Personalized Online Adaptive Learning System
| Project/Area Number |
20H01719
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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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 | Tokyo Institute of Technology |
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Principal Investigator |
クロス ジェフリーS 東京工業大学, 環境・社会理工学院, 教授 (90532044)
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| Project Period (FY) |
2020-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
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 | オンライン学習 / 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 |
Metacognition, or the knowledge and regulation of one's thinking process, includes skills such as goal setting and knowledge monitoring which helps learners improve their learning or make learning more efficient. This research helps learners develop metacognitive skills by creating an adaptive learning online engine for domain-specific courses. It uses natural language processing and machine learning techniques to obtain course improvement points and will customize course materials for individual learners based upon their prior knowledge upon the project conclusion in 2024.
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| Outline of Annual Research Achievements |
Personal Online Analytic Learning System (POALS) is a web-based learning management system (LMS) developed to help learners succeed when taking online learning courses. The system was developed using the open edX LMS and has been tested with learners in 3 online classes at Tokyo Tech. 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 based on student responses on the Metacognitive Tutor to give feedback to teachers on where learner interventions might be needed. In 2021, a new component was added called AI Kaku which is AI assisted writing tool. Key takeaways from this research include which metacognitive skills can be learned independently and which ones require nudging from teachers, which algorithms can work best for knowledge tracing while considering metacognitive measures, and how text data from learner metacognitive reflections can be used for learning analytics. Regarding research achievements, one peer review journal paper was published, one invited conference talk, and 9 conference papers were presented at domestic and international conferences. In addition, a doctoral student conducting this research won the best presentation award at the IEEE Teaching, Assessment and Learning for Engineering (TALE) conference in Dec. 2021 based upon this research.
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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
POALS is a web-based system designed to help learners succeed in online learning environments. It is made up of three components as stated above. The research consists of the LMS development covering data handling, analysis and testing it in online course with actual learners. The research is actually on-schedule as denoted by the number of publications and conference presentations. The research for 2022 will focus on developing the instructor analytics dashboard. In addition, research will be conducted on personalized learning using head mounted displays for immersive learning. Furthermore, research will be conducted on gaze tracking on images that appear online learning courses. Finally, research will be conducted on using natural language processing and analysis of data to provide a narrative to the instructor to share with learners regarding their progress in an online course.
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| Strategy for Future Research Activity |
The POALS research plan for AY 2022 will focus on developing an Instructor Analytics Dashboard. The dashboard is needed to summarize the learner assessment data via graphs and natural language processed narratives that the instructor can use to feedback to the learner to encourage learning and course engagement. It should be noted that POALS was originally developed for use in a web browser on a computer display or tablet. However, recently educational technology has advanced and 360 degree videos can be viewed on youtube and with a head mounted display (HMD) to study online content giving the online learner a virtual reality (VR) immersive experience. A learning module will be developed on the topic of architecture and evaluated using 360 degrees videos watched in HMD to investigate learner engagement. In addition, research results which were obtained last year will be submitted as journal papers and submitted to international conferences. A workshop will be organized at the ACM Learning at Scale international 2022 online conference to present how to prepare multi-branched stories online for teaching and learning purposes.
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Report
(2 results)
Research Products
(19 results)
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[Presentation] Global Learning at Scale2020
Author(s)
David A. Joyner, May Kristine Jonson Carlon, Jeffrey Scott Cross, Eduardo Corpo, Rocael Hernandez Rizzardini, Oscar Rodas, Dhawal Shah, Manoel Cortes-Mendez, Thomas Staubitz, Jose; Ruirez-Valiente
Organizer
v
Related Report
Int'l Joint Research
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