As digital materials proliferate in higher education, e-book interaction logs provide a scalable lens on how students study. However, most existing research analyzes these logs in a one-dimensional manner, which limits the ability to capture students’ study approaches comprehensively. Moreover, the relationship between students’ study approaches, metacognition, and performance remains unclear. To address these challenges, we propose a three-dimensional framework that incorporates engagement, navigation pattern, and context, combining theory-driven and data-driven perspectives to define behavior-based features and group students with similar patterns. We apply this approach to data from a real-world class in which students used an e-book system to study course materials and complete comprehension quizzes. Our analysis identified three distinct groups of students with different study approaches, and revealed that engagement investment alone does not guarantee achievement. We further examined how these approaches relate to students’ metacognitive awareness and academic performance.

Traditional knowledge graph (KG) approach often rely on static textbook content and overlook the dynamic, collaborative interactions in collaborative problem-solving (CPS). This study introduced a three-step integrated KG approach designed to support CPS in STEM education and examined the effective KG features that influence CPS learning outcomes. KGs were generated by combining learning materials and student dialogue data. Two types of features, graph structural features and knowledge activation features, were identified to classify knowledge nodes and analyze how students activated knowledge during CPS. Clustering analysis revealed three types of knowledge nodes: Peripheral Nodes, Core Nodes, and Degree Hubs. Furthermore, key features such as depth, branch, and activated paths showed positive correlations with group discussion performance and CPS skills but had limited influence on test scores. These findings highlight the potential of integrated KGs to support both individual and group learning in STEM education.

E-books generate extensive log data that sheds light on student behaviors. Among these, page jumps offer unique insights into reading strategies. However, prior research seldom connects these jumps to both the e-book's content and page functions and often presents only fragmented information. This study investigates how e-book content and page types relate to student page jump behaviors. We also propose a visualization framework that integrates e-book content and log data, enabling intuitive reading path visualizations and detailed analysis of student interactions. Our approach aims to offer educators actionable insights for refining instructional materials and providing more personalized feedback, ultimately enhancing the e-book learning experience.

Summer vacation is considered a cause of loss in students' learning performance. In this study, we investigated the differences in learning behaviors in reading learning materials and time-related behavior patterns regarding summer assignments among three classes under different instructional styles. The results showed that students' learning behaviors in summer were correlated with instructional styles in school.

Previous research has established that motivation has a positive impact on the learning processes and behaviors [1] [2]. Learning analytics (LA) can play an important role in addressing the issue of collecting learning behaviors. In this study, we observed the teaching activities of three classes and examined the relationships among learning motivation, learning performance, and learning behaviors of students in digital learning material readers.