2022 Fiscal Year Research-status Report
Learning Support by Novel Modality Process Analysis of Educational Big Data
| Project/Area Number |
21K19824
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
久富 望 京都大学, 教育学研究科, 助教 (70825992)
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| Project Period (FY) |
2021-07-09 – 2024-03-31
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| Keywords | Modality analysis / Learning process / Learning Analytics / Recommendation / Knowledge Map |
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| Outline of Annual Research Achievements |
Based on the analysis of pen strokes conducted in the first year of this project, we focused on trying to identify possible problems that students were facing by having them self-explain the knowledge and skills using in the process of answering quiz questions. Students reviewed their pen stroke answers and self-explained at appropriate intervals, which generated annotated time series data of the answering process. This data was then analyzed to using NLP methods to generate sample self-explanations that contained the required knowledge components to solve the quiz item. The results were disseminated as an international conference paper. A self-explanation feedback and recommender system has been developed and implemented with results currently under analysis.
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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
The project is proceeding as planned due to reduced restricts from COVID. However, some areas of investigation into self-explanation that were not previously considered have been identified, and more in-depth research will be conducted as a result.
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| Strategy for Future Research Activity |
Results of analysis from the first (pen stroke) and second (self-explanation) year are being combined to provide overall feedback interface to students, with the possibility of recommending learning tasks to overcome problems encountered in the answering process. More in-depth investigation into automatically analyzing self-explanations using state-of-the-art NLP methods should enable fine grained feedback and possible hints into problems that have been encountered. We are also currently preparing to publish the results of this project in international journals to disseminate the findings to the boarder research community.
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| Causes of Carryover |
Due to the results of the project thus far, further investigation is needed into the analysis of self-explanations than was initially anticipated. More in-depth examination is planned to be carried out in the next fiscal year as a result.
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Research Products
(11 results)
