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Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners' sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.

UNESCO reported that 90% of students are affected in some way by COVID-19 pandemic. Like many countries, Japan too imposed emergency remote teaching and learning at both 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, which 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 in education. Teachers could upload reading materials for instance their course notes and associate an audio of their lecture. While students who registered in their course accessed the learning materials, the system collected their interaction logs in a learning record store. Across the spring semesters from April - July 2020, BookRoll system collected nearly 1.5 million reading interaction logs from more than 6300 students across 243 courses in 6 domains. The analysis highlighted that during emergency remote teaching and learning BookRoll maintained a weekly average traffic above 1,900 learners creating more than 78,000 reading logs and teachers perceived it as useful for orchestrating their course.

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'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-content engagement strategies, e.g., screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student-teacher strategies, e.g., Q and A sessions and reminders. Student-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' 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.

The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized learning analytics (LA) intervention approach that incorporates ebook and recommendation systems is proposed. The proposed approach provides students with actionable feedback regarding personalized remedial actions as the intervention to help them to strategically engage in the use of the ebook system and avoid academic failure when engaged in BL. A quasi-experiment was conducted to examine two classes of an undergraduate course that implemented a conventional BL model. The experimental group comprised 45 students from one class who learned using the proposed approach and received personalized intervention, whereas the control group comprised 42 students from the other class who learned using the conventional BL approach without receiving personalized intervention. The experimental results indicated that the proposed approach can improve students' learning achievements and behavioral engagement in BL. The findings provide pedagogical insights into the application of LA intervention with actionable feedback in BL environments.

Language learners' engagement with a specific task is crucial to improving their academic achievement. To enhance student engagement and academic achievement in language learning, personalized language learning (PLL) can be employed to consider individual learning needs. Personalized review learning has emerged to facilitate PLL as a promising means of enhancing the long-term preservation of skills and knowledge in language education. In this paper, a personalized review learning approach is proposed that improves behavioral engagement and academic achievement in language learning through e-books. It involves implementing an e-book system, namely BookRoll, which allows users to browse uploaded learning materials anytime and anywhere, in concert with a personalized review learning system based on repeated retrieval practice. To evaluate the effects of this approach, a quasi-experiment was conducted on two classes of sophomore undergraduate students majoring in accounting who were enrolled in a Japanese course. 47 students from one class were assigned to an experimental group, whereas 44 students from another class were assigned to a control group. The duration of the experiment was 8 weeks. The experimental group learned using both the e-book system and personalized review learning system, whereas the control group learned only using the e-book system. The experimental group significantly outperformed the control group in terms of both behavioral engagement and academic achievement. The findings indicate that the proposed approach enhanced the students' PLL experiences.

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.

In recent years, learning analytics has become a hot topic with many institutes deploying learning management systems and learning analytics tools. In this paper, we introduce learning analytics platforms that have been established in two top national Japanese universities. These initiatives are part of a broader research project into creating wide-reaching learning analytics frameworks. The aim of the project is to support education and learning through research into educational big data accumulated on these platforms. We also discuss the future direction of our research into learning analytics platforms. This includes introducing a model in which learning analytics tools and the results of research can be shared between different education institutes.

Reviewing learned knowledge is critical in the learning process. Testing the learning content instead of restudying, which is known as the testing effect, has been demonstrated to be an effective review strategy. However, education research recommends that instructors generate practice tests, but this burdens teachers and may also hinder teaching quality. To resolve this issue, the current study applied a modern artificial intelligence technique (BERT) to automate the generation of tests and evaluate the testing effect through e-books in a university lecture (N = 74). The last 5 minutes of each course session were utilized to review the taught content by having students either answer cloze item questions or restudy the summary of the core concepts covered in the lecture. A reading comprehension pretest was conducted before the experiment to ensure that the differences in prior knowledge were nonsignificant between groups, and a posttest was performed to examine the effectiveness of testing. In addition, we evaluated students' reading skills and reading engagement through their ability to identify key concepts and their interaction with e-books, respectively. A positive effect was observed for students who engaged in cloze item practice before the end of each class. The results indicated that the repeated testing group exhibited significantly better reading skills and engaged more with e-books than the restudying group did. More importantly, compared with only restudying the key concepts, answering the cloze items questions significantly improved students' reading comprehension. Our results suggest that machine-generated cloze testing may benefit learning in higher education.

Research has revealed the positive effects of flipped classroom approaches on students' learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation.

