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以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：149 、訪客IP：3.138.125.2
姓名 施博文(Po-Wen Shih) 查詢紙本館藏 畢業系所 資訊工程學系在職專班 論文名稱 透過序列探勘分析學習影片瀏覽操作對於學習成效的影響
(Exploring the influence of video viewing action on learning performance by using sequence mining.)相關論文 檔案 [Endnote RIS 格式]
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至系統瀏覽論文 (2024-7-17以後開放)
摘要(中) 透過分析高成就以及低成就在學習模式上的差異，找出何種學習模式對於成績的提升有幫助。
摘要
隨著技術的進步，互聯網越來越受歡迎。近年來，在線教育機會不斷增長。與傳統研究不同，在線學習提供了極大的靈活性和便利性。 首先，您不必浪費時間和金錢去校園。您還可以選擇一個完全符合您興趣和需求的計劃，因為您不僅限於您所在地區提供的課程。由於學生在操作磨課師平台上，有著多種操作方式，如觀看影片、觀看文件、測驗考試…等。這些行為都會被系統紀錄下來，若是僅紀錄而未分析，則對於這些資訊過於浪費。而最常被學生使用的功能是觀看影片的行為，因此透過觀察學生在操作磨課師教學平台上觀看影片的操作，試著解析學生的操作學習模式，是否會影響到學生考試成績，並希望藉著此分析系統，達到可於期中便可預測到學生未來成績並提供學生更良好的觀看影片的方法，以達到督促增進學生成績的功能。本研究透過相關係數分析、多因子分析、探索性因素分析與滯後序列分析四種學習分析法，來找出對學生學習成績具有影響力的關鍵觀看學習模式。其中，透過單因子共變異分析分析影片參與程度對於成績是否有影響，利用滯後序列分析找出高低成就學生的觀看學習模式的差異，透過相關係數分析找出不同學習行為對於學生成就的影響。利用探索性因素分析將找出的萃取出不同面向，找出學習模式，利用多因子分析找出對整體學生成績最具影響性的觀看模式；而透過本研究所提供之分析結果，教師可參考而對學習狀況較差之學生進行輔導，避免學生因遇到學習困難而放棄學習。
關鍵字：學習分析、磨課師、影片瀏覽操作分析、相關係數分析、單因子共變異分析、探索性因素分析、滯後序列分析、多因子分析
摘要(英) From Lag-sequential Analysis, by analyzing the differences in learning behavior between high achievement and low achievement, it is helpful to find out which learning behavior is helpful for improvement.
ABSTRACT
With advances in technology, Internet has gained become more popular. Online education opportunities have been growing in recent years. Unlike traditional studies, online learning offers great flexibilities and convenience. To begin with, you don′t have to waste time and money traveling to the campus. You can also select a program that entirely fits with your interests and needs because you are not restricted to the classes that are offered in your area. Because students can use many functions in Massive Open Online Courses (MOOCs), such as watching movies, watching documents, quizzing exams, fill in questionnaire, etc. These behaviors are recorded by the MOOCs system. If they are only recorded but not analyzed, they are too wasteful. The most commonly used behavior is watching a learning video. Therefore, this method observes the operation of the students watching the learning video on the MOOCs and try to analyze whether the student′s operational learning model will affect the student′s test scores.
This research uses Correlation coefficient, Multiple Factor analysis, Exploratory factor analysis s and Lag-sequential Analysis to find out the key viewing learning modes that have an impact on students′ learning performance. Among them, Lag Sequential Analysis is used to find out the difference between the learning behavior between high and low achievement students and the influence of difference learning performance is found through by Correlation coefficient. Use Exploratory factor analysis to reduce the number of patterns found and find out the learning mode. Use Multiple correspondence analysis to find the most influential viewing mode patterns for overall student performance. Use the analysis results provided by the research, teachers can find out the students who have poor learning patterns and help them to catch up with the learning schedule.
Keywords: learning analysis, MOOCs, Video learning behavior, Correlation Coefficient analysis, ANCOVA, Lag Sequential Analysis, Exploratory Factor Analysis, Multi Factor Analysis
關鍵字(中) ★ 磨課師
★ 影片瀏覽操作分析
★ 相關係數分析
★ 單因子共變異分析
★ 探索性因素分析
★ 滯後序列分析關鍵字(英) ★ MOOCs
★ Video learning behavior
★ Correlation Coefficient analysis
★ ANCOVA
★ Lag Sequential Analysis
★ Exploratory Factor Analysis論文目次 目錄
摘要 i
ABSTRACT ii
致謝 iv
目錄 v
圖目錄 viii
表目錄 ix
1 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
2 文獻探討 3
2.1 磨課師的優勢與劣勢 3
2.2選取學習分析影片觀看行為之定義 3
2.3分析學習行為對於不同群體之影響 4
2.4滯後序列分析(Lag-sequential Analysis, LSA)的學習分析應用 6
2.4.1滯後序列分析介紹 6
2.4.2滯後序列步驟（Lag Sequential Analysis Step） 6
3 方法設計 8
3.1分析流程 8
3.1資料倉儲階段 8
3.2.1系統資料來源 8
3.2.2學生觀看記錄 9
3.3資料預處理階段 10
3.3.1定義學生觀看行為編碼 10
3.3.2將觀看影片紀錄轉換為行為序列 11
3.4建立分析模型階段 12
4 實驗結果與討論 14
4.1分析學生影片參與率對於成績影響 14
4.1.1 分析學生影片參與率觀察差異統計結果 14
4.1.2 影片參與率對於d學習有著正面效果 16
4.2從分析學生行為序列找出顯著行為 17
4.2.1分析學生行為找出學生觀看影片的顯著行為 17
4.2.1.1 定義滯後序列分析影片操作行為 17
4.2.2滯後序列分析應用於學生學習分析 18
4.2.3透過觀察高低分族群找出行為之間的差異 23
4.2.3.1觀察長短序列（Sequence）的行為 27
4.2.3.2觀察基序（Motif）的行為 28
4.2.4找出基序（Motif）與序列（Sequence）對成績的相關性 31
4.2.4.1分析序列（Sequence）之相關係數 31
4.2.4.2分析基序（Motif）之相關係數 32
4.2.5從觀察學生線上學習影片觀看中可以找到觀看模式和序列 33
4.3從學生觀看影片的行為中萃取出觀看模式 34
4.3.1 萃取影片觀看模式 34
4.3.1.1基序（Motif）合併規則 34
4.3.1.2 序列(sequence)選擇規則 37
4.3.2從學生學習行為中萃取學習模式 39
4.3.2.1 探索性因子篩選條件 39
4.3.2.2 探索性因子篩選結果 39
4.3.2.3 探索性因子萃取結果 42
4.3.3 從線上學習影片觀看行為中找出學習模式 46
4.4觀察學生觀看影片的模式，找出對於成績的關聯性 46
5結論與未來展望 47
參考文獻 48參考文獻 Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge university press.
