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.

Agricultural loss due to the overpopulation of Sika deer poses a significant challenge in Japan, leading to frequent human–wildlife conflicts. We conducted a study in Muroran, Hokkaido (42°22′56.1″ N–141°01′51.5″ E), with the objective of monitoring Sika deer and notifying farmers and locals. We deployed a Sika deer detection model (YOLOv8-nano) on a Raspberry Pi, integrated with an infrared camera that captured images only when a PIR sensor was triggered. To further understand the timing of Sika deer visits and potential correlations with environmental temperature and humidity, respective sensors were installed on Raspberry Pi and the data were analyzed using an ANOVA test. In addition, a buzzer was deployed to deter Sika deer from the study area. The buzzer was deactivated in the first 10 days after deployment and was activated in the following 20 days. The Sika deer detection model demonstrated excellent performance, with precision and recall values approaching 1, and a bounding box creation latency of 0.82 frames per second. Once a bounding box was established after Sika deer detection, alert notifications were automatically sent via email and the LINE messaging application, with an average notification time of 0.32 s. Regarding the buzzer’s impact on Sika deer, 35% of the detected individuals reacted by standing upright with alert ears, while 65% immediately fled the area. Analysis revealed that the time of day for Sika deer visits was significantly correlated with humidity (F = 8.95, p < 0.05), but no significant association with temperature (F = 0.681, p > 0.05). These findings represent a significant step toward mitigating human–wildlife conflicts and reducing agricultural production losses through effective conservation measures.

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.