| 研究課題/領域番号 |
20K00862
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分02100:外国語教育関連
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| 研究機関 | 会津大学 |
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研究代表者 |
イリチュ ピーター 会津大学, コンピュータ理工学部, 准教授 (10511503)
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| 研究分担者 |
Debopriyo Roy 会津大学, コンピュータ理工学部, 教授 (30453020)
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| 研究期間 (年度) |
2020-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2022年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2021年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2020年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
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| キーワード | machine learning / EFL / learning styles / affordances / education / ICT / Machine Learning (ML) / Learning styles / Affordances / Education / EFL Education / Machine Learning / Learning Styles / EFL education |
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| 研究開始時の研究の概要 |
In order to improve the overall efficiency of online education, this research will study learning styles in EFL education by using Machine Learning (ML) to identify patterns in online learner style changes over access device type and develop an improved questionnaire from these findings that identifies an individual’s learner style for each device type.
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| 研究実績の概要 |
The main goal of this study is to investigate learning styles in the context of English as a Foreign Language education, employing Machine Learning techniques to detect changes in the learning styles of online students. An essential intermediate step involves understanding the relationship between device choice and a range of learning activities. Once this understanding is established, it will enable a more comprehensive analysis of different content delivery methods. During the 2023 academic year, we utilized a variety of qualitative coding approaches and Principal Component Analysis (PCA) based methods for data analysis. THe research was published multiple academic proceedings, Journal articles, and book chapters.
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| 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
This year, we successfully surveyed a broader participant pool about their educational device preferences, strengthening our research's empirical foundation. We rigorously analyzed the data to understand students' online learning perceptions, which will improve the interpretation of our final results. Additionally, we investigated the role of generative AI in creating personalized learning content and adapting to individual learning styles within the context of EFL education. We also explored major obstacles to implementing online education, considering historical strategies to predict potential technical problems during our remaining research. Consequently, the main objectives were achieved, leading to the choice of Option 2.
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| 今後の研究の推進方策 |
The research plan for the 2024 academic year will primarily focus on collecting more extensive data on learning styles and integrating generative AI to create personalized learning experiences within English as a Foreign Language (EFL) education.
Following the expanded data collection phase, a thorough analysis of the learning styles data will be performed using Principal Component Analysis (PCA) based techniques, which were successfully applied during the 2022 academic year research. The research will also investigate the potential of generative AI in creating adaptive learning content tailored to individual learning styles, enhancing the effectiveness of EFL education.
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