以深度知識追蹤模型應用於程式學習系統

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姓名

李秉翰(Ping-Han Lee) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

以深度知識追蹤模型應用於程式學習系統
(Application of Deep Knowledge Tracing Model to Support Programming Learning System)

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至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

近年來各國對於程式教育逐漸重視，程式設計能力已成為未來競爭的關鍵能力之
一。過去學生在程式教育課程中常因遇到困難無法解決，教師也不易瞭解學生在程式設
計學習過程中所遇到的問題與困境，導致學生學習成就與動機降低。目前眾多教育資料
探勘研究多著重於學生課程最終通過與否的預測，而透過深度知識追蹤可以針對學生的
學習軌跡進行知識建模，了解學生當下的知識掌握程度，協助學生針對弱點進行改善。
本研究以臺灣北部某國立大學研究所課程進行實驗，研究對象共計20 人，為期18
周。本研究結合深度知識追蹤開發一個程式設計教學輔助系統，並對學生課程中所累積
的數據進行預測，將成果即時的呈現於儀表板中，以幫助學生與教師了解學生學習行為
以及對於各項知識點的掌握程度，並提供相對應之學習建議。在資料分析方面則透過課
程專用伺服器，蒐集學生於程式編輯平台上所操作的日誌以及隨堂測驗的答題資料，並
透過深度知識追蹤進行預測。同時，檢視深度知識追蹤運用在程式設計課堂上的效果以
及是否能有效的協助學生進行學習。
研究結果發現，透過程式設計教學輔助系統，學生程式能力獲得顯著進步，運算思
維以及程式設計學習動機與學習成果呈現顯著正相關；深度知識追蹤能有效運用於程式
設計課堂中，而且針對學生進行不同知識點能力評估；於學習歷程中發現，學習表現較
差的學生有著較低的學習動機與較低的編程練習投入，授課教師可透過學習歷程數據主
動對學生提供協助。未來研究可參照本研究結果，進一步結合開源線上程式能力評量系
統，達到自動化辨識知識點的目標。

摘要(英)

In recent years, more and more countries have paid attention to program education. As a
result, programming ability has become one of the most critical competencies in the future. In
the past, it was hard for teachers to find and understand the problems that students face in the
coding process, resulting in reduced learning achievement and motivation. Currently, many
educational data mining studies focus on the dropout rate of students. Through deep knowledge
tracing, we can model students′ learning trajectories, understand their current knowledge level,
and help students overcome their weaknesses.
This study was conducted at a national university in northern Taiwan. A total of 20
graduate students participated in the experiment for 18 weeks. This study combines deep
knowledge tracing to develop a program learning system. The system supports the predictions
based on the data accumulated from students′ learning processes. The system dashboard can
immediately help students and teachers understand students′ learning behavior and mastery of
various knowledge points and provide corresponding learning suggestions. Students′ logs on
the integrated development environment and pop quizzes are collected for data analysis, and
predictions are made through the deep knowledge tracing technique. It also examines the
effectiveness of deep knowledge tracing in programming classes and whether it can effectively
assist students in learning.
The results show that students′ program ability has been significantly improved in this
study. Both computational thinking and learning motivation have a significant positive
correlation with learning outcomes. Deep knowledge tracing can effectively be used in
programming class to evaluate students′ abilities according to their different knowledge points.
The students with lower learning performance have lower learning motivation and engagement
in programming practice. Teachers can actively assist students with learning records about the
learning process. Future researchers can refer to this study by combining the open-source online
judge system to identify knowledge points automatically.

