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The development of technology enables diverse learning experiences nowadays, which shows the importance of learners’ self-regulated skills at the same time. Particularly, the ability to allocate time properly becomes an issue for learners since time is a resource owned by all of them. However, they tend to struggle to manage their time well due to the lack of awareness of its existence. This study, hence, aims to reveal how learners allocate their time and evaluate the effectiveness of the time allocation by examining its effects on learners’ performance. We collect the learning logs of 116 seventh-graders from the online learning system implemented in a Japanese public junior high school. We look at the data in the time window of 34 days before the regular exam. Even though clustering techniques as a Learning Analytics method help identify different groups of learners, it is seldom applied to group students’ learning patterns with different levels of indicators extracted from their learning process data. In this study, we adopt the method to cluster students’ patterns of time allocation and find that better performance can result from the consistency of study time throughout the exam preparation period. Practical suggestions are then proposed for different roles involved in digital learning environments to facilitate students’ time management. Collectively, this study is expected to make contributions to smart learning environments supporting self-regulated learning in the digital era.

This study investigates learner’s reading behaviors in a critical reading task in humanities course using learning analytics techniques. A Critical Analysis of Literature and Cinema course was selected as a context. The course activities evolved over 10 years, and for this instance, some face-to-face classroom critical reading activities were migrated to online mode by using BookRoll, a learning analytics enhanced eBook platform. Students (n=22 out of the 50 registered) accessed Hayavadana, an Indian play uploaded on BookRoll, and attempted to identify performative elements and cultural references in the text and highlight them. In this study, we analyze learner’s reading logs gathered in the learning record store linked to BookRoll during that activity. We extend our previous work where we identify four online reading profiles: effortful, strategic, wanderers, and check-out, based on learner’s clickstream interactions and time spent with the content. We validate the profiles with qualitative interview data collected from the learners and illustrate the quantified learning behaviors of each of those profiles based on an engagement metric. Our work aims to initiate further discussion related to the application of learning analytics in humanities courses both to probe into the learning behaviors of the students and thereby enhance the experiences with the use of interactive learning environments and data-driven services.

Self-directed learning (SDL) ability, its usefulness in higher education and life-long learning have been highlighted in previous literature. However, there has been much less understanding of the ...

Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system.

This study aims to identify the bibliometric outcomes of articles and research trends in specific areas. This study revealed research on the use of e-books for EFL education published in selected j...

UNESCO reported in March 2020 that 84.5% of total enrolled students are affected in some way by COVID-19 pandemic with more than 166 country-wide closures of schools (UNESCO, 2020). Japan too imposed emergency remote teaching and learning at both the school and university level. In this study, we focus on a national university in Japan, and investigate how teaching and learning were facilitated during this pandemic period using an e-book platform, BookRoll. BookRoll was linked as an external tool to the university’s learning management system. Such an endeavor also reinforced the Japanese national thrust regarding explorations of e-book-based technologies and using Artificial Intelligence (AI) in education. Teachers could upload reading materials, for instance their course notes, and associate an audio of their lecture. When students who registered in their courses accessed the learning materials, the system collected their interaction logs in a learning record store. Across the spring semesters from April to July 2020, the BookRoll system collected nearly 1.5 million reading interaction logs from more than 6,300 students across 243 courses in six domains with above 1,900 learners creating more than 78,000 reading logs weekly. While science and engineering courses mainly used the platform, law and language studies courses utilized the feature to upload audio tracks with reading materials. The interaction log analysis of the students with the content revealed that active learning actions, such as the use of annotations on text, were done more in humanities courses. Lastly, recommendations are created based on the analysis and the perception of the teachers about the emergency remote teaching and learning using the BookRoll system for orchestrating their course. © 2021, Canadian Network for Innovation in Education. All rights reserved.

Early prediction systems have already been applied successfully in various educational contexts. In this study, we investigated developing an early prediction system in the context of eBook-based teaching-learning and used students’ eBook reading data to develop an early warning system for students at-risk of academic failure -students whose academic performance is low. To determine the best performing model and optimum time for possible interventions we created prediction models by using 13 prediction algorithms with the data from different weeks of the course. We also tested effects of data transformation on prediction models. 10-fold cross-validation was used for all prediction models. Accuracy and Kappa metrics were used to compare the performance of the models. Our results revealed that in a sixteen-week long course all models reached their highest performance with the data from the 15th week. On the other hand, starting from the 3rd week, the models classified low and high performing students with an accuracy of over 79%. In terms of algorithms, Random Forest (RF) outperformed other algorithms when raw data were used, however, with the transformed data J48 algorithm performed better. When categorical data were used, Naive Bayes (NB) outperformed other algorithms. Results also indicated that models with transformed data performed lower than the models created using categorical data. However, models with categorical data showed similar performance with models with raw data. The implications of the results presented in this research were also discussed with respect to the field of Learning Analytics.

With COVID-19 pandemic forcing academic institutions to shift to emergency remote teaching (ERT), teachers worldwide are attempting several strategies to engage their learners. Even though existing research in online learning suggests that effectiveness of the online session is more dependent on pedagogical design rather than technology feature, teachers may still focus on the intricacies of the technology. In this paper, we present the evolution of an active learning pedagogy, supported by technology (eBook reader—BookRoll, Analytics Dashboard—LAViEW), for an undergraduate physics classroom across a semester that was affected by the lockdown due to pandemic. The technology-enhanced pedagogy evolved in three phases—technology used in “Content Focus” mode, technology used in “Problem Focus” mode and technology used in “Learning Dialogue Focus” mode. The entire activities were designed and implemented within the technology-enhanced and evidence-based education and learning (TEEL) ecosystem, which supported integration of learning technologies with analytics system. Comparison of the student’s learning logs indicated that there was a sustained engagement in the learning activities conducted during the blended (before lockdown) and online mode (during lockdown). We had conducted one-way ANOVA to compare the post-test scores for each teaching phase and found statistically significant differences in the latter phases. A preliminary qualitative analysis of the learner artifacts generated as memos in BookRoll during each phase revealed that students were posing conceptual clarifications during the latter phases. These were also having greater alignment with the session agenda and showed construction of new knowledge based on the seed knowledge provided during the instructor–learner interaction sessions. The study provides key insights into how reflection and practice by both learner and teacher improves the acceptance of technology-enabled pedagogy.

