應用關聯規則提取閱讀行為並探索與學習成效的關係

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研究生：	李威賜 Wei-Tzu Lee
論文名稱：	應用關聯規則提取閱讀行為並探索與學習成效的關係 Apply association rules to extract reading behaviors and explore the relationship with academic performance.
指導教授：	楊鎮華 Stephen J.H. Yang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系在職專班 Executive Master of Computer Science & Information Engineering
論文出版年：	2020
畢業學年度：	108
語文別：	中文
論文頁數：	63
中文關鍵詞：	線上學習、電子書、先驗演算法、滑動視窗、關聯規則、學習樣式
外文關鍵詞：	BookRoll, online learning, eBook, Apriori, sliding window, association rules
相關次數：	點閱：25 下載：0
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近年來，線上學習平台日益盛行，線上課程的多元化與豐富程度，學生要學習新知識，並不再侷限於面對面的課程。線上學習平台在國外已經相當的盛行，如Coursera、edX，以及美國知名學校麻省理工學院 (Massachusetts Institute of Technology, MIT) 也都擁有線上學習課程，而國內教育目前也積極地推廣，如Moocs磨課師學習平台，NCUX以及BookRoll線上電子書學習系統等等。
以BookRoll學習平台為例，授課教師將教材轉換成電子書並上傳至平台上，學生上課則會透過平台去閱讀教師準備的教材，來達到學習的目的。BookRoll會記錄每一位學生的學習歷程，但這些資訊並不容易讓授課教師直接掌握每一位學生的學習狀態，來瞭解學生的實際學習狀況，以及時給予協助。
因此本研究分析建立於BookRoll線上學習平台，分析學生學習時，所記錄的歷程與動作。透過方法論，將學習歷程轉換為學習序列，經由統計的方法，分析每位學生的學習行為與動作，來探討其與學習成效之間的關聯性。並藉由Apriori演算法，使用滑動視窗搭配關聯規則分析，尋找高分群學生共同的良好學習樣式，及這些學習樣式對學習成就的影響。

In this few years, online learning, or virtual classes offered is getting more popular, because online learning environments provide a greater degree of flexibility than traditional classroom settings and online platforms can also offer more diverse representations of student populations as learners.
In Taiwan, online learning environments are also promoted positively, such as TAIWANMOOC、NCUX and BookRoll, BookRoll is an online eBook learning system, Teachers can convert course contents into online e-books, and BookRoll can collect the reading logs of students.
This study is to analyze reading logs on BookRoll, use statistical methods to find out the relationship between reading actions and learning performance, and explore the learning patterns via Apriori algorithm, to use sliding window and association rules to find out which learning patterns are great behaviors on learning.

摘        要............................I
ABSTRACT................................II
致        謝............................III
目        錄............................IV
圖   目   錄............................VI
表   目   錄............................VII
一、緒  論..............................1
1.1 研究背景............................1
1.2 研究動機............................1
1.3 研究目的............................2
二、文獻探討............................3
2.1 情節探勘............................3
2.2 關聯規則探勘........................4
2.3 情節探勘與關聯規則的應用............5
三、研究內容與方法......................7
3.1 課程描述............................7
3.2 資料集描述..........................8
3.3 學習模式偵測流程....................9
3.3.1 預處理階段........................9
3.3.2 學習模式檢測階段..................12
四、研究結果與討論......................15
4.1 探索閱讀動作和物件與學習成效的相關性....15
4.1.1 探索高分學生與低分學生之間的閱讀動作和閱讀物件差異....15
4.1.2 探索閱讀動作和物件與學習成效之間的關係....19
4.2 探索修改筆記行為與學習成效的相關性....22
4.2.1 探索高分學生和低分學生於修改筆記行為的差異....22
4.2.2 探索修改筆記行為與學習成效之間的關係....25
4.3 尋找修改筆記行為中取得高分的關鍵學習模式....28
4.3.1 找到同頁修改筆記行為中能夠獲取高分的關鍵學習行為....28
4.3.2 尋找閱讀反思修改筆記行為中能夠獲取高分關鍵學習行為....29
4.3.3 尋找重登反思修改筆記行為中能夠獲取高分關鍵學習行為....32
五、結論與未來研究......................47
六、參考文獻............................48