Blom, J., Verma, H., Li, N., Skevi, A., & Dillenbourg, P. (2013). MOOCs are more social than you believe (No. REP_WORK). eLearning Papers.
Cabrera, N., & Ferrer, M. F. (2017). Examining MOOCs: A comparative study among educational technology experts in traditional and open universities. The International Review of Research in Open and Distributed Learning, 18(2).
Conglei Shi, Siwei Fu, Qing Chen and Huamin Qu. Visualizing Video Clickstream Data from Massive Open Online Courses. IEEE Pacific Visualization Symposium,2015.
Daradoumis, T., Bassi, R., Xhafa, F., & Caballé, S. (2013, October). A review on massive e-learning (MOOC) design, delivery and assessment. In 2013 eighth international conference on P2P, parallel, grid, cloud and internet computing(pp. 208-213). IEEE.
Dissanayake, D., Perera, T., Elladeniya, C., Dissanayake, K., Herath, S., & Perera, I. (2018). Identifying the learning style of students in MOOCs using video interactions. International Journal of Information and Education Technology, 8(3).
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological methods, 4(3), 272.
Guo, P., Kim, J., & Rubin, R. (2014). How MOOC video production affects student engagement.
Hou, H. T. (2009). A Framework for Dynamic Sequential Behavioral Pattern Detecting and Automatic Feedback/Guidance Designing for Online Discussion Learning Environments. In Advanced Learning. IntechOpen.
Hou, H. T., Sung, Y. T., & Chang, K. E. (2009). Exploring the behavioral patterns of an online knowledge-sharing discussion activity among teachers with problem-solving strategy. Teaching and Teacher Education, 25(1), 101-108.
Hou, H. T., Wu, S. Y., Lin, P. C., Sung, Y. T., Lin, J. W., & Chang, K. E. (2014). A blended mobile learning environment for museum learning. Journal of Educational Technology & Society, 17(2), 207-218.
Hwang, G. J., Chu, H. C., & Yin, C. (Eds.). (2018). Learning Analytics. Routledge.
Lee, S. Y., Chae, H. S., & Natriello, G. (2018). Identifying User Engagement Patterns in an Online Video Discussion Platform. International Educational Data Mining Society.
Onah, D. F., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: behavioural patterns. EDULEARN14 proceedings, 1, 5825-5834.
Osborne, J. W., Costello, A. B., & Kellow, J. T. (2008). Best practices in exploratory factor analysis. Best practices in quantitative methods, 86-99.
Pohl, M., Wallner, G., & Kriglstein, S. (2016). Using lag-sequential analysis for understanding interaction sequences in visualizations. International Journal of Human-Computer Studies, 96, 54-66. (LSA)
Shi, C., Fu, S., Chen, Q., & Qu, H. (2015, April). Vismooc: Visualizing video clickstream data from massive open online courses. In 2015 IEEE Pacific visualization symposium (PacificVis) (pp. 159-166). IEEE.
Thompson, B. (2007). Factor analysis. The Blackwell Encyclopedia of Sociology.
Wu, A. D., Li, Z., & Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment, Research & Evaluation, 12(3), 1-26. (MFA)
Wu, S. Y., Hou, H. T., Hwang, W. Y., & Liu, E. Z. F. (2013). Analysis of learning behavior in problem-solving-based and project-based discussion activities within the seamless online learning integrated discussion (SOLID) system. Journal of Educational Computing Research, 49(1), 61-82.
王國忠. (2006). 學生學習意願與學習困擾因素之探討-以高雄市某私立高職汽車為例 (未出版之碩士論文). 義守大學, 高雄市.
孫怡君. (2016). 採用網路翻轉教學優缺點之探究. 中山大學資訊管理學系研究所學位論文, 1-128.
唐偉庭. (2017). 應用資料視覺化技術於 Moodle 數位學習平台之研究. 宜蘭大學資訊工程研究所學位論文, (2017 年), 1-45.
張義松. (2014). 數位學習平台應用於國小數學科學習成效之探究—以國小五年級分數單元為例. 虎尾科技大學資訊管理研究所在職專班學位論文, 1-84.
廖偉翔. (2017). 以資料視覺化傳達課程目的應用於磨課師課程, 探討學習動機, 學習滿意度與自我效能分析-以生活中無所不在的物理課程為例. 宜蘭大學資訊工程研究所學位論文, 1-78.
指導教授 楊鎮華(Chen-Hua Yang) 審核日期 2019-7-19 推文 plurk
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