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研究生:馬肇亨
研究生(外文):Zhao-Heng Ma
論文名稱:探討結合自動評分的機器學習為基礎之同儕導師推薦系統與其對學習影響之評估
論文名稱(外文):An Investigation of the effects of Machine Learning-based Peer Tutor Recommender and Automated Assessment System
指導教授:施國琛施國琛引用關係黃武元黃武元引用關係
學位類別:博士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:70
中文關鍵詞:做中學策略同儕導師同儕教學推薦系統自動評估系統機器學習電腦視覺
外文關鍵詞:Learning by doingPeer TutoringRecommender systemAutomated assessment systemMachine learningComputer vision
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從實作中學習在不同階段的學校都是非常重要的教學方式,通常會會分為教學與練習評估兩個階段,教學階段由教師示範操作、接著給學生練習並評估學生的作業。計算機課程在技術型高中普遍都必須修習,此課程對於學生日後撰寫小論文、製作報告及畢業求職都是很非常有用的。在教學現場,通常用廣播教學進行示範,但在練習評估階段,學習落後的學生往往缺乏協助而無所適從。教師面對同時需要協助的學生及檢查不同學生的作業是分身乏術。同儕學習是有效且常應用於不同教學的策略,透過同儕之間的互助達到提升學習成效的目的。但在傳統配對策略是程度好的學生擔任同儕導師(tutor)程度差的學生當學員(tutee)進行輔導,但只按成績分組往往沒有考慮到學生的社群關係而影響成效。研究者發展一套完整的系統,包含兩個模組提供教師檢查課堂作業及安排同儕學習的功能,提升學習成效。第一個模組使用本研究之自動評估系統,以電腦視覺技術直接從螢幕上判斷學生作答是否正確,若有錯誤給予說明提示,第二個模組為同儕導師推薦系統,我們以學生的社群關係問卷、學習成效及推薦同儕導師的回饋,透過機器學習列出推薦的同儕導師,再指派同儕導師去輔導提出申請的學員。研究結果證明對於學生的學習成效有顯著提升,系統推薦導師能考慮到同儕關係與實作能力。本研究可提供技術型高中電腦應用軟體實作課程教學及同儕教學系統設計的參考方向。
In vocational high schools, many information technology courses frequently use the learning by doing strategy. Particularly learning computer application operating skills is essential for students because excellent computer application operating skills can help them attain good jobs. However, when fostering students’ computer application operating skills by teaching in vocational high school using the learning by doing strategy, a teacher learns that helping all students, evaluating their learning problems, and providing feedback to correct their mistakes are challenging. After investigating the challenge, a machine learning-based peer tutor recommender system (MPTRS) with automated assessment was proposed to enhance students’ learning performance in computer application operating skills. The advanced automated assessment system (AAS) used computer vision technology to evaluate student assignments and instantly return feedback. The recommender mechanism of the MPTRS enhanced mutual help among students based on their social relationships, learning performance, and recommender feedback. Furthermore, machine learning techniques were used to improve recommenders. In the experiment, the experimental group used the proposed system, and the control group used a conventional commercial automated grading system. From the experimental results, the learning performance of the experimental group significantly improved between the pretest and post-test. Students can correct and complete more assignments by using the advanced AAS and students who behind in learning also can use the peer tutor recommender function for asking help. Participants were also satisfied with the proposed advanced AAS and MPTRS. It is worth to promote the proposed system to teachers of adopting the learning-by-doing strategy in computer application classes.
摘 要 i
Abstract iii
誌 謝 v
Table of Contents vi
List of Tables viii
List of Figures ix
1. Introduction 1
1.1 Background 1
1.2 Study objectives and questions 4
2. Related works 6
2.1 Peer tutoring strategy 6
2.1.1 Classwide peer tutoring 7
2.1.2 Reciprocal peer tutoring 7
2.1.3 Fixed-role peer tutoring 8
2.1.4 Social relationships in classroom 9
2.1.5 Computer-aided system 9
2.2 Recommender systems 10
2.2.1 Machine learning methods 11
2.2.2 Collaborative Filtering methods 11
2.2.3 Recommender module in Keras 12
2.2.4 Recommender system in education 13
2.3 Automated grading systems 13
2.3.1 Automated assessment technologies 14
2.3.2 Computer vision technology 15
3. System design and implementation 20
3.1 System architecture 20
3.2 Advanced automated assessment module 21
3.3 Peer Tutor Recommender module in Keras 27
4. Methodology 33
4.1 Research design 33
4.2 Participants 33
4.3 Instruments 33
4.4 Data collection procedure 34
4.5 Data Analysis 35
5. Results and discussion 36
5.1 Result of learning performance 36
5.2 The relationship between uploaded assignment times and performance 37
5.3 The comparison between tutors and tutees 38
5.4 The peer tutor recommender quality 39
5.5 Questionnaire results 42
5.6 Social relationships among students 44
6. Conclusion 47
7. Reference 50
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