| Project/Area Number |
20K00862
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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 02100:Foreign language education-related
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| Research Institution | The University of Aizu |
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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)
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| Co-Investigator(Kenkyū-buntansha) |
Debopriyo Roy 会津大学, コンピュータ理工学部, 教授 (30453020)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| 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)
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| Keywords | Machine Learning (ML) / EFL / Learning styles / Affordances / Education / EFL Education / Machine Learning / Learning Styles / EFL education |
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| 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.
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| Outline of Annual Research Achievements |
The primary objective of this research is to scrutinize learning styles within the sphere of English as a Foreign Language (EFL) education, utilizing Machine Learning (ML) to identify alterations in the learning styles of online students. A critical intermediate phase involves comprehending the correlation between the choice of device and an array of learning activities. Once established, this comprehension will facilitate a more detailed examination of various modes of content delivery. Throughout the academic year of 2022, we deployed an assortment of qualitative coding techniques and PCA-based methodologies for data analysis, while also furthering our investigation into the role of device usage in education, specifically analyzing student device preferences in an online learning environment.
The findings derived from our 2022 research have been disseminated at multiple academic conferences with published proceedings, including FIE 2022, EDUNINE2022, EUROCALL 2022, and LWMOOCS 2022. A special issue of the IEEE Transactions in Education featured an extended version of one of these conference proceedings. Our preliminary findings also received acceptance for an extended publication in the same special issue of IEEE Transactions in Education. Additionally, two book proposals have been approved for publication: one by Springer and the second by IGI Global, with the latter featuring a comprehensive range of expertise from the fields of education and engineering.
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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
Within the span of the current year, we have efficaciously conducted a survey, gathering data from a wider range of participants concerning their preferred device usage for educational purposes. Such data will augment the empirical basis of our ongoing research. Furthermore, we engaged in a rigorous analysis of the data to glean insights into students' perceptions of online learning, an exercise that promises to enhance the interpretation of our ultimate findings.
An exploration of the significant hurdles to the implementation of online education was also undertaken. By taking into account historical strategies, we aimed to anticipate potential technical issues that could surface during the remainder of our research period. Consequently, the primary objectives were successfully met, leading to the selection of Option 2.
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| Strategy for Future Research Activity |
Drawing upon the results obtained so far, the research trajectory for the forthcoming 2023 academic year will be primarily centered on an intensified effort towards gathering more comprehensive data, specifically pertaining to learning styles. The aim is to garner a robust, empirical basis that can offer deeper insights into the nuanced dynamics of learning styles within the realm of English as a Foreign Language (EFL) education.
Subsequent to this expanded data collection phase, a meticulous analysis of the learning styles data will be conducted. The methodological approach for this analytical phase will involve the deployment of an array of Principal Component Analysis (PCA) based techniques. These techniques, which were thoroughly explored and effectively utilized during the research conducted in the 2022 academic year, have proven to be potent analytical tools. Through the application of these PCA-based techniques, the research will aim to derive more profound and nuanced understandings of learning styles in EFL education.
In sum, the 2023 academic year will witness a twofold focus: an intensified effort towards extensive data collection on learning styles, and a rigorous analysis of this data using PCA-based techniques. This strategic focus promises to yield a richer, more nuanced understanding of learning styles, thereby enhancing the overall impact and relevance of the research.
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