Digital learning platforms equipped with behavior sensors have provided abundant educational data. Utilizing this data, learner modeling can identify assorted learner characteristics from their behavior logs for learning analytics and dynamically update them in real time. There is a growing demand for knowledge-level modeling, moving beyond behavioral logs to assess knowledge proficiency. Based on item response theories and cognitive statistical models, existing studies estimate learning rates across various knowledge elements in each learning step in intelligent tutoring systems. However, these models, tailored to specific knowledge domains, offer limited flexibility across different knowledge units and scenarios. This paper introduces an adaptation of the basic additive factor model underpinned by logistic regression, focusing on behavior indicators. Drawing upon authentic learning data from an e-book learning infrastructure for junior high math, we examine the feasibility of our adapted models and demonstrate their potential for flexibility across knowledge units and learning phases.

This study proposes indicators from daily handwritten math learning logs of junior high school students to model knowledge proficiency, and analyzes the extent of their actual correlation with proficiency using the LEAF system. Our analysis reveals that specific pen stroke behaviors, such as writing speed and task engagement time, show significant, though weak, correlations with proficiency levels. These findings suggest that handwritten logs can serve as effective indicators of student proficiency, offering valuable insights for enhancing educational outcomes.

This study proposes the Open Knowledge and Learner Model (OKLM), a novel framework that integrates Learning Analytics (LA) with Digital Twin (DT) technology to model learners' knowledge, internal states, and environments. The OKLM DT framework addresses the limitations of traditional LA systems by enabling accurate estimation of knowledge states and personalized learning strategies. We developed a conceptual framework for the learner DT using LA, verified the accuracy of the OKLM-based DT model, and applied it to a learning support system. Initial experiments in an English literature recommendation system showed that, while the recommendations did not significantly enhance learners' motivation, they were well-received and positively correlated with increased engagement among highly motivated learners. A subsequent study involving Intensive Reading (IR) support for EFL learners further validated the model's effectiveness. Additionally, experiments targeting educators demonstrated that the OKLM's visualization tools were valuable for understanding learner characteristics and tailoring teaching materials. These findings suggest that OKLM can enhance the versatility and accuracy of learner models across various educational contexts, offering a significant advancement in the field of LA.

Many systems to assist in learning English grammar have been developed in the field of learning material recommendation systems (LMRSs). Compared with those based on knowledge models, recommendations based on data tend to cause the cold-start problem, and it is said that explainable LMRSs may be able to enhance learners’ motivation. In our research, we propose an explainable English grammar quiz recommendation system using a knowledge map to support students’ learning of English grammar with trust in the system and motivation. The learning effect of the explanation of the system was evaluated in an experiment, in which 349 high school students in Japan participated. This experiment showed that there was little learning effect of the explanation, but the system reliability and motivation improved by the explanation. The limitation and our future work regarding the validity and learning effects of the system are also indicated.

Learning vocabulary is a crucial but challenging activity for English as a foreign language learners, and computer-assisted language learning can facilitate this process. Moreover, e-learning is attracting a great deal of attention as a new technology to bring educational support which traditional learning systems cannot provide. Recommender systems as its implementation have been subject to discussion. In this study, we propose a comprehensive learning analytics-based platform for efficient vocabulary learning, including an e-book reader and a book/quiz recommender. The system on this platform estimates learners' knowledge based on their activities and brings personalized recommendation and its bases to the learners. Also, this platform provides teachers with visualized feedback regarding the recommendation and students' engagement in learning.