We aim to identify the engagement strategies that higher education students, engaging in emergency online learning in low-resource settings, perceive to be effective. We conducted a sequential mixed-methods study based on Moore&rsquo, s interaction framework for distance education. We administered a questionnaire to 313 students engaging in emergency online learning in low-resource settings to examine their perceptions of different engagement strategies. Our results showed that student&ndash, content engagement strategies, e.g., screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student&ndash, teacher strategies, e.g., Q and A sessions and reminders. Student&ndash, student strategies, e.g., group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies varies based on the students&rsquo, gender and technology access. To support instructors, instructional designers, and researchers, we propose a 10-level guide for engaging students during emergency online classes in low-resource settings.

Collaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems.

Blended learning technique has adapted many new digitized tools to facilitate students with flexible and self-phased learning opportunities. The flipped classroom strategy, one of the blended learning models has been limited by low engagement of students in the online component. In the present study, we augment a Flip and Pair (F&P), an active-learning strategy into the blended learning course. Following the AB type single group quasi-experimental design, we evaluated the effects of F&P strategy on the student’s engagement and learning while orchestrating it for an undergraduate engineering physics course. Our results highlighted that there is a positive correlation between the engagement (computed based on learning logs of TEEL (Technology-enhanced and Evidence-based Education and Learning) platform in the F&P activities with that of the performance score (knowledge quizzes and final exam). F&P strategy had a better contribution compared to Flip and Traditional Teaching (F&TT) strategy with respect to both engagement and performance. Also, students exhibited a positive perception of learning and engagement. Based on our findings, we identified the key instructional measures that an instructor can follow to increase student engagement while using the F&P strategy.

Self-directed learning (SDL) ability, its usefulness in higher education, and life-long learning have been highlighted in previous literature. However, understanding the effects of SDL ability in the school settings, specifically how it affects learners' affective and behavioral outcomes, remains missing and calls for further investigation. Given this research gap, this study developed a goal-oriented active learning system, GOAL, to support students' SDL and investigated how students' perceptions of SDL ability affect their reading engagement, SDL behavior, and motivation for extensive reading. The results showed that the high SDL ability students demonstrated significantly more reading engagement, SDL behaviors, motivation and autonomy for extensive reading than those with low SDL ability. These findings suggested that an SDL support environment could be exploited as a useful tool to support foreign language learning in the schools; however, the affective and behavioral outcomes created by the environment were affected to varying degrees by the levels of students’ SDL ability. The study provided implications for researchers studying extensive reading and SDL environments, as well as for educators seeking to improve extensive reading with SDL strategy usage.

The current study is situated in a city where English is the second language in school. A two-group intervention was conducted in a kindergarten. The experimental group of learners was given computer-assisted intervention in phonological processing skill and the control group had traditional (pencil-andpaper) training on phonological processing skill. This paper reports the performance of those early learners across two phonological detection tests and explicates their performance patterns using the iSAT tool and its associated analysis method. The results showed that children in the experimental group outperformed than those in the control group during posttest and retention test which were assessed immediately after the intervention and 10 weeks after the intervention. This suggests the superior effect of using computer-assisted intervention on improving young learners' phonological detection ability.

Interactive Stratified Attribute Tracking (iSAT) is a visual analytics tool for cohort analysis. In this paper, we show how instructors can use iSAT to visualize transitions of groups of students during teaching-learning activities. Interactive visual analytics gives the instructor the affordance of understanding the dynamics of the class of students and their activities from the data collected in their own teaching-learning context. We take an example of a peer instruction (PI) activity and describe how iSAT can be used to analyze its clicker responses. During PI, typically instructors only use histograms to visualize the distribution of clicker responses in the pre- and post-discussion phases. We show that the use of iSAT to analyze clicker data in real time to trace transitions of participants’ responses during various voting phases can support them in planning for their post-PI activities. Seven patterns of transitions that emerge are aligned, returns, starburst, slide, attractor, switching, and void. We interpret them in the context of the example. Such transition patterns are neither available in multiple histograms of individual voting phase nor generated in real time to be visualized as a flow diagram. We had conducted two workshops to introduce iSAT to the instructors and demonstrated the workflow of using iSAT with their dataset. Here, we report usefulness and usability data collected from those workshops. In conclusion, we highlight the power of iSAT for instructors to do cohort analysis in their teaching-learning practice.

Representational competence (RC), defined as "the ability to simultaneously process and integrate multiple external representations (MERs) in a domain", is a marker of expertise in science and engineering. However, the cognitive mechanisms underlying this ability and how this ability develops in learners, is poorly understood. In this paper, we report a fully controllable interface, designed to help school students develop RC. Further, as the design emerged from the application of distributed and embodied cognition theory to the RC problem, the design also seeks to shed light on the cognitive mechanisms underlying the integration of MERs. Here we report a preliminary eye and mouse tracking study, which sought to develop a detailed understanding of how students interacted with our interface, under self and text-guided exploration conditions. We also examined how the interaction process related to students' ability to integrate the representations in the interface. Results highlighted several desirable student behaviors, and potential points of modification of the interface to improve integration of MERs.