Last week, I participated in the JSISE National Conference and gave a presentation on “Metaboard,” a learning dashboard developed through AIP acceleration research. This weekend, I participated in the JSET 2020 Autumn National Conference. I would like to thank the conference planning committee and the executive committee for their hard work in organizing the autumn conference. Thank you very much. I imagine it was quite challenging, as it involved developing a dedicated system for posters and handling responsibilities beyond just planning and operations. Thank you for your hard work.

This time, there were five presentations from Yamada Laboratory, including my own.

Xuanqi Feng, Masanori Yamada (2020). An Approach to Detecting Learning Path Patterns in Game-Based Learning Environments in Informal Learning, Proceedings of the JSET 2020 Autumn National Conference, 171-172.

In the evaluation of game-based learning environments for informal learning in a fully online environment, there is a problem where the learning process cannot be observed, making it difficult to construct formative and practical evaluations. Learning analytics, which focuses on analyzing learners’ behavioral history, can be expected to solve this problem. In the author’s previous research, an approach to detect and visualize learners’ behavioral patterns was proposed, but it did not address learning content. In this study, we focused on learning paths—the order in which knowledge is learned—and proposed a new analytical approach to detect learning path patterns using Levenshtein distance and hierarchical cluster analysis.

Li Chen, Masanori Yamada (2020). Proposal for Collaborative Problem-Solving STEM Lesson Design Based on Learning Behavior Perspectives, Proceedings of the JSET 2020 Autumn National Conference, 203-204.

In recent years, collaborative problem-solving STEM education has spread globally with the aim of acquiring knowledge related to science and technology and fostering problem-solving skills using that knowledge. In this study, we used a learning analytics approach to examine how to design collaborative problem-solving processes and what STEM learning factors should be incorporated into STEM classes from the perspective of learning behavior.

Yufan Xu, Masanori Yamada (2020). Development of a Visualization System for Individual Learning Behavior and Participation to Improve Participation in CSCL, Proceedings of the JSET 2020 Autumn National Conference, 249-250.

One of the major issues in Computer-Supported Collaborative Learning (CSCL) research is the variation in learner participation. One factor contributing to this is that group members cannot grasp the individual learning status of others or their participation in group activities, leading to social loafing. To solve this problem, the authors have previously designed visualizations for individual learning and participation during group activities. In this paper, based on the previous visualization designs, we describe the data collection and interface development.

Xuewang Geng, Masanori Yamada (2020). Examination of the Effectiveness of a Compound Verb Learning Support System Using Augmented Reality, Proceedings of the JSET 2020 Autumn National Conference, 401-402.

In recent years, the advancement of mobile technology has increased opportunities for learning using mobile devices. Augmented reality (AR) offers the potential to enhance learning experiences by using mobile devices to combine the real world with virtual objects. The authors have developed an AR-based system to support the learning of Japanese compound verbs. This paper reports on the evaluation results, focusing on the relationship between learning outcomes and the degree of technology acceptance. The evaluation results showed a strong negative correlation between the behavioral intention to use AR and the difference between pre- and post-test scores.

Masanori Yamada, Yoshiko Goda, Hironori Egi (2020). Does the Seat Location of Students in Class Affect Learning Outcomes and Awareness? – An Approach from Self-Regulated Learning –, Proceedings of the JSET 2020 Autumn National Conference, 349-350.

The seat location of students in class has attracted attention as an indicator that teachers empirically use to estimate learning motivation and outcomes. However, research has often been based on teachers’ experience, and the theoretical basis has been unclear. This study analyzed whether the seat location of students in lecture-style classes affects learning outcomes and attitudes from the perspective of Self-Regulated Learning. The analysis showed that there is no statistically significant relationship between seat location and self-regulated learning awareness, number of absences, or grades.

Our students also worked hard on their presentations. Since there were presentations I wanted to attend, I was not able to monitor the sessions the whole time, but Professor Fujimoto (University of Tokyo) came to visit Mr. Feng and listened to his talk. Thank you very much. There was also a professor who stayed at Mr. Geng’s poster for about an hour to discuss his work. I am sure there were others who visited our other students as well. I truly feel that our research is supported by many attendees. I am very grateful. Thank you very much.

Now that the autumn conference is over, the next one is the spring conference. There are still many things under consideration, but we need to finalize them soon. We will publish information on our website and in our newsletter as it becomes available, so please check back when you have time. In addition to the spring conference, there is an international conference on science and technology education called IEEE TALE in December. I am serving as the Publication Chair and coordinating the Learning Analytics session for Special Track 2. Things are starting to get busy there as well. JSiSE and JSET members can participate at a discounted rate, so I hope you will consider attending.

Thank you for your continued support.