge Newsom made the opposite argument: He criticized the Supreme Court, when considering matters such as handgun regulation and abortion rights, for being insufficiently faithful to originalism and overly attuned to social practices that occurred or continued after constitutional ratification. Such traditions, he warned, “have no demonstrable connection to the original, written text.”

The current Supreme Court is the object of considerable controversy and confusion. To understand its decisions properly, especially over the past three or four years, the key is to realize that each critic is half right. Justice Breyer is right that the Constitution should be interpreted, in part, in light of practices that persisted after its ratification, but wrong to think that the current court is not doing this. Judge Newsom is right that the current court is doing this, but wrong to think that it should not be.

This court is conventionally thought of as originalist. But it is often more usefully and accurately understood as what I call “ traditionalist ”: In areas of jurisprudence as various as abortion, gun rights, free speech, religious freedom and the right to confront witnesses at trial, the court — led in this respect by Justices Samuel Alito, Clarence Thomas and Brett Kavanaugh — has indicated time and again that the meaning and law of the Constitution is often to be determined as much by enduring political and cultural practices as by the original meaning of its words.

The fact that the Supreme Court seems to be finding its way toward an open embrace of traditionalism should be broadly celebrated. To be sure, the court’s traditionalism has played a role in many decisions that have been popular with political conservatives, such as the Dobbs ruling in 2022 that overturned Roe v. Wade. But it is not a crudely partisan method. Justice Sonia Sotomayor, an Obama nominee, has used it in a decision for the court — and Justice Amy Coney Barrett, a Trump nominee, has expressed some skepticism about it.

Traditionalism may not be partisan, but it is political: It reflects a belief — one with no obvious party valence — that our government should strive to understand and foster the common life of most Americans. The Supreme Court has relied on traditionalism to good effect for many decades, though the justices have seldom explicitly acknowledged this. Traditionalism should be favored by all who believe that our legal system ought to be democratically responsive, concretely minded (rather than abstractly minded) and respectful of the shared values of Americans over time and throughout the country.

To get a better sense of what traditionalism is, it is useful to compare it with the two dominant approaches to constitutional interpretation in adjudication: originalism and what is often called “living constitutionalism.”

Sometimes the Constitution’s words are not clear and their application to a particular issue is also unclear. Consider the line “Congress shall make no law respecting an establishment of religion,” from the First Amendment. Judges face choices about how to determine what exactly Congress (and today, by extension, the states) is being forbidden from doing.

One option is to discern the meaning that those words would have had at the time of their adoption, using ratification-era dictionaries, contemporary documents by learned authorities, databases of usage, other linguistic and legal sources and perhaps activities closely confined to the founding period. That is originalism.

Another option is to understand those words by recourse to a high ideal or abstraction. For example, a judge might take that passage of the First Amendment to reflect a principle of separation of church and state and then apply that principle in light of the judge’s moral views or perceptions of contemporary moral standards in the case at hand. That is living constitutionalism.

Traditionalism offers a third option. Here, one would look at specific political and cultural practices — the activities of the organs of government and of individuals and groups across the country over long periods of time — to help determine constitutional meaning and law. For example, one might observe that the practice of legislative prayer (prayer that opens legislative assemblies) was pervasive long before and at the time of the First Amendment’s ratification, and that it continued for centuries afterward. For that reason, one would conclude that legislative prayer is unlikely to violate the prohibition against an “establishment of religion.”

The intuition is straightforward: It would be odd to think that the Establishment Clause of the First Amendment prohibits legislative prayer if legislative prayer was widely practiced before, during and for centuries after ratification. Were we supposed to put a stop to a practice many showed no sign of wanting to stop, and indeed, that a great many people were eager to continue and did continue? Sometimes, yes, moral reflection or changed circumstance prompts a re-evaluation of our practices. But in general, we do what we mean and we mean what we do, and constitutional law takes its shape accordingly.

In its 2021-2022 term, traditionalism was the Supreme Court’s preferred method in a number of high-profile cases. Consider New York State Rifle and Pistol Association v. Bruen, a 2022 decision that concerned a New York law that strictly limited the carrying of guns outside the home. Justice Thomas, writing for the majority, held that New York’s requirement to demonstrate a “special need for self-protection” before the state would issue a handgun permit for self-defense outside the home violated the Second Amendment.

The “historical tradition” of handgun regulation, Justice Thomas argued, established the limits of the right to keep and bear arms. He noted that the practices of regulation “from before, during and even after the founding” of the United States indicated “no such tradition in the historical materials,” which suggested that a long, unbroken line of tradition, stretching from medieval England to early 20th century America, was at odds with New York’s law. The opinion granted the existence of scattered 19th-century regulations akin to New York’s, but argued that these were dwarfed by the dearth of analogous traditions of gun regulation over time and across state and local communities.

One can see a similar traditionalist approach in Dobbs, where Justice Alito, writing for the court, examined the government practices of abortion regulation before, during and after ratification of the 14th Amendment, concluding that there is no constitutional right to abortion in part because there is “an unbroken tradition of prohibiting abortion” that persisted “from the earliest days of the common law until 1973.”

Likewise, in Kennedy v. Bremerton School District, the Supreme Court decided in 2022 that a public school football coach who prayed on the field after games was not in violation of the Establishment Clause by holding, in an opinion by Justice Neil Gorsuch, that this was not analogous to prayer practices long considered Establishment Clause violations. And in the unanimously decided case Houston Community College System v. Wilson, the court in 2022 held that “long settled and established practice” determined that elected bodies do not violate their members’ freedom of speech when they censure one of their members.

