Sayuri Chida
ABSTRACT
This study sought to improve the performance of students with low motivation and grades in university-level mathematics. Objective and subjective data were collected simultaneously from e-learning and motivation tests, respectively, and were analyzed using machine learning. The results were, thereafter, provided as feedback to the learners, which enabled them to undertake self-regulated learning. To enhance and refine our previous study, we conducted Structural Equation Modeling to analyze the causality between factors that lead to motivation for self-regulated learning, adding new subjective data collected through the use of Motivation Scales of Motivated Strategies for Learning Questionnaire. This is how we provided them with better feedback. The results of the evaluation experiment confirmed that the experimental group with feedback showed increase in the final exam scores and the subscale scores related to motivation.
- National Survey of University Students, Center for Management and Policy Studies, The University of Tokyo, 2018, https://ump.p.u-tokyo.ac.jp/crump/cat77/cat82/22018.htmlGoogle Scholar
- SLATE: Centre for the Science of Learning & Technology https://www.uib.no/en/slateGoogle Scholar
- University of California, Berkeley. Intelligent research design for data intensive social science, Berkeley Learning Analytics Group. https://live-dlab.pantheon.berkeley.edu/working-groups/berkeley-learning-analytics-groupGoogle Scholar
- Manabu Suzuki, Kazunari Hosoki, Tatsuo Kitamura, Hitoshi Kato.2016. Relationships among the time, burden, and psychological stress responses of students conducting tasks at home in clinical training. In Proceedings of The 51st Japan Physical Therapy Congress.Google Scholar
- Masamichi Yuzawa, 2014. Working Memory and Education, Working Memory Supporting Learning, (pp.81–97) Kitaoji Shobo.Google Scholar
- Yoshihide Noritake, Yuko Takei, and Masaharu Terasaki, 2020. Effect of short-term written disclosure on working memory task performance decline caused by high-pressure situations. Ed Psych R. 68, 134 –146.Google Scholar
- Sayuri Chida and Ken'ichi Minamino, 2022. Effectiveness of relative assessment feedback using motivation tests and learning data in university mathematics. 21th Information Science and Technology Forum.Google Scholar
- Yuhei Chiba, Sayuri Chida, and Ken'ichi Minamino, 2023. Analysis of obstacles to mathematics learning using M-GTA in university. In Proceedings of Information Processing Society of Japan, 85th National Convention.Google Scholar
- Zimmerman B. J., Developing self-fulfilling cycles of academic regulation: An analysis of experimental instructional models. In D. H. Schunk & B. I. Zimmerman. Self-regulated learning: From learning to self-regulated practice. New York. The Guilford Press.Google Scholar
- PintrichP.R, SMITH, D.A.F, GRACIA, T. and MCKEACHIE, W. J. Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53:801-813, 1993.Google Scholar
- Kai Hatano. 2013. The effects of intrinsic motivation of university students on their self-regulated learning attitudes via self-regulated learning strategies. J. Jap Soc Ed Eng 37 (Suppl.), 81–84.Google Scholar
- Yoshiko Goda, Masahiro Yamada, Hiroshi Kato, Gakuji Matsuda, Hiroshi Saito, and Hiroyuki Miyagawa. 2012. Self-tuning learning scale in asynchronous distributed e-learning. A. Uni Ed 15, 9–20.Google Scholar
- Nahoko Ishikawa and Chiharu Mukogo. 2017. An analysis of the relationship between self-regulated learning strategies in working students in university correspondence courses. J. Jap Soc Ed Eng 40, 4, 315–324.Google Scholar
- “What is WebClass?”, Japan Data Pacific, https://www.datapacific.co.jp/webclass/Google Scholar
- Toshimitsu Inomata and Kenichi Minamino, 2020. Discrete Mathematics for Information Systems, Kyoritsu Publishing.Google Scholar
- Toshihiko Ito, Nobuyuki Okamoto, and Shigehiko Yagira. Development of Shimane-AMTM (I). In Proceedings of the Faculty of Education, Shimane University, 20, 65–83. 1986.Google Scholar
- Pintrich PR, De Groot EV, Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology 82: 33-40, 1990.Google Scholar
- Akemi Miyabe, Chiaki Togashi, Yumiko Sakuma, and Chihiro Sato. 2016. Reliability and validity of the Japanese version of the MSLQ (Motivation Scales). J. Jap Soc Health Phys 25, 276 –286.Google Scholar
- Masanori Shinohara, Methods of Educational Practice Research - Introduction to Statistical Analysis with SPSS and Amos, Minerva Shobo, 2016.Google Scholar
- Shinji Oshio. 2020. Learning from Research Cases: Analysis of Psychological and Survey Data by SPSS and Amos, 3rd edition, Tokyo Tosho.Google Scholar
- Atsushi Mizumoto and Osamu Takeuchi. 2008. Basics and considerations for reporting effect sizes in research papers, English Education Research, Kansai English Education Society, 31, 57 –66.Google Scholar
- Lanqin Zheng, Xin Li,2016, The Effects of Motivation, Academic Emotions, and Self-regulated Learning Strategies on Academic Achievements in Technology-enhanced Learning Environment, IEEE 16th International Conference on Advanced Learning Technologies.Google Scholar
Index Terms
- Relative Evaluation Feedback to Promote Self-Regulated Learning in University Mathematics
Recommendations
Promoting self-regulated learning in web-based learning environments
Self-regulated learning with the Internet or hypermedia requires not only cognitive learning strategies, but also specific and general meta-cognitive strategies. The purposes of the Study2000 project, carried out at the TU Dresden, were to develop and ...
Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses
Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and outperform those with weaker SRL skills. SRL is critical in learning environments that ...
Ontologies for Self-Regulated Learning
Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and InnovationSelf-regulated learning is the key for students to be masters of their own learning processes. These self-initiated processes enable these students to become controllers of their learning experiences. The objective of this research is to maximize the ...
Comments