臺灣博碩士論文加值系統

由於資訊和通信技術的快速發展，行動裝置設備變得非常普遍，因此也產生了許多網路上的學習課程，這意味者獲取知識是隨手可得的。例如：國立中央大學的系統Open edX，是為了提供學生能夠即時且不因地區限制，隨時隨地的學習或做課程的複習、討論等，使學生可以透過開放式的教學平台有效提升學習成效。從教師的觀點而言，可以將學生的學習過程實質的歸納或統計成為數據，然後對此學生的學習資訊進行更有效率的分析。
在本研究中，我們設計了一個基於Lag-sequential Analysis、K-means Clustering和Hierarchical Agglomerative Clustering的分析程序。我們的目標是透過使用Open edX系統紀錄中選定的特定班級學生操作紀錄，作為主要的分析樣本。
應用所設計的分析程序對於萃取高學習成效和低學習成效之間的學習差異非常有效，因此教師可利用此方法協助低學習成效的學生在學習過程中的早期階段避免學習遇到的困難。

Owing to the rapid development of Information and Communication Technology, mobile devices became extremely popular, and therefore, it is convenient to take a e-learning course. For example, in National Central University, the system “Open edX” is provided for students for ubiquitous learning. From a teacher’s viewpoint, he/she can digitize the learner behavior and then make analytics. In this thesis, we design a procedure of analytics based on Lag-sequential Analysis, K-means Clustering, and Hierarchical Agglomerative Clustering. We aim at clustering students who were attending courses by using Open edX. In particular, The procedure of analytics is useful for discovering the distinction between low and high performance. As a result, teachers can help the low-performance students to overcome barriers in an early stage.

摘要 i
ABSTRACT ii
致謝 iii
目錄 iiv
圖目錄 vi
表目錄 viii
1 緒論 1
1.1研究背景與動機 1
1.2研究目的 1
1.3問題研究 2
2 文獻探討 3
2.1方法探討 3
2.2序列分析(Sequential Analysis) 3
2.2.1滯後序列分析(Lag-sequential Analysis, LSA)介紹 3
2.3集群分析(Clustering) 4
2.3.1叢集式分群分析(K-means Clustering, K-means)介紹 4
2.3.2階層分群法(Hierarchical Agglomerative Clustering, HAC)介紹 5
3 研究方法 6
3.1資料收集 6
3.1.1資料介紹 6
3.1.2資料內容 6
3.2資料分析流程 7
3.2.1資料紀錄 7
3.2.2資料狀態定義 10
3.2.3資料狀態架構 12
3.3資料分析 14
3.3.1找出整體學習序列 14
3.3.2區分整體學習模式 15
3.3.3各群學習模式分析 15
4 實驗結果與討論 16
4.1步驟1：尋找整體學習模式 16
4.2步驟2：整體學習模式分群 26
4.3步驟3：萃取群集學習模式 28
4.4步驟4：衍生探討 31
5 結論、研究限制及未來研究 39
5.1結論 39
5.2未來研究 39
參考文獻 41

Celonis-Tools.https://www.celonis.com/intelligent-business-cloud(retrieved: 2019/01/15).
Lag-Sequential-Analysis.http://blog.pulipuli.info/2017/10/behavior-analysis-lag-sequential.html(retrieved: 2018/12/10).
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2016). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29.
Chien, T. C., Chen, Z. H., & Chan, T. W. (2017). Exploring long-term behavior patterns in a book recommendation system for reading. Journal of Educational Technology & Society, 20(2), 27-36.
Hou, H. T. (2011). A case study of online instructional collaborative discussion activities for problem-solving using situated scenarios: An examination of content and behavior cluster analysis. Computers & Education, 56(3), 712-719.
Hou, H. T. (2012). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233.
Hwang, G. J., Hsu, T. C., Lai, C. L., & Hsueh, C. J. (2017). Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns. Computers & Education, 106, 26-42.
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.
Kang, J., Liu, M., & Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72, 757-770.
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & education, 104, 18-33.
Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286-297.
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.
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.
Tempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408-420.
Yang, T. C., Chen, S. Y., & Hwang, G. J. (2015). The influences of a two-tier test strategy on student learning: A lag sequential analysis approach. Computers & Education, 82, 366-377.