關鍵字(中)

★ 學習分析
★ 教育資料探勘
★ 深度知識追蹤
★ 程式設計教育
★ 視覺化儀表板

關鍵字(英)

★ Learning analysis
★ Educational data mining
★ Deep knowledge tracing
★ Programming education
★ Dashboard

論文目次

目錄
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖目錄 ix
表目錄 x
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究問題 3
1-4 名詞釋義 3
二、文獻探討 5
2-1 教育資料探勘 5
2-1-1 學習管理系統 5
2-1-2 教育資料探勘 6
2-1-3 程式教育資料探勘 7
2-2 學習預測與深度知識追蹤 9
2-2-1 學習成效的預測 9
2-2-2 知識追蹤 11
2-2-3 深度知識追蹤 12
2-3 程式設計學習動機 15
2-3-1 學習動機 15
2-3-2 程式設計學習動機 15
2-4 運算思維 17
三、研究方法 20
3-1 研究對象 20
3-2 研究流程 21
3-2-1 第一階段(2021年6月至2021年8月)：課程設計與教學輔助系統建置 22
3-2-2 第二階段(2021年9月至2022年1月)：系統實測 22
3-2-3 第三階段(2022年1月至2022年4月)：資料探勘及分析 22
3-3 課程設計 23
3-4 研究工具 26
3-4-1 程式能力測驗與隨堂測驗 26
3-4-2 程式能力專案測驗 29
3-4-3 學生背景問卷 30
3-4-4 程式設計學習動機問卷 31
3-4-5 運算思維問卷 33
3-4-6 創新教學計劃問卷 35
3-4-7 課後問卷 37
3-5 資料分析 38
3-5-1 成績部分 38
3-5-2 知識點部分 39
3-5-3 學生學習編程log相關紀錄 42
3-5-4 問卷結果 43
3-5-5 分析方法 44
四、系統設計與實作 46
4-1 系統環境架構 46
4-2 學習管理系統 48
4-2-1 答題記錄蒐集 49
4-2-2 資料視覺化儀表板 50
4-3 程式編程日誌資料蒐集系統 53
4-3-1 log系統操作日誌蒐集套件 54
4-4 深度知識追蹤 56
五、研究結果 58
5-1 程式設計學習成效 59
5-2 程式設計學習動機問卷 60
5-2-1 程式設計學習動機與學習表現成績的相關 61
5-2-2 程式設計學習動機與學習歷程的相關 62
5-3 運算思維問卷 62
5-3-1 運算思維問卷與學習表現成績的相關 63
5-4 深度知識追蹤預測結果 64
5-4-1 深度知識追蹤預測學生能力結果與學習表現成績的相關 64
5-4-2 運用深度知識追蹤對能力專案考表現進行加權預測的相關 65
5-5 學生答題知識點分群相似與成績 66
5-6 學習歷程特徵於不同學習表現學生間之差異 71
5-6-1 程式能力專案測驗不同之學生的特徵差異 71
5-6-2 程式能力專案測驗不同之學生的運算思維與程式設計學習動機差異 73
六、討論 74
6-1 學生認知作業難度、參與度與表現的關係 74
6-2 學習歷程特徵於不同儀表板觀看重點學生間之差異 77
6-3 學生問卷調查結果 81
6-3-1 教學輔助系統對學生的幫助 81
6-3-2 結合視覺化儀表板可增進學生了解學習歷程 82
6-3-3 程式設計學習動機與運算思維的相關 83
6-4 與其他知識追蹤相關研究比較 83
七、結論 85
7-1 研究結論 85
7-1-1 教學輔助系統能有效給予學生學習幫助及提升運算思維 85
7-1-2 深度知識追蹤及知識點應用於程式教育可有效預測學生各項能力 86
7-1-3 藉由學生程式編輯歷程可建立學生預警機制 86
7-2 研究限制 87
7-3 未來展望 87
參考文獻 89
附錄一、程式能力測驗前測 99
附錄二、程式設計學習動機量表 105
附錄三、運算思維量表 107
附錄四、學生背景問卷 109
附錄五、課程隨堂測驗題目 110
附錄六、教學創新計畫期中問卷 114
附錄七、程式能力專案測驗 117
附錄八、課後問卷 124
附錄九、知情同意書 125

