| Project/Area Number |
22K02874
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 09070:Educational technology-related
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| Research Institution | Kyoto University of Advanced Science (2023-2024)
Ritsumeikan University (2022) |
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Principal Investigator |
マルチュケ モリツ 京都先端科学大学, 経済経営学部, 准教授 (80738584)
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| Co-Investigator(Kenkyū-buntansha) |
林 勇吾 立命館大学, 総合心理学部, 教授 (60437085)
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| Project Period (FY) |
2022-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2024: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2023: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2022: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | E-Learning / Online Course / Kano model / NLP / E-learning / Educational technology / Machine Learning / Kano Model |
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| Outline of Research at the Start |
This research aims to address the comprehension of e-learning online course features by using the Kano model in combination with statistical and machine learning methods.
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| Outline of Annual Research Achievements |
I have (co-)authored three journal papers (all first author, including a high-impact journal Wiley Disasters), four conference papers (two first author), and one book (organizing committee member for proceedings of ACM IC4E). The knowledge gained from collaborating with international researchers in various fields has prompted future research ideas, which are currently implemented. These include the extension of the current research topic to the use and analysis of generative AI and large language models (LLMs) in e-learning settings.
The research system setup was updated (dynamic CMS website with necessary functionality and questionnaire updates), to include generative AI and natural language processing (NLP).
A new approach was implemented by using dynamic topic modeling to classify and label e-learning comments to analyze topics and their sentiment. Findings were published in IEEE WAIE 2024 proceedings.
I have extended my research collaborative community to include researchers from Brawijaya University in Indonesia (e-learning and IoT experts), Munich University of Applied Sciences in Germany (LLM expert and PhD student collaboration), International Balkan University in North Macedonia (Psychology and Psycho-technology expert), as well as an international team at Ritsumeikan University and Kyoto University of Advanced Science (technology and AI experts, international economy experts). This research grant and research project had a wide-reaching cross-pollination effect of e-learning with multiple research fields.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
While there was an initial delay due to a job change in AY2023 and the project was extended by one year, class and analysis are being pursued at a smooth rate now.
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| Strategy for Future Research Activity |
Data from further courses is being implemented and evaluated. Data gathered for each course has been extended to track students interaction and satisfaction with course content as well as a chatbot feature for students to interact with the course content.
Results from additional classes will be consolidated with previous findings. Advanced language models (transformer based, e.g., BERT or LAMA), including Topic Modeling, UMAP clustering, etc. will be used to evaluate freeform text comments and create a hybrid model with the Kano method. New and updated results will be published in top international conferences. Aggregate results are planned to be published in renowned journals.
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