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.

Language learning applications usually estimate the learner’s language knowledge over time to provide personalized practice content for each learner at the optimal timing. However, accurately predicting language knowledge or linguistic skills is much more challenging than math or science knowledge, as many language tasks involve memorization and retrieval. Learners must memorize a large number of words and meanings, which are prone to be forgotten without practice. Although a few studies consider forgetting when modeling learners’ language knowledge, they tend to apply traditional models, consider only partial information about forgetting, and ignore linguistic features that may significantly influence learning and forgetting. This article focuses on modeling and predicting learners’ knowledge by considering their forgetting behavior and linguistic features in language learning. Specifically, we first explore the existence of forgetting behavior and cross-effects in real-world language learning datasets through empirical studies. Based on these, we propose a model for predicting the probability of recalling a word given a learner’s practice history. The model incorporates (1) three types of key information related to forgetting (time-gap, interaction, and word features), (2) question formats, and (3) similarities between words using the attention mechanism. Extensive experiments on two real-world datasets show that the proposed model improves performance compared to baselines. Moreover, the results indicate that combining multiple types of forgetting information and item format improves performance. In addition, we find that incorporating semantic and morphological features, such as word embeddings, to model similarities between words in a learner’s practice history and their effects on memory also improves the model. Our work indicates a potential future research direction for the knowledge tracing task in second language acquisition, which gives more instructive results for enhancing learning and teaching.

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.

ABSTRACT The conservation of endangered species is contingent upon accurate and efficient wildlife monitoring, which is essential for informed decision‐making and effective preservation strategies. With the global population of Amur tigers (Panthera tigris altaica) falling below 600, innovative conservation strategies are critically needed. Traditional monitoring methods have fallen short in accuracy and efficiency, leading to a shift towards leveraging big data and artificial intelligence for effective wildlife surveillance. Existing re‐identification techniques struggle with natural habitat challenges like occlusions, changing poses, varying light, and limited data. To overcome these issues, we propose the pose‐guided dual branch re‐identification network (PDBRNet). Our approach integrates pose estimation to guide feature disentanglement and alignment, crucial for accurate re‐identification, while an image preprocessing method considering illumination factors mitigates lighting variations' impact on accuracy. Through validation on the occluded and illumination‐varying amur tiger (OIAT) dataset, PDBRNet demonstrates exceptional performance. Specifically, in single‐camera scenarios, PDBRNet achieves an outstanding mean average precision (mAP) of 79.4, surpassing the performance of PGCFL (51.6) and PPGNet (69.7). Moreover, in cross‐camera scenarios, PDBRNet maintains its superiority with a remarkable mAP of 54.0, along with Rank‐1 and Rank‐5 scores of 97.8 and 98.9, respectively, showcasing its robustness in real‐world surveillance applications. The introduction of PDBRNet significantly enhances re‐identification accuracy and holds promise for addressing complexities in field environments, contributing significantly to wildlife conservation efforts.

Course recommender systems can assist students in identifying suitable or appealing courses by leveraging user interaction data. However, a prevalent issue with existing course recommender systems is their tendency to prioritize accuracy over explainability. To address this limitation, we propose a novel Knowledge Entity-Aware Model for course recommendation called KEAM, which supports explicit user profile generation based on detailed information from a knowledge graph to enhance comprehension of the students. Specifically, we exploit the information within knowledge graphs using neural networks. Then, KEAM captures students' preferences and creates profiles for explainable recommendations. Comprehensive experiments are conducted on two datasets to verify the effectiveness and explainability of KEAM.

Language learning applications usually estimate the learner’s language knowledge over time to provide personalized practice content for each learner at the optimal timing. However, accurately predicting language knowledge or linguistic skills is much more challenging than math or science knowledge, as many language tasks involve memorization and retrieval. Learners must memorize a large number of words and meanings, which are prone to be forgotten without practice. Although a few studies consider forgetting when modeling learners’ language knowledge, they tend to apply traditional models, consider only partial information about forgetting, and ignore linguistic features that may significantly influence learning and forgetting. This paper focuses on modeling and predicting learners’ knowledge by considering their forgetting behavior and linguistic features in language learning. Specifically, we first explore the existence of forgetting behavior and cross-effects in real-world language learning datasets through empirical studies. Based on these, we propose a model for predicting the probability of recalling a word given a learner’s practice history. The model incorporates key information related to forgetting, question formats, and semantic similarities between words using the attention mechanism. Experiments on two real-world datasets show that the proposed model improves performance compared to baselines. Moreover, the results indicate that combining multiple types of forgetting information and item format improves performance. In addition, we find that incorporating semantic features, such as word embeddings, to model similarities between words in a learner’s practice history and their effects on memory also improves the model.