臺灣博碩士論文加值系統

目前大多學生課後學習以傳統習作方式學習，多為教師指定回家作業，由於缺乏互動性，以致於學生學習意願低落以及缺乏思索，等待教師告訴下一步驟，並且學生在遇到新問題時，缺乏詮釋題目的能力，而無法有效地將所學知識應用於新的問題上。
本論文建置遊戲學習系統提升學生學習動機，再以問題導向學習設計遊戲題目，透過情境式題目使學生詮釋題意後解決問題，並且學生可以檢視自我學習歷程，瞭解自身學習情形，訂立學習目標，達到自我導向學習效果。
應用資料探勘中的決策樹演算法分析學生學習表現與遊戲表現之間關聯性，透過指定因子分類模組找出影響學生學習成效因素，並提供教師作為教學建議與學生學習建議。

At present, most students do homework that is the way of traditional. Teachers design homework in most cases. It lacks of interactivity, so that students with poor willingness learn and lack of thought. They wait for teacher to tell the next step. When students encounter new problems, because they lack the ability of interpreting the subject, they can’t effectively applied the knowledge on new problems.
In this thesis, using game learning system to improve learning motivation, then using problem-based learning to design game problem. Through situational problem, students can interpret the meaning of problems and then solve the problem. In addition, students can view the self-learning process and build learning goal. Finally, they are able to reach self-directed learning.
In this thesis, using item response theory to analysis the response of patients in questionnaires and further investigate its causes and subsequent coping. Using of the decision tree algorithm to analysis the correlation between students'' performance and game performance. Finding the factor of student learning efficiency through specify classification module. These factors can provide teachers and students with suggestions.

目錄
第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 2
1.3論文架構 4
第二章 相關研究 5
2.1遊戲導向學習 5
2.2問題導向學習 6
2.3資料探勘 9
2.3.1決策樹演算法 11
2.3.2常見的決策樹演算法 12
第三章 研究方法 15
3.1研究流程 15
3.2系統架構 17
3.3提升學習意願方法 18
3.3.1通知機制 18
3.3.2使用者互動控制 19
3.3.3師生互動控制 20
3.4學習歷程方法 21
3.4.1檢閱自我學習歷程 21
3.4.2檢閱學生學習歷程 22
3.5問題設計方法 23
3.6精進化教學 24
3.7驗證學習成效與影響學習因素 25
3.7.1資料收集 26
3.7.2驗證學習成效 26
3.7.3影響學習因素 27
第四章 實作成果 32
4.1實作環境 32
4.2遊戲學習系統 33
4.2.1主動通知學生課程任務 33
4.2.2遊戲課程任務 34
4.2.3檢閱自我學習歷程 37
4.2.4檢閱學生學習歷程 38
4.2.5師生互動控制 39
4.3課程案例設計 41
4.3.1 主題-Recursive 41
4.4遊戲學習學生表現 42
4.5學習成效分析 51
4.5.1提升學習成效 51
4.5.2應用題型 52
4.5.3瞭解題型 53
4.6決策樹分析 54
4.6.1 影響期末考因素 55
4.6.2 影響學期成績因素 56
第五章 結論與未來方向 59
5.1結論 59
5.2未來方向 60
參考文獻 61
附錄一 英文論文 65
圖目錄
圖 1決策樹模組 11
圖 2研究流程 16
圖 3系統架構 17
圖 4登入機制架構 18
圖 5使用者互動控制架構 19
圖 6命令分析回饋 20
圖 7師生互動控制架構 21
圖 8檢閱自我學習歷程架構 22
圖 9檢閱學生學習歷程架構 23
