| Project/Area Number |
20K00862
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 02100:Foreign language education-related
|
|---|
| Research Institution | The University of Aizu |
|---|
Principal Investigator |
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)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
Debopriyo Roy 会津大学, コンピュータ理工学部, 教授 (30453020)
|
|---|
| Project Period (FY) |
2020-04-01 – 2025-03-31
|
| Project Status |
Granted (Fiscal Year 2023)
|
| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
|---|
| Keywords | machine learning / EFL / learning styles / affordances / education / ICT / Machine Learning (ML) / Learning styles / Affordances / Education / EFL Education / Machine Learning / Learning Styles / EFL education |
|---|
| Outline of Research at the Start |
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.
|
| Outline of Annual Research Achievements |
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.
|
| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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.
|
| Strategy for Future Research Activity |
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.
|