                                

[1]. Mannila, H., & Toivonen, H. (1996). Discovering generalized episodes using minimal occurrences. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96), 146-151.
[2]. Codish, D., Rabin, E., & Ravid, G. (2019). User behavior pattern detection in unstructured processes–a learning management system case study. In Interactive Learning Environments, 1-27.
[3]. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining Association rules between Sets of Items in Large Database. Proceedings of the ACM SIGMOD Conference on Management of Data, Washington, D.C., United States.
[4]. Han, J., Pei, J., & Yin, Y. (1999, May). Mining Frequent Patterns without Candidate Generation. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 1-12.
[5]. Raorane, A.A., Kulkarni, R.V., & Jitkar, B.D. (2012, February). Association Rule - Extracting Knowledge Using Market Basket Analysis. Research Journal of Recent Sciences, 1(2), 19-27.
[6]. Hilage, T., & Kulkarni, R. (2011). Application of data mining techniques to a selected business organization with special reference to buying behavior. Int. J. Database Manag. Syst., 3(4), 169–181.
[7]. Manpreet, K., & Shivani, K. (2016). Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining. Procedia Computer Science, 85, 78–85.
[8]. Bhambri, V. (2011, June). Application of Data Mining in Banking Sector. International Journal of Computer Science and Technology, 2(2).
[9]. Ketui, N., Wisomka, W., & Homjun, K. (2019). Association Rule Mining with Permutation for Estimating Students Performance and Its Smart Education System. Journal of Computers, 30(2), 93-102.
[10]. Manjarres, A.V., Sandovalm, L. G. M., & Suárez, M. J. S. (2018). Data mining techniques applied in educational environments: Literature review. Digital Education Review, 33, 235-266.
[11]. Kularbphettong, K., Waraporn, P., & Tongsiri, C. (2012). Analysis of Student Motivation Behavior on e-Learning Based on Association Rule Mining. World Academy of Science, Engineering and Technology, 66, 502-505.
[12]. Freyberger, J., Heffernan, N., & Ruiz, C. (2004). Using association rules to guide a search for best fitting transfer models of student learning. In: Workshop Analyzing Student-Tutor Interaction Logs Improve Educational Outcomes at ITS Conference, 1-10.
[13]. Basha, S. A. H. (2018, March-April). Study of Education Patterns in Rural and Urban India using Association Rule Mining: Implementation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(3), 578-588.
[14]. Oladipupo, O. O., & Oyelade, O. J. (2010). Knowledge Discovery from Students' Result Repository: Association Rule Mining Approach. International Journal of Computer Science and Security, 4(2), 199-207.
[15]. Zakaria, S.A., Muhamad, W.Z.A.W., & Azziz, N.H.A. (2018). Analyzing undergraduate students' performance in engineering statistics course using educational data mining: Case study. in UniMAP. AIP Conf. Proc.
[16]. Chen, Y., & Weng, C. (2009). Mining fuzzy association rules from questionnaire data. Knowledge-Based Systems Journal, 22(1), 46–56.
[17]. Upendran, D., Chatterjee, S., Sindhumol, S., & Bijlani, K. (2016). Application of predictive analytics in intelligent course recommendation. Procedia Computer Science, 93(1), 917-923.
[18]. Merceron, A., & Yacef, K. (2005). Educational Data Mining: a Case Study. In Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED 2005, Amsterdam, The Netherlands, IOS Press.
[19]. Jigang, Z., & Jingmei, Z. (2016). The Application of Association Rules Mining in the Analysis of Students' Test Scores. International Conference on Education, Management and Computer Science, 14-18.
[20]. Bambrah, C., Bhandari, M., Maniar, N., & Munde, V. (2014, March). Mining Association Rules in Student Assessment Data. International Journal of Advanced Research in Computer and Communication Engineering, 3(3).

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