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題名 運用學習管理系統資料進行學生學習行為之資料探勘
Data Mining of Student Learning Behaviors using Learning Management System Data
作者 林侑萱
Lin, Yu-Hsuan
貢獻者 江玥慧
Chiang, Yueh-hui
林侑萱
Lin, Yu-Hsuan
關鍵詞 學習管理系統
教育資料探勘
程式碼分群
學習行為模式
學習成效
Learning Management System
Educational Data Mining
Program Code Grouping
Learning Behavior
Learning Effectiveness
日期 2024
上傳時間 5-Aug-2024 12:45:17 (UTC+8)
摘要 除了在教學現場能夠觀察到學生在課堂上的學習情形，數位教學平台中的日誌資料也隱含了學生學習行為的相關訊息。而本研究希望使用Moodle平台所蒐集的資料，從三種角度探討基礎程式設計課程的學生學習狀況，首先，透過學生的程式作業來觀察學生的共同撰寫模式，以幫助教學者了解學生可能會出現的程式錯誤以及有哪些寫法，再來，透過日誌資料，找尋程式撰寫及小組合作需要協助的學生，在課程上的行為模式，透過這些行為模式能幫助教學者找到這些學生，並多加關注這些學生在程式撰寫上及小組活動上是否需要幫助，最後，找出學習管理系統上可能影響學生學習成效的因素，幫助教學者提前觀察到學生後續可能的學習成效。這些研究結果可以協助教學者了解學生在學習上的行為，以提供學生更適合的幫助。
In addition to observing students' learning in the classroom at the teaching site, the log data in learning management system also contains information about students' learning behaviors. This study analyzes data collected from the Moodle platform to achieve three objectives: to explore the differences in programming approaches among students in programming courses, to examine the behavior patterns of students who need assistance with programming and group work, and to identify the factors that influence worksheet performance. These findings can help educators understand student learning behaviors better and provide more suitable support for students.
參考文獻 [1]N.Ademi, S.Loshkovska and S.Kalajdziski, "Prediction of Student Success Through Analysis of Moodle Logs: Case Study ", ICT Innovations 2019. Big Data Processing and Mining. Communications in Computer and Information Science, vol 1110. Springer, Cham, 2019. [2]A.Namoun and A.Alshanqiti, "Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review ". Applied Sciences.11(1):237, 2021 [3]G.Yang et al., "ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation", 8th International Conference on Dependable Systems and Their Applications (DSA), Yinchuan, China, 2021, pp. 30-41, 2021 [4]P.Wang,L.Zhu,Q.Wang,O.Jaiteh and C.Guo,"An Empirical Understanding of Code Clone Detection by ChatGPT", 2023 6th International Conference on Data Science and Information Technology (DSIT), Shanghai, China, pp. 78-83, 2023 [5]林世唐, "學生程式碼相似度之研究—以抄襲偵測之應用為例",〔碩士論文，淡江大學〕,華藝線上圖書館,2005 [6]H.Aldriye, A.AlKhalaf and M.Alkhalaf, "Automated Grading Systems for Programming Assignments: A Literature Review " .International Journal of Advanced Computer Science and Applications .2019 [7]蔡明志、陳思豪、曾宣瑜、胡俊之,"程式抄襲源頭偵測之研究"。《輔仁管理評論》 26卷3期 p. 27-50,2019 [8]R.Maertens,C.V.Petegem,N.Strijbol,T.Baeyens,A.C.Jacobs,P.Dawyndt and B.Mesuere, "Dolos: Language-agnostic plagiarism detection in source code ", Journal of Computer Assited learning,Volume38, Issue4,2022 [9]F.Nielsen,"Hierarchical Clustering ",Introduction to HPC with MPI for Data Science pp 195–211,2016 [10]M.Valle Torre, C.Oertel and M.Specht,"The Sequence Matters in Learning-A Systematic Literature Review",In Proceedings of the 14th Learning Analytics and Knowledge Conference, Pages 263-272, March,2024 [11]A.Tlili,T.Sun,M.Denden,Kinshuk,S.Graf,C.Fei and H.Wang, "Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach ",Frontiers in psychology, 14, 1071985,2023 [12]K.Stefanova and D.Kabakchieva, "Educational data mining perspectives within university big data environment", 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), Madeira, Portugal, pp. 264-270, 2017 [13]V.Mandalapu, et al. "Student-Centric Model of Login Patterns: A Case Study with Learning Management