• 沒有找到結果。

第五章 系統架構與建置

6.5  討論

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

53

形成了"記憶"的學習效果。內容完整的提供,也許加強鷹架學習效果; 針對弱點的補 強提示,一樣有加強效果,但不如預期。可能的原因是提示的設計是使用選擇題的隱涵 性提示,而非顯性提示,在學生處於不明知識領域時,僅提供其部份又隱性的知識,幫 助的效果可能不如預期;但在適當的時間點(遇到思考困境時),提供較為完整的知識領 域學習,應該是有複習或補強學習的效果(熟習和不熟習的一起溫習,加強了記憶),因 此學生在需要知識時,如能提供一個完整的知識領域的參考學習,也許是更有助學習效 果及完整性。整體而言,就開放式問答的學習模式當中,在適當的時間點提供完整的學 習提示似乎是較佳的模式。

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

54

第七章 結論與未來發展

7.1 結論

我們設計了一個以開放式申論題為基礎的線上學習輔助系統,用以提升學習的效 果。藉由將開放式問題所涉及的觀念細分成許多觀念組合,並建立階層化使用者知識模 型以進行評估,讓系統得以了解學生於各個觀念的表現。同時我們也對於各個觀念設計 相對應的提示,在學生陷入瓶頸的時候能夠給予適當的協助。我們也設計了實驗來收集 學生在作答時的答題風格資料,並且嘗試找出我們認為最佳的回饋時機點。

實驗結果顯示,學生在回答開放式問答時,如果有給予提問式提示,是有助於學生 學習及回答的。而給予提示的時機點,在實驗結果中看起來影響並沒有很大,比較重要 的反而是給予的提示內容為何。固定觀念順序的給予提示,能夠給予學生較為全面的觀 念補強,對於學生的作答是較有幫助的。而給予觀念弱點的補強提示,效果相較於固定 觀念順序的補強,反而效果較差,這是當初設計實驗時所沒預料到的。在實驗中,我們 也發現對於主動學習性較高的學生,這個系統也會有較多的幫助,但是學習主動性較高 的學生,在一個班級裡佔的人數比例也並不高。未來我們希望能改進系統,使得系統對 於其他的學生也能夠有所幫助。

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

55

7.2 未來發展

第三章中,我們建立了階層式的觀念樹,各個觀念被學習到的先後順序可以由樹狀 結構來記錄。目前每次要提供回饋時,皆會從觀念樹的根節點開始走訪,而根節點通常 都是比較基礎的觀念。即使學生的答案已經有一定的水準,系統給予的回饋仍然會從基 礎開始問起。若是學生耐心不足,無法等到系統走訪到較難觀念的節點時,便無法得到 更進階的提示。未來若是能依據學生的程度進而改善走訪觀念樹的方式,相信能給予學 生更多的學習幫助。

第四章中,我們嘗試建立一個使用者風格模型,希望能夠了解學生在作答過程中的 行為。目前的答題風格模型會觀察學生剛開始的作答行為,同時也會以固定的投資報酬 率來判斷學生在作答中是否遇到瓶頸。我們希望未來能更進一步的找出學生在作答中是 否會有一些固定的模式,讓我們可以針對不同模式提出不同的教學方式。

在最後的實驗章節中,我們發現學生的學習主動性與學習效果有一定的關聯。目前 是以人工的方式去觀察學生的作答歷程以及系統提供的回饋來分類。從實驗的結果看 來,目前的系統對於學習主動性高的學生是比較有幫助的,未來若是可以即時根據學生 目前與系統互動的方式,分辨出學生的學習主動性高低,便可以針對不同學習主動性的 學生設計不同的回饋方式。

[1] C.C. Huang, H.C. Wang, T.-Y. Li and C.Y. Chang, “An Online Testing and Analysis System for Students’ Creative Problem-Solving Ability in Sciences,” in Proceedings of the Tenth Global Chinese Conference of Computers in Education, China, 2006.

[2] H.C. Wang T.Y. Li, C.C. Huang, and C.Y. Chang, “VIBRANT: A Brainstorming Agent for Computer Supported Creative Problem Solving,” in PM. Ikeda, K. Ashley, and T.-W. Chan (Eds.): Italic, LNCS 4053, pp. 787 – 789, 2006.

[3] H.-C. Wang, T.-Y. Li, & C-Y. Chang, “A user modeling framework for exploring creative problem-solving ability,” in Proceedings of 12th International Conference on Artificial Intelligence in Education, pp. 941-943, 2005.

[4] H.-C. Wang, T.-Y. Li, & C-Y. Chang, “Automated scoring for creative problem solving ability with ideation-explanation modeling,” in Proceedings of 13th Interna-tional Conference on Computers in Education, 2005.