圖 10課程題目設計架構 24
圖 11統計分析與資料探勘架構 25
圖 12資料探勘流程 28
圖 13決策樹分析流程 30
圖 14系統實作-通知學生課程題目 34
圖 15系統實作-情境案例說明 35
圖 16系統實作-命令輸入 35
圖 17系統實作-辨識命令與動作回饋 36
圖 18系統實作-課程任務成功 36
圖 19系統實作-課程任務失敗 37
圖 20系統實作-檢閱自我學習歷程-1 37
圖 21系統實作-檢閱自我學習歷程-2 38
圖 22系統實作-檢閱學生學習歷程-1 39
圖 23系統實作-檢閱學生學習歷程-2 39
圖 24系統實作-師生互動討論 40
圖 25系統實作-師生線上互動討論 40
圖 26參與人數 42
圖 27完成第一關人數比例 43
圖 28完成第二關人數比例 43
圖 29學生進行遊戲天數 44
圖 30完成任務成功比率 45
圖 31任務進行總次數 45
圖 32題目學習成功次數 46
圖 33題目學習失敗次數 48
圖 34「前後測-應用遞迴程式解決問題」之分數分佈 51
圖 35「前後測-瞭解遞迴程式解決問題」之分數分佈 51
圖 36影響期末考因素之預測變數重要性 55
圖 37影響學期成績因素之預測變數重要性 57
表目錄
表 1常見決策樹演算法 12
表 2分數轉換級距 29
表 3實作環境 33
表 4施測樣本與時間 33
表 5任務成功，進行13次任務學生 47
表 6任務成功，進行12次任務學生 47
表 7任務失敗，進行8次任務學生 48
表 8任務失敗，進行7次任務學生 49
表 9「抵達出口卻沒有停止機器人運作」錯誤情形 50
表 10「沒有發現石頭造成機器人執行錯誤」錯誤情形 50
表 11「前方有石頭卻持續前進，造成無法前進錯誤」錯誤訊息 50
表 12「應用題型」之實驗組檢定摘要表 53
表 13「應用題型」之對照組檢定摘要表 53
表 14「瞭解題型」之實驗組檢定摘要表 54
表 15「瞭解題型」之對照組檢定摘要表 54
表 16「期末考」決策樹分析模型預測正確率 56
表 17「期末考」實際值與預測值情形 56
表 18「學期成績」決策樹分析模型預測正確率 58
表 19「學期成績」實際值與預測值情形 58

[1]P. W. Airasian, K. A. Cruikshank, R. E. Mayer, P. Pintrich, J. Raths and M. C. Wittrock, A taxonomy for learning, teaching, and assessing: A revision of bloom''s taxonomy of educational objectives. Anderson LW and Krathwohl DR.New York: Addison Wesley Longmann, 2001.
[2]N. Vos, H. Van DerMeijden, and E. Denessen, “Effects of constructing versus playing an educational game on student motivation and deep learning strategy use,” Comput. Educ., vol. 56, no. 1, pp. 127–137, 2011.
[3]T. Hainey, T. M. Connolly,M. Stansfield, and E. A. Boyle, “Evaluation of a game to teach requirements collection and analysis in software engineering at tertiary education level,” Comput. Educ., vol. 56, no. 1, pp. 21–35, 2011.
[4]R.E. Parker, “Small-Group Cooperative Learning--Improving Academic, Social Gains in the Classroom.” Nassp Bulletin, vol. 69, no. 479, pp. 48-57, 1985.
[5]M. Papastergiou, “Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation,” Comput.Educ., vol. 52, no. 1, pp. 1-12, 2009.
[6]E. Y. Leng, W. Z. W. Ali, R. Mahmud, and R. Baki, “Computer games development experience and appreciative learning approach for creative process enhancement,” Comput. Educ., vol. 55, no. 3, pp. 1131–1144, 2010.
[7]L. M. Miller, C. I. Chang, S. Wang, M. E. Beier, and Y. Klisch, “Learning and motivational impacts of a multimedia science game,” Comput.Educ., vol. 57, no. 1, pp. 1425–1433, 2011.