Systems", International Educational Data Mining Society ,2021 [14]J.W.You, "Identifying significant indicators using LMS data to predict course achievement in online learning", The Internet and Higher Education,Volume 29, April 2016, Pages 23-30,2015 [15]A.S.Aljaloud ,D.M. Uliyan, A.Alkhalil, M.Abd Elrhman, A. F.M.Alogali, Y.M.Altameemi and P.Kwan, "A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM", IEEE Access, vol. 10, pp.85255-85265, 2022 [16]P.Resta, C.Awalt and M.Menchaca, "Self and peer assessment in an online collaborative learning environment ", E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 682-689), Association for the Advancement of Computing in Education (AACE),2002 [17]M.J.Tsai, C.Y.Wang and P.F.Hsu, "Developing the computer programming self-efficacy scale for computer literacy education ", Journal of Educational Computing Research, 56(8), 1345-1360,2019
描述 碩士
國立政治大學
資訊科學系
111753121
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111753121
資料類型 thesis
dc.contributor.advisor 江玥慧zh_TW
dc.contributor.advisor Chiang, Yueh-huien_US
dc.contributor.author (Authors) 林侑萱zh_TW
dc.contributor.author (Authors) Lin, Yu-Hsuanen_US
dc.creator (作者) 林侑萱zh_TW
dc.creator (作者) Lin, Yu-Hsuanen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 12:45:17 (UTC+8)-
dc.date.available 5-Aug-2024 12:45:17 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 12:45:17 (UTC+8)-
dc.identifier (Other Identifiers) G0111753121en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152569-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 111753121zh_TW
dc.description.abstract (摘要) 除了在教學現場能夠觀察到學生在課堂上的學習情形，數位教學平台中的日誌資料也隱含了學生學習行為的相關訊息。而本研究希望使用Moodle平台所蒐集的資料，從三種角度探討基礎程式設計課程的學生學習狀況，首先，透過學生的程式作業來觀察學生的共同撰寫模式，以幫助教學者了解學生可能會出現的程式錯誤以及有哪些寫法，再來，透過日誌資料，找尋程式撰寫及小組合作需要協助的學生，在課程上的行為模式，透過這些行為模式能幫助教學者找到這些學生，並多加關注這些學生在程式撰寫上及小組活動上是否需要幫助，最後，找出學習管理系統上可能影響學生學習成效的因素，幫助教學者提前觀察到學生後續可能的學習成效。這些研究結果可以協助教學者了解學生在學習上的行為，以提供學生更適合的幫助。zh_TW
dc.description.abstract (摘要) In addition to observing students' learning in the classroom at the teaching site, the log data in learning management system also contains information about students' learning behaviors. This study analyzes data collected from the Moodle platform to achieve three objectives: to explore the differences in programming approaches among students in programming courses, to examine the behavior patterns of students who need assistance with programming and group work, and to identify the factors that influence worksheet performance. These findings can help educators understand student learning behaviors better and provide more suitable support for students.en_US
dc.description.tableofcontents 第1章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 1 1.3 研究問題 2 1.4 論文架構 3 第2章 文獻探討 4 2.1 程式碼比對工具 4 2.2 Hierarchical Clustering（階層式分群法） 6 2.3 序列分析 7 2.4學習管理系統上影響學習成效之因素 8 第3章 研究方法 11 3.1 數據收集與研究樣本 11 3.2 研究架構 11 3.3 實驗(一) 探討學生程式作業撰寫模式 12 3.3.1 實驗資料 12 3.3.2 資料篩選及產生相似度資料後分群 13 3.4 實驗(二)學生在課程上的行為模式 14 3.4.1實驗資料 14 3.4.2資料處理 14 3.4.3 課程活動 17 3.4.4 行為編碼 19 3.4.5 序列分析 21 3.5實驗(三) 影響學生學習單分數之因素 21 3.5.1 實驗資料 21 3.5.2 資料前處理 22 3.5.3 建立模型 23 3.5.4評估指標 23 第4章 研究結果與討論 24 4.1 學生程式撰寫模式 24 4.1.1 分群作業中，輪廓係數較高者之撰寫模式 25 4.1.2 分群作業中，輪廓係數較低者之撰寫模式 39 4.2 學生在課程上的行為模式 49 4.2.1 程式撰寫需要協助的學生與多數學生在課程上的行為差異 49 4.2.2個人學習表現佳但合作表現不佳的學生與多數學生在課程上的行為差異 50 4.3 影響學生學習單成績的因素 55 第5章 結論 56 參考文獻 59 附錄一 62 附錄二 66 附錄三 71zh_TW
dc.format.extent 5301402 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111753121en_US
dc.subject (關鍵詞) 學習管理系統zh_TW
dc.subject (關鍵詞) 教育資料探勘zh_TW
