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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84715


    Title: 應用教育大數據與學習分析於改善程式設計自我調節學習之研究;The Research of Applying Educational Big Data and Learning Analytics to Improve Self-Regulated Learning in Programming
    Authors: 楊鎮華
    Contributors: 資訊工程學系
    Keywords: 教育大數據;學習分析;程式設計;自我調節學習;學生學習風險預測;教師早期介入輔導;閱讀行為樣式;Educational big data;learning analytics;programming;self-regulated learning;at-risk students prediction;early intervention;reading patterns
    Date: 2020-12-08
    Issue Date: 2020-12-09 10:46:29 (UTC+8)
    Publisher: 科技部
    Abstract: 本計畫針對教育領域的大量數據資料,以學習分析為其研究主軸,期望以學習分析所揭露的結果來輔助教師教學與提升學生的學習成效。本計畫將針對程式設計課程,探究自我調節學習(Self-Regulation Learning, SRL)對於程式設計課程學習成效之影響。本計畫將在整合型學習環境中,根據所收集的學習行為歷程,透過資料探勘技術來早期識別高風險學生以及萃取學生學習樣式,搭配即時性學習儀表板提供學生學習相關資訊,做為教師輔導干預的參考依據。為達上述目標,本計畫將發展(1)即時性學習儀表板,(2)學習成效預測模型,(3)學習樣式萃取模型等三大學習分析方法。在整合型學習環境下,即時性視覺化學習儀表板主要用以即時呈現學生在課堂學習活動的參與程度情況。本計畫將根據學生學習樣式、學生學習成效的預測結果、學生自我調適學習能力等相關資訊,針對各類型學生提出適當有效的自我調適學習調整建議,藉以進行個人化教學輔導補救措施,實現適性學習或是適性教學的目標,以達到提高學習成效的目標。本計畫為三年期計畫,第一年將進行自主式SRL程式設計翻轉教室的先導研究,將以109學年度本校通識中心開設的Python程式設計課程為研究課程,研究對象約為50位修課學生。接著,本計畫將於第二年將研究對象擴大為110學年度本校所有修習聯合微積分程式設計實習課程的學生,並進行大規模資料驗證分析。最後,計畫第三年則是針對110學年度本校所有修習聯合微積分程式設計實習課程的學生,進行輔導式SRL程式設計翻轉教室,探討基於學習分析結果進行SRL行為調整的輔導活動是否能夠有效的提升學生的學習成效。 ;This project focus on the research topic of learning analysis for a large amount of data in the field of education. It is hoped that the results revealed by learning analysis will assist teachers in teaching and improve students' learning effectiveness. For programming courses, this project will explore the impact of Self-Regulation Learning (SRL) on the learning performance in programming course. This project is based on student learning history collected from an integrated learning environment. It aims to explore student learning patterns and identify high-risk students early, and then provides a real-time learning dashboard to provide teachers with information about student learning and help teachers intervene and guide students adjust their learning. To achieve the above goals, this project will develop (1) real-time learning dashboards, (2) students' learning performance prediction model, and (3) learning patterns extraction model. In an integrated learning environment, a real-time learning dashboard is used to demonstrate student participation in classroom learning activities. According to students learning patterns, the predicted student's learning performance, and students' self-regulation learning capabilities, this project will propose appropriate and effective learning adjustment suggestions for various types of students in order to provide students with personalized intervention to achieve the goal of adaptive learning and then to improve learning performance.The project will carry out the learning activity in an autonomous SRL programming flipped classroom. In the first year, a pilot study of autonomous SRL programming flipped classrooms will be conducted. Participants in the project are students of the Python programming course offered by the National Central University General Education Center in the 109 academic year. This course has approximately 50 students. The second year of the project is a large-scale data validation analysis, and its participants will be extended to all students at the 110 academic year at National Central University who will take Calculus courses and practical training for programming. Finally, in the third year of this project, the participates are the students at the 110 academic year in National Central University who took Calculus and join practical training for programming. They will conduct interventional SRL programming flipped classroom. This project aims to explore whether the intervention activities based on the results of learning analysis to adjust students' SRL ability can be effective Improve students' learning performance.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[資訊工程學系] 研究計畫

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