( 您好!臺灣時間:2021/05/13 02:20
字體大小: 字級放大   字級縮小   預設字形  


研究生(外文):Ching-Hsiang Kang
論文名稱(外文):Using Sequence Clustering to unveil students’ learning strategies and explore their relationship with Cognitive Load and Learning Performance
外文關鍵詞:Cognitive LoadSequence ClusteringLearning StrategyLearning Performance
  • 被引用被引用:0
  • 點閱點閱:43
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
學生在測量認知負荷程度時必須使用問卷進行測量,而認知負荷問卷實務上需要間隔兩周至三周一次進行施測,頻繁地施測也會造成學生的問卷疲勞,且收集問卷後還需要進行統計分析,以至於導師在得知學生的認知負荷負載程度時往往已近學期尾聲,許多干預或輔導措施已不具即時性,難以再進行拯救輔導等等相關措施。除此之外,一些與時間性有關的題目,可能會因為學生在反思過程中記憶模糊或主觀意識影響導致作答有誤,其答案的正確性若能透過線上系統的日誌分析,則會更加精確且可靠。換句話說,如果能夠從學生操作學習管理系統(Learning Management System, LMS)的learning logs就揭露學生認知負荷(cognitive load)的狀況,對於認知負荷的測量是十分即時且快速,且對於教師授課內容的調整、預防以及者干預措施也可獲得即時性的效益。
本研究收集了113位大學生在LMS留下的learning logs,並將learning logs編碼成序列以保持學習歷程的完整性,並將類似的序列分群以檢測學生的學習策略。
分析的結果一共得到五種常見的線上學習策略,並找到一種學習策略可以替代測量(proxy measure)學生的認知負荷程度,本研究同時驗證了學生學習策略與學習成效之間的關聯性,結果表明部分學習策略與學習成效具有正向且顯著的關聯。最後我們得知,學生想要獲得較佳的學習成效,如何正確搭配教材影片是不可或缺的一環,研究發現觀看影片以及做線上測驗的順序是非常重要的,若學生跳過影片直接進行線上測驗,表示學生在課堂中已經累積過多的認知負荷,需要進行即時的調整,反之,若學生正確的使用課程的教材搭配影片學習,循序理解教材內容,對學習是有正向助益的。
Cognitive load must be measured with a questionnaire, but the questionnaire measurement can only conduct once every two or three weeks in practice, and frequent measuring will also cause students are aware of questionnaire fatigue. After collecting the response of the questionnaire, it takes time to perform a statistical test for understanding students' real reflection. Therefore, when teachers know the degree of cognitive load of the students, the semester is almost at the end. Teachers have difficulty giving timely interventions for counseling accordingly. Besides, for some questions related to time in the questionnaire, if students answer through reflection, may lead to wrong answers because of the recall process or subjective consciousness. However, it is more reliable and real-time if we could unveil students' cognitive load by using learning logs generated from the learning management system, and the adjustment of teaching content, interventions and counseling accordingly can also obtain immediate benefits.
In this study, we conducted a system programming course with 113 college students and collected students' learning logs from a learning management system. We encode learning logs into sequences to maintain the integrity of the learning process, and cluster similar learning sequences to detect patterns in students' learning strategies.
Adopting a sequence cluster analysis, we extracted five learning strategies from the collected learning logs. Moreover, experimental results demonstrated that those learning strategies could unveil the degree of cognitive load of students. This study also estimated the correlation relationships between students' learning performance and students' learning strategies. The results present two factors that are under a positive and significant relationship. Furthermore, if students want to get better learning performance, how to properly match the course materials and videos is very important. The study found that the order that students watch videos and take quizzes is a critical factor. Students who skip the videos and take quizzes directly with higher cognitive load in the classroom, and it needs to give intervention immediately. On the contrary, if students correctly use the course materials and videos to learn, and understand the teaching content step by step, it will be of positive help to learning.
