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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.

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.

Increasing organizations and educational institutions have implemented virtual learning communities to encourage knowledge sharing. However, this task can not be accomplished simply by grouping people together and telling them "sharing your knowledge will make you learn better". This research attempts to examine the factors influencing knowledge sharing from the perspective of human behavior. Theory of Planned Behavior is integrated with social network ties and empirical findings from virtual learning community literature to develop the research model. The current research model comprises eight hypotheses to explore questions of whether social network ties, learners' attitude toward knowledge sharing, learners' beliefs of their capabilities in performing online knowledge sharing, and subjective norms relate to knowledge sharing intention, which leads to actual behavior in a virtual learning environment. This study empirically validates the hypothesized relationships using a field survey of college students and MBA students enrolling courses conducted in a virtual learning community. Attitude, subjective norm, Web-specific self-efficacy and social network ties are shown to be good predictors of knowledge sharing intention which, in turn, is significantly associated with knowledge sharing behavior. Knowledge creation self-efficacy does not significantly impact knowledge sharing intention. (Contains 3 tables and 2 figures.)