| 研究課題/領域番号 |
22K02874
|
|---|
| 研究機関 | 立命館大学 |
|---|
研究代表者 |
マルチュケ モリツ 立命館大学, グローバル教養学部, 准教授 (80738584)
|
|---|
| 研究分担者 |
林 勇吾 立命館大学, 総合心理学部, 教授 (60437085)
|
|---|
| 研究期間 (年度) |
2022-04-01 – 2025-03-31
|
| キーワード | E-learning / Kano model / Educational technology / Machine Learning |
|---|
| 研究実績の概要 |
The research system setup was completed (dynamic CMS website with necessary functionality). Multiple online courses (Introduction to Algorithms and Programming, Social Change with AI, Software Engineering) were implemented and data was collected and analyzed for two classes. Anonymous ex-ante and ex-post questionnaires were designed and distributed. The research results, stemming from the Kano model analysis and AI language model analysis, were published in two international conferences and one journal.
The perception of e-learning and online courses by students can provide valuable insights into course design and user experience. Students were surveyed on the same 12 features related to online course satisfaction before and after each course. Textual comments were also gathered. The Kano model from customer satisfaction research was used to perform an ex-ante and ex-post comparative analysis for the 12 features of both short-term and long-term courses. A simple neural network was trained on freeform comments for both courses to create language models (Word2Vec) and compare the findings with the Kano model results. The results of the macro shift (expectations vs. consumption experience) and micro shift (individual student’s shift within a requirement) were compared.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Multiple online courses were implemented and data was collected and analyzed for two classes. Anonymous ex-ante and ex-post questionnaires were designed.
The research progressed according to plan.
|
| 今後の研究の推進方策 |
Data from further courses is planned to be implemented and evaluated. In addition to undergraduate classes, graduate classes will also be taken into account.
Results from additional classes will be consolidated with previous findings. Advanced language models will be trained to evaluate freeform text comments and create a hybrid model with the Kano method. Updated results will be published in top international conferences. High impact journals are being targeted next for dissemination of these findings.
|
