| Project/Area Number |
20K20131
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 90020:Library and information science, humanistic and social informatics-related
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| Research Institution | Kyoto University |
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Principal Investigator |
Majumdar Rwitajit 京都大学, 学術情報メディアセンター, 特定講師 (30823348)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | GOAL / Self-direction skills / DAPER / Wearable devices / Activity logs / Learning logs / Behavior analytics / Adaptive system / Self Direction Skills / Learning Analytics / Quantified Self / DAPER model |
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| Outline of Research at the Start |
GOAL platform synchronizes data from BookRoll, an e-book-based learning system and physical activity data from wearable devices applications such as Google Fit and Apple Health. It aims to design and develop AI-based adaptive scaffolds using the data collected in the GOAL system and evaluate its efficiency and effectiveness. key question of investigation is "How does learners develop their self-directedness using learning and wellbeing activity data and adaptive technology support?"
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| Outline of Final Research Achievements |
For students, daily activities include learning as well as physical activities. Data generated during such activities can be recorded in a learning environment and by using wearable sensors. In this research, we extended the developed GOAL system. GOAL can synchronize data from various services such as interaction logs within a learning system and activity logs from smartwatches. The infrastructure was then implemented in actual public school and university contexts in Japan. It reached more than 300 students in the K-16 context. The project investigated how learners interact with their own data from multiple contexts in such an environment to develop their self-direction skills. We conducted multiple studies focusing on the co-design of the system for multi-modal learning analytics, learning behavior analysis, the impact of using self-directed learning support on extensive reading skills of learners and providing process feedback based on pattern extraction from interaction data.
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| Academic Significance and Societal Importance of the Research Achievements |
Being self-directed is an essential skill for students. While student have access to various online learning environments and devices that they use in their daily life, this research focused on how to give data logged in such system for the learners own self-direction skill development.
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