摘要 I
目錄 IV
圖目錄 VI
表目錄 VII
一、 緒論 1
1.1 問卷調查分析 1
1.2 學習策略分析 1
二、 文獻探討 3
2.1 問卷與認知負荷 3
2.2 學習策略 4
三、 研究方法與實驗 5
3.1 研究對象 5
3.2 問卷設計 5
3.3.1 資料收集 6
3.3.2 資料前處理及序列化 7 分類事件類別 7 定義序列時間 8 加入章節資訊並合併 8 連續且相同事件類別合併 9 去除離群值 9
3.3.3 序列分群(Sequence Clustering) 10 聚合式階層分群法(Agglomerative hierarchical clustering) 10 連結演算法(Linkage algorithm) 10 序列的相異性度量(Sequence dissimilarity measures) 13 評估指標 13
3.4 相關性分析與差異性分析 14
3.4.1 斯皮爾曼等級相關係數(Spearman’s Rank Correlation Coefficient) 14
3.4.2 獨立樣本t檢定(Independent Sample t test) 15
四、 結果及討論 16
4.1 研究問題1 18
4.2 研究問題2 27
4.3 研究問題3 28
五、 結論與未來研究 32
六、 研究限制 33
七、 參考文獻 34
附錄A 37
Akçapinar, G., Chen, M.-R. A., Majumdar, R., Flanagan, B., & Ogata, H. (2020). Exploring student approaches to learning through sequence analysis of reading logs. Paper presented at the Proceedings of the Tenth International Conference on Learning Analytics & Knowledge.
Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students' self-regulated learning. Metacognition and learning, 9(2), 161-185. Retrieved from ://WOS:000340669000005
Brinton, C. G., Buccapatnam, S., Chiang, M., & Poor, H. V. (2016). Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance. Ieee Transactions on Signal Processing, 64(14), 3677-3692. Retrieved from ://WOS:000377370500010
Chamot, A. U. (2005). Language learning strategy instruction: Current issues and research. Annual review of applied linguistics, 25, 112-130.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology,
Dawson, R. (2011). How significant is a boxplot outlier? Journal of Statistics Education, 19(2).
Gabadinho, A., Ritschard, G., Studer, M., & Müller, N. S. (2009a). Extracting and rendering representative sequences. Paper presented at the International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management.
Gabadinho, A., Ritschard, G., Studer, M., & Müller, N. S. (2009b). Mining sequence data in R with the TraMineR package: A user’s guide. Geneva: Department of Econometrics and Laboratory of Demography, University of Geneva.
Greene, B. A., & Miller, R. B. (1996). Influences on achievement: Goals, perceived ability, and cognitive engagement. Contemporary Educational Psychology, 21(2), 181-192. Retrieved from ://WOS:A1996UL99100005
Jansen, R. S., Van Leeuwen, A., Janssen, J., Kester, L., & Kalz, M. (2017). Validation of the self-regulated online learning questionnaire. Journal of computing in higher education, 29(1), 6-27.
Jovanović, J., Gašević, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33(4), 74-85.
Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of participant behavior in a massive open online course. International Review of Research in Open and Distributed Learning: IRRODL, 18(6), 1-18.
Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., & Adesope, O. (2015). Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions. The Internet and Higher Education, 27, 74-89.
Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior, 80, 179-196.
Mayer, R. E. (1988). Learning strategies: An overview learning and study strategies. Edited by CE Weinstein, ET Goetz and PE Alexander. Newyork: academic Pres, Inc.
Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The computer journal, 26(4), 354-359.
Neuville, S., Frenay, M., & Bourgeois, E. (2007). Task value, self-efficacy and goal orientations: Impact on self-regulated learning, choice and performance among university students. Psychologica Belgica, 47(1-2), 95-117. Retrieved from ://WOS:000250483400007
Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of educational psychology, 84(4), 429.
Paas, F. G. W. C., Vanmerrienboer, J. J. G., & Adam, J. J. (1994). Measurement of Cognitive Load in Instructional-Research. Perceptual and Motor Skills, 79(1), 419-430. Retrieved from ://WOS:A1994PJ03800012
Pask, G., & Scott, B. (1972). Learning strategies and individual competence. International Journal of Man-Machine Studies, 4(3), 217-253.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology, 82(1), 33.
Porter, S. R., Whitcomb, M. E., & Weitzer, W. H. (2004). Multiple surveys of students and survey fatigue. New directions for institutional research, 2004(121), 63-73.
Steif, P. S., & Dollár, A. (2009). Study of usage patterns and learning gains in a web‐based interactive static course. Journal of Engineering Education, 98(4), 321-333.
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and instruction, 4(4), 295-312.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational psychology review, 10(3), 251-296.
Tuan*, H. L., Chin, C. C., & Shieh, S. H. (2005). The development of a questionnaire to measure students' motivation towards science learning. International journal of science education, 27(6), 639-654.
Winne, P. H. (2006). How software technologies can improve research on learning and bolster school reform. Educational Psychologist, 41(1), 5-17.
電子全文 電子全文(網際網路公開日期:20250629)
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