參考文獻

梁琨、任依梦、尚余虎、张翼英、王聪(2021)。深度学习驱动的知识追踪研究进展综述。计算机工程与应用，57(21)，41-58。 https://doi.org/10.3778/j.issn.1002-8331.2106-0552
劉恆宇、張天成、武培文、于戈(2019)。知識追蹤綜述。華東師範大學學報 (自然科學板)，2019(5)，1-15。https://doi.org/10.3969/j.issn.1000-5641.2019.05.001
劉嫈楓. (2022, January 1). 有數位工具≠數位轉型－均一呂冠緯，從「心」教育. Ubrand.Udn.Com. https://ubrand.udn.com/ubrand/story/11817/5996382
謝佩珊、石美倫(2020)。國內大學學習預警資訊系統研究: 發展現況與侷限。教育科學研究期刊，65(4)，171-201。https://doi.org/10.6209/JORIES.202012_65(4).0006
Adekitan, A. I., & Salau, O. (2019). The impact of engineering students′ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250. https://doi.org/https://doi.org/10.1016/j.heliyon.2019.e01250
Adler, R. F., & Kim, H. (2018). Enhancing future K-8 teachers’ computational thinking skills through modeling and simulations. Education and Information Technologies, 23(4), 1501-1514. https://doi.org/10.1007/s10639-017-9675-1
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. https://doi.org/10.1016/j.tele.2019.01.007
Alhazbi, S. (2016). Active Blended Learning to Improve Students’ Motivation in Computer Programming Courses: A Case Study. In M. Abdulwahed, M. O. Hasna, & J. E. Froyd (Eds.), Advances in Engineering Education in the Middle East and North Africa: Current Status, and Future Insights (pp. 187-204). Springer International Publishing. https://doi.org/10.1007/978-3-319-15323-0_8
ArchMiller, A., Fieberg, J., Walker, J. D., & Holm, N. (2017). Group peer assessment for summative evaluation in a graduate-level statistics course for ecologists. Assessment & Evaluation in Higher Education, 42(8), 1208-1220. https://doi.org/10.1080/02602938.2016.1243219
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: using learning analytics to increase student success Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, British Columbia, Canada. https://doi.org/10.1145/2330601.2330666
Aznar-Diaz, I., Hinojo-Lucena, F. J., Caceres-Reche, M. P., & Romero-Rodriguez, J. M. (2020). Analysis of the determining factors of good teaching practices of mobile learning at the Spanish University. An explanatory model [Article]. Computers & Education, 159, 12, Article 104007. https://doi.org/10.1016/j.compedu.2020.104007
Bentham, C. (2017). Faculty perspectives and participation in implementing an early alert system and intervention in a community college. https://purl.fcla.edu/fcla/etd/CFE0006857
Berland, M., & Wilensky, U. (2015). Comparing Virtual and Physical Robotics Environments for Supporting Complex Systems and Computational Thinking. Journal of Science Education and Technology, 24(5), 628-647. https://doi.org/10.1007/s10956-015-9552-x
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
Blikstein, P. (2011). Using learning analytics to assess students′ behavior in open-ended programming tasks Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, Canada. https://doi.org/10.1145/2090116.2090132
Brophy, J. (2004). Motivating students to learn. Routledge. https://doi.org/10.4324/9781410610218
Carter, A. S., Hundhausen, C. D., & Adesope, O. (2015). The Normalized Programming State Model: Predicting Student Performance in Computing Courses Based on Programming Behavior Proceedings of the eleventh annual International Conference on International Computing Education Research, Omaha, Nebraska, USA. https://doi.org/10.1145/2787622.2787710
Chang, C.-S., Chung, C.-H., & Chang, J. A. (2020). Influence of problem-based learning games on effective computer programming learning in higher education. Educational Technology Research and Development, 68(5), 2615-2634. https://doi.org/10.1007/s11423-020-09784-3
Chao, P.-Y. (2016). Exploring students′ computational practice, design and performance of problem-solving through a visual programming environment. Computers & Education, 95, 202-215. https://doi.org/10.1016/j.compedu.2016.01.010