For some critics, the invocation of “tradition” sets off alarm bells. After all, our country looks very different today, demographically and otherwise, than it did hundreds of years ago, when political power was held by relatively few and denied to others for illegitimate reasons. These critics ask how well traditionalism deals with the contemporary realities of American democracy.

The answer to this legitimate question is: Compared to what? Consider again originalism and living constitutionalism. These approaches, different as they are from each other, are both suited to elite actors working at the nerve centers of legal and political power. Both depend on the preferences and findings of the legal professional class. Originalism privileges the centuries-old writings of illustrious figures of the founding or Reconstruction era as determined by today’s most brilliant legal historians and theorists. Living constitutionalism privileges the high ideals of today’s most prominent academics and judges.

Traditionalism, by contrast, looks to the ordinary practices of the American people across time and throughout the country. In democracies, people obey the law because they believe it is legitimate, and the law acquires legitimacy when the people believe they have had a hand, direct or indirect, in shaping it. True, the practices of “the people” may be repudiated or upended — no political tradition is perfect — but while they endure, their origin in popular sovereignty is a presumptive reason to preserve them.

Tradition, in the law and elsewhere, illuminates a basic fact of human life: We admire and want to unite ourselves with ways of being and of doing that have endured for centuries before we were born and that we hope will endure long after we are gone. At its core, this is what constitutional traditionalism is about: a desire for excellence, understood as human achievement over many generations and in many areas of life, that serves the common good of our society.

Not all traditions are worthy of preservation. Some are rightly jettisoned as the illegitimate vestiges of days gone by. But many, and perhaps most, deserve our solicitude and need a concerted defense.

Traditions can be fragile things. To the extent that a revitalized practice of constitutional interpretation is possible, it will depend on determining the content of the Constitution with an eye to their sustenance and restoration.

Marc O. De Girolami ( @MarcODeGirolami ) is a law professor at the Catholic University of America, where he is a co-director of the Center for Law and the Human Person.

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News & Insights

Where Will C3.ai Stock Be in 5 Years?

April 05, 2024 — 06:45 am EDT

Written by Harsh Chauhan for The Motley Fool  ->

C3.ai (NYSE: AI) endured a lot of volatility in 2024. After a subdued start to the year, shares of the pure-play provider of enterprise artificial-intelligence (AI) software rocketed higher toward the end of February following the release of the company's fiscal 2024 third-quarter results (for the three months ended Jan. 31).

The stock shot up more than 25% in a single day as it delivered a beat-and-raise quarter. The company's results showed that it could indeed capitalize on the growing need for AI software with its solid momentum. However, the gains were short-lived, and shares tumbled 32% since the beginning of March and are now down 12% in 2024.

Is C3.ai's pullback an opportunity for savvy investors in anticipation of solid gains over the next five years? Let's find out.

A lucrative market could help accelerate C3.ai's growth

The enterprise AI market is predicted to clock a compound annual growth rate (CAGR) of 34% through the end of the decade, according to Grand View Research. The market's growth will be fueled by the need for generative AI applications such as chatbots, virtual assistants, speech recognition, and natural language processing, among others.

C3.ai is looking to tap this massive market with its platform, which allows its customers to design, develop, and deploy enterprise-grade generative AI applications. As third-quarter results indicate, customers are showing interest in its AI platform.

Management struck four deals in the $5 million to $10 million range last quarter, up from none in the year-ago period. The number of deals with a value between $1 million and $5 million increased from six to 10 in the year-ago period. Deals valued at less than $1 million increased an impressive 80% year over year.

Though the average total contract value of C3.ai's deals was down to $1.2 million in the third quarter from $1.9 million in the year-ago period, the higher number of deals that it is signing should help it offset that decline.

A key reason C3.ai is able to close more enterprise AI software deals now is because of its transition to a pay-as-you-go model from a subscription model. By removing the need for protracted negotiations associated with long-term subscription agreements, management believed that it would be able to accelerate its sales cycle and improve the adoption of its platform. The strategy seems to be working: It closed 50 agreements last quarter, an increase of 85% from the year-ago period.

The number of new pilot programs that C3.ai is engaged in with its customers increased 71% year over year to 29, indicating that it could continue winning new business by converting these pilots into new customers. The company also says that its potential pipeline of customers increased by 73% on a year-over-year basis last quarter. So top-line growth is expected to pick up from this year's estimated growth of 15% to $308 million.

AI revenue estimates for current fiscal year ; data by YCharts.

How much upside can the stock deliver over the next five years?

The chart above indicates that C3.ai's annual revenue could jump to almost $446 million in fiscal 2026. The company generated $267 million in revenue in fiscal 2023, so the top line is expected to have a CAGR of 19% over the next three fiscal years. Assuming its top line increases at a 20% annual rate in fiscal years 2027 and 2028, annual revenue could increase to $642 million after five years.

Shares currently trade at 10 times sales, which represents a premium to the U.S. technology sector's average price-to-sales ratio of 7.2. Assuming the sales multiple is in line with the technology sector after five years, its market cap could increase to $4.62 billion. That would be a 48% increase from its current amount.

But C3.ai could command a higher sales multiple after five years if its growth indeed accelerates thanks to artificial intelligence, which is why it might be a good idea for investors to capitalize on this AI stock's recent pullback because it could deliver healthy long-term gains.

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Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool recommends C3.ai. The Motley Fool has a disclosure policy .

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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