[5] S. Carberry, “Techniques for Plan Recognition,” User Modeling and User-Adapted Interaction, 2001.

[6] C.-C. Huang, C.-Y. Chang, T.-Y. Li, and H.-C. Wang, “A Collaborative Support Tool for Divergent Thinking: Idea Storming Cube,” in Proceedings of 2008 Annual International Conference of National Association for Research in Science Teaching, 2008.

[7] R. Kumar, C. Rose, V. Aleven ,A. Iglesias & Robinson, “A Evaluating the Effec-tiveness of Tutorial Dialogue Instruction in an Exploratory Learning Context,” in Proceedings of International Conference on Intelligent Tutoring Systems, 2006.

[8] C-Y. Chang, Y-H. Weng, “An Exploratory Study on Students' Problem-Solving Ability in Earth Sciences,” International Journal of Science Education, 24(5), pp.

441-451, 2002.

[9] H-C. Wang, C-Y. Chang & T-Y. Li, “Automated Scoring for creative prob-lem-solving ability with ideation-explanation modeling,” in Proceedings of 13th In-ternational Conference on Computers in Education (ICCE 2005), pp.522-529, 2005.

[10] I.H. Witten, E. Frank, L. Trigg, M. Hall, G. Holmes, and S.J. Cunningham, “Weka:

Practical machine learning tools and techniques with Java implementations,” in Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems”, pp. 192-196, Dunedin, New Zealand, 1999.

[11] D. J. Treffinger, E. C. Selby, and S. G. Isaksen, “Understanding individual prob-lem-solving style: A key to learning and applying creative problem solving,”

Learning and Individual Differences, Elsevier, 2008.

[12] K. Ferguson, I. Arroyo, S. Mahadevan, B. Woolf and A. Barto, “Improving Intelli-gent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels,” in Proceeding of Intelligent Tutoring System, 2006.

[13] N. Baghaei, A. Mitrovic, “A constraint-based collaborative environment for learning UML class diagrams,” in Lecture Notes in Computer Science, Springer, 2006.

[14] M. Gonzalez ,and D. Suthers, “Coaching Collaboration in a Computer Mediated Learning Environment,” in Stahl, G. (eds.) CSCL2002, pp.583-584, 2002.

[15] C.-C. Huang, T.-Y. Li, H.-C. Wang, and C.-Y. Chang, ”Idea Storming Cube: A Game-based System to Support Creative Thinking,” in Proceedings of the First

IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning (DIGITEL2007), 2007.

[16] H.-C. Wang, C.-Y. Chang, and T.-Y. Li, ”Assessing creative problem-solving with automated text grading,” Computer and Education, 51, pp.1450-1466, 2008.

[17] A. Mitrovic, M. Mayo, P. Suraweera, and B. Martin, “Constraint-based Tutors: a Success Story,” in Proceedings of 14th Int. Conf. Industrial and Engineering Ap-plications of Artificial Intelligence and Expert Systems, pp.931-940, 2001.

[18] A. Dempster, N. Laird, and D. Rubin, “Maximization-likelihood from Incomplete Data via the EM Algorithm,” Journal of Royal Statistical Society, Series B, 1977.

[19] E. Hannan, and B. Quinn, “The determination of the order of an autoregression,”

Journal of the Royal Statistical Society, 1979.

[20] D. M. Johnson, Systemic introduction to the psychology of thinking, New York:

Harper & Row, 1972.

[21] D. J. Treffinger, S. G. Isaksen, & K. B. Stead-Dorval, Creative problem solving: An introduction (4th ed.), 2006.

[22] A. F. Osborn, Applied imagination: Principles and procedures of creative thinking, New York: Charles Scribner’s Sons,1953.

[23] D. J. Treffinger, E. C. Selby, S. G. Isaksen, & J. H. Crumel, Problem solving style:

Introduction and overview, Sarasota, FL: Center for Creative Learning,2007.

[24] I. Myers, & M. McCaulley, Manual: A guide to the development and use of the Myers-Briggs Type Indicator Palo, 1985.

[25] R. Dunn and K. Dunn, Teaching secondary students through their individual learn-ing styles, Boston: Allyn & Bacon, 1993.

[26] O. Martinsen, & G. Kaufmann, Cognitive style and creativity ,1999.

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

59

[27] R. Roscoe, J. Wagster and G.Biswas, “Using Teachable Agent Feedback to Support Effective Learning by Teaching,” in The Thirtieth Annual Meeting of the Cognitive Science Society, pp. 2381-2386, Washington, DC, July 2008.

[28] W. Ma and K. Chen “Introduction to CKIP Chinese word segmentation system for the first international Chinese word segmentation bakeoff,” in Proceedings of the second SIGHAN workshop on Chinese language processing, 2003.

[29] Flex:http://www.adobe.com/products/flex/

相關文件