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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.

Personalized or precision education (PE) considers the integration of multimodal technologies to tailor individuals' learning experiences based on their preferences and needs. To identify the impact that emerging multimodal technologies have on personalized education, we reviewed recent implementations and applications of systems (e.g., MOOCs, serious games, artificial intelligence, learning management systems, mobile applications, augmented/virtual reality, classroom technologies) that integrate such features. Our findings revealed that PE techniques could leverage the instructional potential of educational platforms and tools by facilitating students' knowledge acquisition and skill development. The added value of PE is also extended beyond the online digital learning context, as positive outcomes were also identified in blended/face-to-face learning scenarios, with multiple connections being discussed between the impact of PE on student efficacy, achievement, and well-being. In line with the recommendations and suggestions that supporters of PE make, we provide implications for research and practice as well as ground for policy formulation and reformation on how multimodal technologies can be integrated into the educational context.

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.

This study presents an ontology-based visualization support system for e-book learners which promotes both meaningful receptive learning and meaningful discovery learning. To examine the system effectiveness, two learning modes are used: (a) reception comparison mode, where at the outset learners are shown complete versions of expert-generated topic maps; and (b) "cache-cache comparison mode," where at the first stage of learning all information concerning relations is concealed, and at the second stage learners are encouraged to actively create those relations before comparing the learner-generated and expert-generated relations. The 50 control group participants studied in reception comparison mode while the 146 experimental group participants studied in cache-cache comparison mode. Differences in learning perception and achievement between the two groups are examined, as is the effect of learner expertise level on learning mode effectiveness. Although the control group reported significantly more pressure and less satisfaction than the experimental group, no significant learning achievement differences were found between the two groups. However, in cache-cache comparison mode, the performance of learners with low prior knowledge increased more than that of learners with high prior knowledge; on the other hand, for learners with high prior knowledge, no significant effect of learning mode on learning achievement was found.

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 effects of SDL ability in the school settings, specifically the effects on learners' SDL behaviors and processes. To address this limitation, this study investigated the relations between SDL ability, SDL behaviors, and reading outcomes and further explored the process of planning behaviors in SDL. This study examined the context of SDL for extensive reading using a goal-oriented active learning system, GOAL. The results showed that the high SDL ability students demonstrated significantly more reading outcomes in terms of books completed and the number of days read than those with low SDL ability. The high SDL ability students engaged significantly more in planning behaviors, that were found to be significantly correlated with reading outcomes, than the low SDL ability students. Cluster analysis and transition analysis also differentiate groups of learners with different planning behaviors. These findings suggested that the learning behaviors and outcomes facilitated by the environment were affected to varying degrees by the levels of students' SDL ability, and personalized feedback can be created using the SDL behavioral variables and patterns in the environment.

In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn't delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts.

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 journals of the Web of Science from 2011 to 2020, and discusses the trends of the top-cited countries, journals, the most used keywords, and the popular subjects, learning strategies, target skills, and the research outcomes. A total of 53 selected articles were manually and systematically analyzed. The results revealed that the most productive country was Taiwan, and the most cited journal was Computers & Education. The most used keywords were "engagement." The bibliometric analysis can be reflected in the content analysis as preschoolers and elementary school students were the most common participants, guided learning were the most frequently used strategy, and the main target skill was reading. This review study provides an overview of the research agenda for the last 10 years, and highlights the trends in the field by analyzing relevant past research. When the use of e-books in EFL education studies was examined, reading comprehension, teaching/learning strategies, and learning engagement were found to be crucial in the studies. Therefore, there is a need to develop more useful features of e-books to support EFL teaching practices. It is therefore suggested that future research focus more on pedagogical aspects and e-book feature development.

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.

In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring current needs in an actual special needs context. In this study, we used the learning and evidence analysis framework (LEAF) as a learning analytics-enhanced learning environment and employed Active Reading as an example learning task to investigate the challenges and possibilities of applying AI to inclusive education in the future. Two students who attended a resource room formed the context. We investigated learning logs in the LEAF system while each student executed a given learning task. We detected specific learning behaviors from the logs and explored the challenges and future potential of learning with AI technology, considering human involvement in orchestrating inclusive educational practices.

In the age of artificial intelligence (AI), trust in AI systems is becoming more important. Explainable recommenders, which explain why an item is recommended, have recently been proposed in the field of learning technology to improve transparency, persuasiveness, and trustworthiness. However, the methods for generating explanations are limited and do not consider the learner's cognitive perceptions or personality. This study draws inspiration from tailored intervention research in public health and investigates the effectiveness of personality-based tailored explanations by implementing them for the recommended quizzes in an explainable recommender system. High school students (n = 217) were clustered into three distinct profiles labeled Diligent (n = 77), Fearful (n = 72), and Agreeable (n = 68), based on the Big Five personality traits. The students were divided into a tailored intervention group (n = 106) and a control group (n = 111). In the tailored intervention group, personalized explanations for recommended quizzes were provided based on student profiles, with explanations based on quiz characteristics. In the control group, only non-personalized explanations based on quiz characteristics were provided. An 18-day A/B experiment showed that the tailored intervention group had significantly higher recommendation usage than the control group. These results suggest that personality-based tailored explanations with a recommender approach are effective for e-learning engagement and imply improved trustworthiness of AI learning systems.

Persistence represents a crucial trait in learning. A lack of persistence prevents learners from fully mastering their current skills and makes it difficult for them to acquire new skills. It further hinders the administration of effective interventions by learning systems. Although most studies have focused on identifying non-persistence and unproductive persistence behaviors, few have attempted to model students' persistence propensity in learning. In the present study, we evaluated students' persistence propensity in formative assessments by using an item response theory model with their attempt data. In addition, we modeled their wheel-spinning propensity. The students (N = 115) of first-level mathematics classes at a high school in Japan underwent the aforementioned formative assessments; their log data were collected. Persistence propensity was found to be correlated with frequency-related statistics, and wheel-spinning propensity was correlated with correctness-related statistics. However, persistence and wheel-spinning propensities were not correlated. A comparison of the students' scores with various persistence and wheel-spinning propensities revealed that both traits considerably influenced their academic performance. The present study provides insights into the use of attempt data to evaluate various characteristics crucial for learning, which are otherwise difficult to evaluate.

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.

Recent spread of the COVID-19 forces governments around the world to temporarily close educational institutions. In this paper, we evaluated learning engagement, level of satisfaction and anxiety of e-book based remote teaching strategy on an online learning platform. The research involves 358 students at an urban junior-high school in Japan. Learning logs were analyzed to measure student engagement, whereas survey responses indicated their perception regarding the remote learning experience. Log analysis revealed that the average completion rate over 267 learning materials was 67%. We also observed a significant decrease in engagement 3 weeks after remote learning and different subjects and grades. Survey analysis showed students felt both satisfaction and anxiety about remote learning. However, there were significant differences in the level of satisfaction between different grades. The results indicated that: (1) maintaining students' motivation is a challenge to remote learning in secondary schools; and (2) we need to relieve students' anxiety about their own progress in the class and their classes after the break. This study is the first to report trends in actual teaching--learning engagement, which were recorded during sessions of emergency remote teaching in Japanese schools. The results can inform the future implementation of remote learning in junior-high schools.

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.