dc.subject (關鍵詞) 程式碼分群zh_TW
dc.subject (關鍵詞) 學習行為模式zh_TW
dc.subject (關鍵詞) 學習成效zh_TW
dc.subject (關鍵詞) Learning Management Systemen_US
dc.subject (關鍵詞) Educational Data Miningen_US
dc.subject (關鍵詞) Program Code Groupingen_US
dc.subject (關鍵詞) Learning Behavioren_US
dc.subject (關鍵詞) Learning Effectivenessen_US
dc.title (題名) 運用學習管理系統資料進行學生學習行為之資料探勘zh_TW
dc.title (題名) Data Mining of Student Learning Behaviors using Learning Management System Dataen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1]N.Ademi, S.Loshkovska and S.Kalajdziski, "Prediction of Student Success Through Analysis of Moodle Logs: Case Study ", ICT Innovations 2019. Big Data Processing and Mining. Communications in Computer and Information Science, vol 1110. Springer, Cham, 2019. [2]A.Namoun and A.Alshanqiti, "Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review ". Applied Sciences.11(1):237, 2021 [3]G.Yang et al., "ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation", 8th International Conference on Dependable Systems and Their Applications (DSA), Yinchuan, China, 2021, pp. 30-41, 2021 [4]P.Wang,L.Zhu,Q.Wang,O.Jaiteh and C.Guo,"An Empirical Understanding of Code Clone Detection by ChatGPT", 2023 6th International Conference on Data Science and Information Technology (DSIT), Shanghai, China, pp. 78-83, 2023 [5]林世唐, "學生程式碼相似度之研究—以抄襲偵測之應用為例",〔碩士論文，淡江大學〕,華藝線上圖書館,2005 [6]H.Aldriye, A.AlKhalaf and M.Alkhalaf, "Automated Grading Systems for Programming Assignments: A Literature Review " .International Journal of Advanced Computer Science and Applications .2019 [7]蔡明志、陳思豪、曾宣瑜、胡俊之,"程式抄襲源頭偵測之研究"。《輔仁管理評論》 26卷3期 p. 27-50,2019 [8]R.Maertens,C.V.Petegem,N.Strijbol,T.Baeyens,A.C.Jacobs,P.Dawyndt and B.Mesuere, "Dolos: Language-agnostic plagiarism detection in source code ", Journal of Computer Assited learning,Volume38, Issue4,2022 [9]F.Nielsen,"Hierarchical Clustering ",Introduction to HPC with MPI for Data Science pp 195–211,2016 [10]M.Valle Torre, C.Oertel and M.Specht,"The Sequence Matters in Learning-A Systematic Literature Review",In Proceedings of the 14th Learning Analytics and Knowledge Conference, Pages 263-272, March,2024 [11]A.Tlili,T.Sun,M.Denden,Kinshuk,S.Graf,C.Fei and H.Wang, "Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach ",Frontiers in psychology, 14, 1071985,2023 [12]K.Stefanova and D.Kabakchieva, "Educational data mining perspectives within university big data environment", 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), Madeira, Portugal, pp. 264-270, 2017 [13]V.Mandalapu, et al. "Student-Centric Model of Login Patterns: A Case Study with Learning Management Systems", International Educational Data Mining Society ,2021 [14]J.W.You, "Identifying significant indicators using LMS data to predict course achievement in online learning", The Internet and Higher Education,Volume 29, April 2016, Pages 23-30,2015 [15]A.S.Aljaloud ,D.M. Uliyan, A.Alkhalil, M.Abd Elrhman, A. F.M.Alogali, Y.M.Altameemi and P.Kwan, "A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM", IEEE Access, vol. 10, pp.85255-85265, 2022 [16]P.Resta, C.Awalt and M.Menchaca, "Self and peer assessment in an online collaborative learning environment ", E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 682-689), Association for the Advancement of Computing in Education (AACE),2002 [17]M.J.Tsai, C.Y.Wang and P.F.Hsu, "Developing the computer programming self-efficacy scale for computer literacy education ", Journal of Educational Computing Research, 56(8), 1345-1360,2019zh_TW