[8]A. Y. K. Chua, “The design and implementation of a simulation game for teaching knowledge management,” J. Amer. Soc. Inf. Sci. Technol., vol. 56, no. 11, pp. 1207–1216, 2005.
[9]D. J. Ketelhut and C. C. Schifter, “Teachers and game-based learning: Improving understanding of how to increase efficacy of adoption,” Comput. Educ., vol. 56, no. 2, pp. 539–546, 2011.
[10]H.S. Barrows, Problem-based learning: An approach to medical education, 1980.
[11]J.A. Rankin, “Problem-based medical education: effect on library use,” Bull.Med.Libr.Assoc., vol. 80, no. 1, Jan, pp. 36-43, 1992.
[12]K.M. Edens, “Preparing problem solvers for the 21st century through problem-based learning,” College Teaching, vol. 48, no. 2, pp. 55-60, 2000.
[13]M. J. Berry and G. S. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2004.
[14]Azzedine Boukerche, Samer Samarah, “In-Network Data Reduction and Coverage-Based Mechanisms for Generating Association Rules in Wireless Sensor Networks, ” IEEE Transactions on Vehicular Technology, vol. 58, No. 8, pp.4426-4438, October 2009.
[15]Sanjay Kumar Shukla, Manoj Kumar Tiwari, “GA Guided Cluster Based Fuzzy Decision Tree for Reactive Ion Etching Modeling:A Data Mining Approach,” IEEE Transaction on Semiconductor Manufacturing, vol. 25, No. 1, pp.45-56, February 2012.
[16]Stefano Ceccon, David F. Garway-Heath, David P. Crabb, and Allan Tucker, "Exploring Early Glaucoma and the Visual Field Test:Classification and Clustering Using Bayesian Networks,” IEEE Journal of biomedical and health informatics, vol. 18, No. 3, pp.1008-1014, May 2014.
[17]Brian A. Archer, Udaya D. Annakkage, Bathiya Jayasekara, Punya Wijetunge, “Accurate Prediction of Damping in Large Interconnected Power Systems With the Aid of Regression Analysis,” IEEE Transaction on power systems, vol. 23, No .3, pp.1170-1178, August 2008.
[18]Koby Todros, and Alfred O. Hero, III, “On Measure Transformed Canonical Correlation Analysis” IEEE Transactions on signal processing, vol. 60, No. 9, pp.4570-4585, September 2012.
[19]Anonymous, “決策樹- 台灣Wiki., ” http://www.twwiki.com/wiki/決策樹, September 24, 2013.
[20]Jocelyne Fayn, “A Classification Tree Approach for Cardiac Ischemia Detection Using Spatiotemporal Information From Three Standard ECG Leads,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 95-102, January 2011.
[21]Junghwan Cho, Xiaopeng Li, Zhiyong Gu, and Pradeep U. Kurup, “Recognition of Explosive Precursors Using Nanowire Sensor Array and Decision Tree Learning,” IEEE Sensors Journal, vol. 12, no. 7, pp.2384-2391, July 2012.
[22]Guleng Sheri and David Corne, “Learning-Assisted Evolutionary Search for Scalable Function Optimization: LEM(ID3),” IEEE Congress on Evolutionary Computation, pp.1-8, July 2010.
[23]Leszek Rutkowski, Lena Pietruczuk, Piotr Duda, and Maciej Jaworski, “Decision Trees for Mining Data Streams Based on the McDiarmid’s Bound,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp.1272-1279, June 2013.
[24]J. R. Quinlan, C4. 5: Programs for Machine Learning, 1993.
[25]J.R.Quinlan, “C5.0: An Informal Tutorial,” http://www.rulequest.com/see5-unix.html, March 2013.
[26]T. Hainey, T.M. Connolly, M. Stansfield and E.A. Boyle, ''"Evaluation of a game to teach requirements collection and analysis in software engineering at tertiary education level," Comput.Educ., vol. 56, no. 1, pp. 21-35, July 2011.