Information and communication technology considerably impacts students’ engagement with self-regulated learning (SRL) methodologies. However, there has been a lack of any comprehensive visualization of the SRL process, making it difficult to interpret student behaviors. To address this issue, the REXX platform is used in this study to visualize SRL outputs. While REXX has previously been used to present educational metrics more comprehensively and personally in the learning management system (LMS) framework, research on understanding student behavior through the learning analytics platform (LAP) remains unexplored. This study focused on transforming REXX from an LMS to an LAP to capture detailed features of individual student profiles, thereby reflecting specific SRL characteristics. We collected profile data from 215 high school students via an e-learning web application and used K-means clustering to categorize their behaviors. The method yielded a Davies–-Bouldin score of 0.9718, a silhouette score of 0.54, and a Calinski–Harabasz score of 124.1805. This study addresses both teaching and learning strategies for educators and students. It represents a considerable step toward understanding student behavior in the e-learning environment. However, we recommend integrating machine learning models to enhance automated learning strategies alongside this baseline framework.

In recent years, autonomous robots become to be desired to treat multi-task. A robot must decide a concrete action for plural objectives. Major researches try to realize this by weighted rewards. Weighted rewards can represent a human's intention easily. But weight of each task must change dynamically by a change of surrounding situation or of a robot status. Authors consider an independent learning for each task and selection of one concrete action from candidates of each learning. Authors propose a priority function to calculate priority for each task corresponding to surrounding situation or a robot status and propose a system which do decision making by using the priority function. Authors confirmed the usefulness of proposed method with simulation.

This study examines the integration of self-regulated learning strategies with practical activities via information and communication technology (ICT) tools for data collection in high school physics curricula. It explores the interpretation of behavioral data to understand students’ learning strategies. The study engaged educators from six schools, resulting in 40 samples, to implement ICT-enabled experimental kits designed for practical learning activities. Using experimental kits aligned with curriculum outcomes, this research pioneers data collection and analysis to reveal student learning tactics, laying the groundwork for learner profile development. The methodology employs ICT-enabled experimental kits for data gathering, facilitating students’ behavior analysis during learning activities. Clustering techniques, including K-means and principal component analysis, were applied to interpret data, uncovering patterns in goal-setting, self-monitoring, and reflective learning behaviors. These findings highlight the method’s potential to create comprehensive learner profiles, enabling educators to tailor educational experiences to individual learners’ needs more effectively. This initiative aims to enrich the learning experience by seamlessly integrating traditional educational methods with technological innovations, focusing on tailoring them to individual student profiles.