Cheung, L. P., & Yang, H. (2017). Heterogeneous Features Integration in Deep Knowledge Tracing. In (pp. 653-662). Springer International Publishing. https://doi.org/10.1007/978-3-319-70096-0_67
Chung, C.-H., Pasquini, L. A., & Koh, C. E. (2013). Web-based learning management system considerations for higher education. Learning and Performance Quarterly, 1(4), 24-37. https://www.learntechlib.org/p/41809/
Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modelling and User-Adapted Interaction, 4(4), 253-278. https://doi.org/10.1007/bf01099821
Deperlioglu, O., & Kose, U. (2013). The effectiveness and experiences of blended learning approaches to computer programming education. Computer Applications in Engineering Education, 21(2), 328-342. https://doi.org/10.1002/cae.20476
Devasia, T., Vinushree, T. P., & Hegde, V. (2016, 16-18 March 2016). Prediction of students performance using Educational Data Mining 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), https://doi.org/10.1109/SAPIENCE.2016.7684167
Fajari, L. E. W., Sarwanto, & Chumdari. (2020). Student critical thinking skills and learning motivation in elementary students. Journal of Physics: Conference Series, 1440(1), 012104. https://doi.org/10.1088/1742-6596/1440/1/012104
Furner, J. M., & Gonzalez-DeHass, A. (2011). How do students’ mastery and performance goals relate to math anxiety? Eurasia Journal of Mathematics, Science and Technology Education, 7(4), 227-242. https://doi.org/10.12973/ejmste/75209
George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge. https://doi.org/10.4324/9780429056765
Grover, S., & Pea, R. (2013). Computational Thinking in K–12:A Review of the State of the Field. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189x12463051
Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199-237. https://doi.org/10.1080/08993408.2015.1033142
Guswara, A. M., & Purwanto, W. (2021). The Contribution of Google Classroom Application and Motivation to The Learning Outcomes of Web Programming. Journal of Education Technology, 4(4), 424. https://doi.org/10.23887/jet.v4i4.29896
Hamuy, E., & Galaz, M. (2010). Information versus Communication in Course Management System participation [Article]. Computers & Education, 54(1), 169-177. https://doi.org/10.1016/j.compedu.2009.08.001
Hasan, R., Palaniappan, S., Raziff, A. R. A., Mahmood, S., & Sarker, K. U. (2018, 13-14 Aug. 2018). Student Academic Performance Prediction by using Decision Tree Algorithm. 2018 4th International Conference on Computer and Information Sciences (ICCOINS),
Hegde, V., & Prageeth, P. P. (2018, 19-20 Jan. 2018). Higher education student dropout prediction and analysis through educational data mining. 2018 2nd International Conference on Inventive Systems and Control (ICISC),
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A systematic review of deep learning approaches to educational data mining. Complexity, 2019. https://doi.org/10.1155/2019/1306039
Hien, N. T. N., & Haddawy, P. (2007). A decision support system for evaluating international student applications 2007 37th annual frontiers in education conference-global engineering: knowledge without borders, opportunities without passports, https://doi.org/10.1109/FIE.2007.4417958
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/doi.org/10.1007/978-3-642-24797-2_4
Huitt, W. (2001). Motivation to learn: An overview. Educational psychology interactive, 12(3), 29-36. http://www.edpsycinteractive.org/topics/motivation/motivate.html
Hung, H.-C., Liu, I.-F., Liang, C.-T., & Su, Y.-S. (2020). Applying Educational Data Mining to Explore Students’ Learning Patterns in the Flipped Learning Approach for Coding Education. Symmetry, 12(2), 213. https://doi.org/10.3390/sym12020213
Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263-279. https://doi.org/https://doi.org/10.1016/j.compedu.2014.11.022
Jenkins, T. (2001). The motivation of students of programming Proceedings of the 6th annual conference on Innovation and technology in computer science education, Canterbury, United Kingdom. https://doi.org/10.1145/377435.377472
Jenkins, T. (2002). On the difficulty of learning to program.
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs (Vol. 2). Springer. https://doi.org/10.1007/978-1-4757-3502-4
Jiang, B., Wu, S., Yin, C., & Zhang, H. (2020). Knowledge Tracing Within Single Programming Practice Using Problem-Solving Process Data. IEEE Transactions on Learning Technologies, 13(4), 822-832. https://doi.org/10.1109/TLT.2020.3032980
Jiau, H. C., Chen, J. C., & Ssu, K. F. (2009). Enhancing Self-Motivation in Learning Programming Using Game-Based Simulation and Metrics. IEEE Transactions on Education, 52(4), 555-562. https://doi.org/10.1109/TE.2008.2010983
Jokhan, A., Sharma, B., & Singh, S. (2019). Early warning system as a predictor for student performance in higher education blended courses. Studies in Higher Education, 44(11), 1900-1911. https://doi.org/10.1080/03075079.2018.1466872
Kakeshita, T., & Murata, M. (2017, 9-13 July 2017). Utilizing Programming Education Support Tool Pgtracer in an Actual Programming Course 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), https://10.1109/IIAI-AAI.2017.36
Kakeshita, T., & Murata, M. (2018). Application of programming education support tool pgtracer for homework assignment. International Journal of Learning Technologies and Learning Environments, 1(1), 41-60. https://doi.org/10.52731/ijltle.v1.i1.293
Kakeshita, T., & Ohta, K. (2015). Student log analysis functions for web-based programming education support tool pgtracer Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, Brussels, Belgium. https://doi.org/10.1145/2837185.2837227
Kakeshita, T., Yanagita, R., & Ohta, K. (2015, 2015/04). A Programming Education Support Tool pgtracer utilizing Fill-in-the-Blank Questions-Overview and Student Functions https://doi.org/10.2991/ermm-15.2015.43
Kasurinen, J., & Nikula, U. (2009). Estimating programming knowledge with Bayesian knowledge tracing. SIGCSE Bull., 41(3), 313–317. https://doi.org/10.1145/1595496.1562972
Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205-240. https://doi.org/10.1007/s10639-020-10230-3
Kleinginna, P. R., & Kleinginna, A. M. (1981). A categorized list of emotion definitions, with suggestions for a consensual definition. Motivation and emotion, 5(4), 345-379. https://doi.org/doi.org/10.1007/BF00992553
Kouis, D., Kyprianos, K., Ermidou, P., Kaimakis, P., & Koulouris, A. (2020). A framework for assessing LMSs e-courses content type compatibility with learning styles dimensions. Journal of e-Learning and Knowledge Society, 16(2), 73-86. https://doi.org/10.20368/1971-8829/1135204
Lai, Y.-H., Chen, S.-Y., Lai, C.-F., Chang, Y.-C., & Su, Y.-S. (2021). Study on enhancing AIoT computational thinking skills by plot image-based VR. Interactive Learning Environments, 29(3), 482-495. https://doi.org/10.1080/10494820.2019.1580750
Lam, M. S. W., Chan, E. Y. K., Lee, V. C. S., & Yu, Y. T. (2008, 2008//). Designing an Automatic Debugging Assistant for Improving the Learning of Computer Programming Hybrid Learning and Education, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85170-7_32
Law, K. M. Y., Lee, V. C. S., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218-228. https://doi.org/10.1016/j.compedu.2010.01.007
Lee, J., & Yeung, D.-Y. (2019). Knowledge query network for knowledge tracing: How knowledge interacts with skills Proceedings of the 9th International Conference on Learning Analytics & Knowledge, https://doi.org/10.1145/3303772.3303786
Lin, Y.-G., McKeachie, W. J., & