| Project/Area Number |
23K20186
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| Project/Area Number (Other) |
20H01719 (2020-2023)
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Multi-year Fund (2024)
Single-year Grants (2020-2023) |
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| Section | 一般 |
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| Review Section |
Basic Section 09070:Educational technology-related
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| Research Institution | Institute of Science Tokyo |
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Principal Investigator |
クロス ジェフリーS 東京科学大学, 環境・社会理工学院, 教授 (90532044)
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| Project Period (FY) |
2024-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
Fiscal Year 2024: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2023: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | online learning / personalized learning / AI / dashboard / educational technology / オンライン学習 / edtech / personalized / learning system / essay grading / machiine learning / metacognition / LMS / software / 個人学習 / 学習管理システム / 仮想現実 / 人工知能 / メタ認知 / ラーニング分析 / eラーニング / 機械学習 / 教育技術 / 個人化された学習 |
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| Outline of Research at the Start |
The Personalized Online Adaptive Learning System (POALS) is a web-based add-on to a learning management system (LMS) developed to help learners succeed when taking online learning courses. It is made up of three components: 1) the Metacognitive Tutor to equip students with metacognitive skills needed for autonomous learning crucial to online learning, 2) the Adaptive Engine to help students manage the cognitive strain of having metacognitive tutoring alongside domain knowledge learning, and the 3) Analytics Dashboard for the instructor.
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| Outline of Annual Research Achievements |
Significant strides in personalized online learning, focusing on the integration of artificial intelligence (AI) and educational technology were achieved.The lab developed the Personalized Online Adaptive Learning System (POALS), a web based platform designed to enhance learner autonomy through metacognitive skill development.
The lab's AI in Education group explored various innovative applications of AI in learning environments. Research included the use of large language models (LLMs) to facilitate personalized learning in complex subjects like linear algebra, aiming to improve comprehension and performance through tailored feedback and natural language processing analysis. Another project focused on the development of Virtual Memory Palaces (VMPs) using VR technology, examining the effectiveness of image memorability prediction models to optimize educational content.
These research activities underscore the lab's commitment to advancing personalized education through AI and immersive technologies, contributing to the broader goal of sustainable and effective learning solutions. The results resulted in a number of conference and publications.
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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
Three students conducted research on personalized learning working on different themes and there progress was reasonable.
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| Strategy for Future Research Activity |
The research grant was scheduled to end at the end of March 2025 but some budget remains so research will continue in 2025. Research will continue on personalized online learning and using LLMs with learner facial emotion recognition to give feedback.
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