Providing early predictions of academic performance is necessary for identifying at-risk students and subsequently providing them with timely intervention for critical factors affecting their academic performance. Although e-book systems are often used to provide students with teaching/learning materials in university courses, seldom has research made the early prediction based on their online reading behaviours by implementing machine learning classifiers. This study explored to what extent university students' academic achievement can be predicted, based on their reading behaviours in an e-book supported course, using the classifiers. It further investigated which of the features extracted from the reading logs influence the predictions. The participants were 100 first-year undergraduates enrolled in a compulsory course at a university in Taiwan. The results suggest that logistic regression supports vector classification, decision trees, and random forests, and neural networks achieved moderate prediction performance with accuracy, precision, and recall metrics. The Bayes classifier identified almost all at-risk students. Additionally, student online reading behaviours affecting the prediction models included: turning pages, going back to previous pages and jumping to other pages, adding/deleting markers, and editing/removing memos. These behaviours were significantly positively correlated to academic achievement and should be encouraged during courses supported by e-books.

Self-direction skill is considered a vital skill for twenty-first-century learners in both the learning context and physical activity context. Analysis skill for self-directed activities requires the students to analyze their own activity data for understanding their status in that activity. It is an important phase that determines whether an appropriate plan can be set or not. This research presents a framework designed to foster students' analysis skill in self-directed activities, which aims: (1) to build a technology-enabled learning system allowing students to practice analyzing data from their own daily contexts; (2) to propose an approach to model student's analysis skill acquisition level and process; and (3) to provide automated support and feedback for analysis skill development tasks. The analysis module based on the proposed framework was implemented in the GOAL system which synchronized data from learners' physical and reading activities. A study was conducted with 51 undergraduate students to find reliable indicators for the model to then measure students' analysis skills. By further analyzing students' actual usage of the GOAL system, we found the actual activity levels and their preferences regarding analysis varied for the chosen contexts (learning and physical activity). The different context preference groups were almost equal, highlighting the utility of a system that integrates data from multiple contexts. Such a system can potentially respond to students' individual preferences to execute and acquire self-direction skill.

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.

This paper describes an automatic quiz generation system designed to support language learning that utilizes digital textbook logs. Learners often memorize words in digital textbooks while preparing for an examination, and they often use the highlight function for the words. Previous studies regarding annotations and highlights have shown that learning only by using the highlight function on important content in textbooks did not affect learning achievements. Therefore, in this study, we developed a system that can support the repeated learning by analyzing digital textbook logs and providing appropriate quizzes. An evaluation experiment involving 31 international students was conducted to assess whether the quizzes provided by our proposed system are able to enhance the learning achievements as compared to teacher-created quizzes. The results show that the quizzes by our proposed system and the teacher-created quizzes were both equally effective. A correlation analysis was conducted to identify the correlation among the learning achievements, the number of quizzes, and each variable in questionnaires. We found that there is a positive correlation between the number of quizzes and the students' learning achievements.

Precision education is now recognized as a new challenge of applying artificial intelligence, machine learning, and learning analytics to improve both learning performance and teaching quality. To promote precision education, digital learning platforms have been widely used to collect educational records of students' behavior, performance, and other types of interaction. On the other hand, the increasing volume of students' learning behavioral data in virtual learning environments provides opportunities for mining data on these students' learning patterns. Accordingly, identifying students' online learning patterns on various digital learning platforms has drawn the interest of the learning analytics and educational data mining research communities. In this study, the authors applied data analytics methods to examine the learning patterns of students using an ebook system for one semester in an undergraduate course. The authors used a clustering approach to identify subgroups of students with different learning patterns. Several subgroups were identified, and the students' learning patterns in each subgroup were determined accordingly. In addition, the association between these students' learning patterns and their learning outcomes from the course was investigated. The findings of this study provide educators opportunities to predict students' learning outcomes by analyzing their online learning behaviors and providing timely intervention for improving their learning experience, which achieves one of the goals of learning analytics as part of precision education.

Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students' learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students' self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students' learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students' text marking skill and facilitating precision education.

This paper describes a method to collect data of which section of pages learners were browsing in digital textbooks without eye-tracking technologies. In previous researches on digital textbook systems, it was difficult to collect such data without using eye-tackers. However, eye-trackers cost a massive budget. Our proposed system automatically hides the texts in the digital textbooks with mask processing before the learners browse the texts in the digital textbooks. If they click the hidden texts, the system gets rid of the masks and the texts appear letter by letter. We used NMF to discover learners' browsing patterns from the collected logs. Evaluation experiments were conducted to examine the effectiveness of our system in terms of fascination, understandableness and enhancement of thinking and to discover learners' browsing patterns. It was found that our method could enhance thinking skills. A browsing pattern of diligent learners with high learning achievements was also found.

Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of evidence as structured information. However, such platforms often focus on researchers as end-users and its design is not aligned to the practitioners' workflow. In our work, we propose a technology-mediated process of capturing teaching-learning cases (TLCs) using a learning analytics framework. Each case is primarily a single data point regarding the result of an intervention and multiple such cases would generate an evidence of intervention effectiveness. To capture TLCs in our current context, our system automatically conducts statistical modelling of learning logs captured from Learning Management Systems (LMS) and an e-book reader. Indicators from those learning logs are evaluated by the Linear Mixed Effects model to compute whether an intervention had a positive learning effect. We present two case studies to illustrate our approach of extracting case effectiveness from two different learning contexts -- one at a junior-high math class where email messages were sent as intervention and another in a blended learning context in a higher education physics class where an active learning strategy was implemented. Our novelty lies in the proposed automated approach of data aggregation, analysis, and case storing using a Learning Analytics framework for supporting evidence-based practice more accessible for practitioners.

Purpose: The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers' explanations, conduct exercises, etc. Design/methodology/approach: The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher's PC. The teacher can easily grasp students' status and whether or not students are following the teacher's explanation. Findings: Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences. Originality/value: Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.

Mobile and ubiquitous technologies have been applied to a wide range of learning fields such as science, social science, history and language learning. Many researchers have been investigating the development of ubiquitous learning environments; nevertheless, to date, there have not been enough research works related to the reflection, analysis and traces of learners' activities in the history of ubiquitous learning environment. Therefore this paper presents a research on the design and development of a dashboard function which proposes new opportunity for ubiquitous learning. The dashboard captures, analyzes and visualizes traces of learning activities in order to promote awareness and enables learners to reflect on their own activity and helps to recall what they have learned. An initial evaluation has been conducted with 14 international students. Results indicate that the dashboard is a useful tool for self-reflection on activities and recall what learners have learned by repeated quizzes.

In this paper, we assess the feasibility of using a retooled mobile and ubiquitous computer system to facilitate knowledge dissemination between users during the process of acculturation. Focused on the foreign population of a Japanese post-graduate university, the system provides a platform on which to study not only the behavior of participants but also the process of acculturation dynamically in context. Both quantitative and qualitative data were analyzed, with quantitative data being obtained through the use of survey instruments based on proven instruments in the field of acculturation and qualitative data obtained via the use of semi-structured interviews. The results of this study demonstrate that the retooled mobile and ubiquitous computer system used had measurable benefit in aiding participants undergoing the process of acculturation.

A Ubiquitous Learning Log (ULL) is defined as a digital record of what a learner has learned in daily life using ubiquitous computing technologies. In this paper, a project which developed a system called SCROLL (System for Capturing and Reusing Of Learning Log) is presented. The aim of developing SCROLL is to help learners record, organize, recall and evaluate ULLs. Using SCROLL, learners can not only receive personalized quizzes and answers to the questions, but also navigate and be aware of their past ULLs supported by augmented reality views. In particular, this paper introduces an approach that helps learners record their learning experiences in daily life from life-log photos with the help of SenseCam. To evaluate the effectiveness of this system, a case study of an undergraduate English course is presented to show how it can be used to facilitate seamless learning.

The authors' research defines a ubiquitous learning log (ULLO) as a digital record of what a learner has learned in the daily life using ubiquitous technologies. In their previous works, the authors proposed a model named LORE (Log--Organize--Recall--Evaluate) to describe the learning process of ULLO and developed a system named SCROLL to implement this model. This paper focuses on Log among 4 factors in LORE and proposed a passive way to log ULLOs. They use SenseCam to capture a learner's learning activities and propose a system named PACALL to support reflection of what s/he has seen. This system filters bad photos that taken by SenseCam and helps learner find learning content. The author use this system in language learning and help learners learn the foreign name of objects around.

In traditional classes, having many questions from learners is important because these questions indicate difficult points for learners and for teachers. This paper proposes a FAQ-based e-Learning environment to support Japanese language learning that focuses on learner questions. This knowledge sharing system enables learners to interact and share information and knowledge through FAQ and e-mail. Teachers contribute answers to discussion among learners. The system also allows learners to discuss and collaborate, stimulating their motivation to study Japanese as a foreign language. All questions are stored in a FAQ database, allowing other learners to reuse resources, helping learners learn by themselves and reduce teacher workloads. (Contains 4 tables and 4 figures.)