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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.

This methodological-theoretical synergy provides an integrative framework of learning analytics through the development of the human-and-machine symbiotic reinforcement learning. The framework intends to address the challenges of the current learning analytics model, including a lack of internal validity, generalizability, immediacy, transferability, and interpretability for precision education. The proposed framework consists of a master component (the brain) and its four subsuming components: social networking, the smart classroom, the intelligent agent, and the dashboard. The brain component takes in and analyzes multimodal streams of student data from the other components with the model-based reinforcement learning, which forms policies of adequate actions that maximize the long-term rewards for both the human and machine in the seamless learning environment. An example case plan in advanced statistics was demonstrated to illustrate the course description, data collected in each component, and how the components meet different features of the smart learning environment to deliver precision education. An empirical demonstration was provided using some selected mulitmodal data to inform the effectiveness of the proposed framework. The human-and-machine symbiotic reinforcement learning has theoretical and practical implications for the next-generation learning analytics models and research.

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.

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.

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 benefit that influence the learner' 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 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.

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.

Blended learning (BL) combines traditional classroom activities with online learning resources, enabling students to obtain higher academic performance through well-defined interactive learning strategies. However, lacking the capacity to self-regulate their learning, many students might fail to comprehensively study the learning materials after face-to-face learning. In this study, a learning analytics dashboard (LAD)-based self-regulated learning (SRL) approach is proposed to enhance the students’ practices of SRL in an e-book-based BL environment. The proposed approach aims to support students to precisely reflect on their face-to-face e-book reading activities, effectively review the e-book learning materials after the face-to-face learning sessions, and, finally, set new goals for their next face-to-face learning session by using a LAD. To evaluate the effects of the proposed approach, a quasi-experimental design was deployed in a university-level course that adopted a BL model. The experimental group learned through the proposed approach using an e-book and the LAD, whereas the control group learned using the conventional BL approach using only the e-book. The results of the one-way analysis of covariance (ANCOVA) and Mann–Whitney U test demonstrate a statistically significant difference ( p -value less than 0.01) between both groups in terms of students’ learning outcomes, awareness of SRL, self-efficacy (SE), and e-book reading engagements. This provides educators with evidence of the effectiveness of an explicit SRL approach in BL, which not only improves student learning outcomes from the given course and awareness of self-regulation and SE but also increases course engagement compared to students who learn with conventional BL approaches.

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.

The advancement in network technology has stimulated the proliferation of online learning. Online learning platforms, such as the learning management systems (LMS) and e-book reading systems, are widely used in higher education to enhance students' reflection and planning of the learning process. Although many studies have explored the relationships between students' reading patterns and learning performances, few have examined the effects of self-regulated learning, learning strategy, and self-efficacy on the learning performance of students. Here, the authors collected the reading logs from an e-book reading system BookRoll and investigated the correlations between students' abilities of self-regulated learning, learning strategy, self-efficacy, and learning performance. The results of this study provide valuable insights to the teachers in higher education regarding designing courses helpful for students to improve their learning performance.

Protected areas are key to meeting biodiversity conservation goals, but direct measures of effectiveness have proven difficult to obtain. We address this challenge by using environmental DNA from leech-ingested bloodmeals to estimate spatially-resolved vertebrate occupancies across the 677 km 2 Ailaoshan reserve in Yunnan, China. From 30,468 leeches collected by 163 park rangers across 172 patrol areas, we identify 86 vertebrate species, including amphibians, mammals, birds and squamates. Multi-species occupancy modelling shows that species richness increases with elevation and distance to reserve edge. Most large mammals (e.g. sambar, black bear, serow, tufted deer) follow this pattern; the exceptions are the three domestic mammal species (cows, sheep, goats) and muntjak deer, which are more common at lower elevations. Vertebrate occupancies are a direct measure of conservation outcomes that can help guide protected-area management and improve the contributions that protected areas make towards global biodiversity goals. Here, we show the feasibility of using invertebrate-derived DNA to estimate spatially-resolved vertebrate occupancies across entire protected areas. Invertebrate-derived eDNA (iDNA) is an emerging tool for taxonomic and spatial biodiversity monitoring. Here, the authors use metabarcoding of leech-derived iDNA to estimate vertebrate occupancy over an entire protected area, the Ailaoshan Nature Reserve, China.