Yamada Laboratory, Kyushu University

Presenting at IEEE ICALT 2020

2020年07月06日

The COVID-19 pandemic shows no signs of settling down… Even as things began to look calm and various restrictions were lifted, the number of infections is rising again. The Japan Society for Educational Technology, with which I am involved, held its autumn national conference online, and the Japan Society for Educational Systems and Information Technology national conference, where I am scheduled to present, will also be held online.

#At the Japan Society for Educational Technology, I will present on the relationship between seating position and Self-Regulated Learning.
#At the Japan Society for Educational Systems and Information Technology, I will present on the formative evaluation of the learning dashboard “Meta-board” as a result of AIP acceleration research.

In Japan, online conferences have been moving forward, starting with the Japan Society for Educational Technology spring national conference, and the trend of holding international conferences online seems likely to continue for some time. My laboratory is scheduled to present two papers at IEEE ICALT 2020, and these will also be online presentations. If it had been held on-site, it would have been in Estonia… It is a wonderful place; I visited it when the ICWL international conference was held there previously, and I was even able to visit the Skype headquarters. I am interested in it as a country where IT adoption is quite advanced. It is enviable that various administrative procedures can be completed online, which is rarely the case in Japan.

I will be presenting the following two papers at IEEE ICALT 2020. Both are short papers, but I would be happy if you could listen to them if you are interested. It seems that participation alone costs 50 Euros.

Geng, X., Xu, Y., Chen, L., Ogata, H., Shimada, A., and Yamada, M. (2020). Learning Analytics of the Relationships among Learning Behaviors, Learning Performance, and Motivation, Proceedings of IEEE ICALT 2020, in printing

This is an analysis of the relationship between the Course Interest Survey, an evaluation metric of the ARCS model (a type of Instructional Design), and learning logs, conducted as part of the expansion of Learning Analytics to high schools that we carried out last year, using mathematics as the field. Classes were conducted in three groups based on proficiency levels, and we also performed comparisons between these classes. While there has not been much analysis of instructional design and learning behavior, it is an important perspective for considering effective Instructional Design. What was previously evaluated via questionnaires can now be evaluated at the behavioral level, allowing us to consider what is necessary for feedback to improve instruction.

Chen, L., Xu, Y., Geng, X., Ogata, H., Shimada, A., and Yamada, M. (2020). Do Difference Instructional Styles Affect Students’ Learning on Summer Assignment?, Proceedings of IEEE ICALT 2020, in printing

This is a presentation on the analysis of learning behaviors regarding (a portion of) summer vacation homework in high school. The data used is limited to mathematics. Summer vacation homework seems to be a cultural phenomenon in Asia. In Western countries, although I don’t think this applies to every country or region, the prevailing idea seems to be that since it is a vacation, students should rest. That is a very sound way of thinking (laughs). Although I couldn’t get the reviewers to understand that point, we are analyzing learning behaviors during long vacations like summer break, when teachers cannot monitor students face-to-face. The data analyzed this time is a portion of the summer homework provided via BookRoll, which was not mandatory but was recommended to be completed. As you might imagine, procrastination behavior is visible overall, but there were still characteristics specific to each class. While procrastination in summer homework is predictable, we did not know when or how they were learning. However, Learning Analytics allows us to understand such things, and we can use this analysis to consider classes for the second semester.

It looks like online conferences and international meetings will continue throughout this year, but I would like to steadily do what I can and advance Learning Analytics research that can be put into practice. Following LAK2020, my students are also working hard and producing results. The program for IEEE ICALT 2020 is posted here.

https://icalt2020.ut.ee/program

#Professor Dirk is giving a keynote, isn’t he?

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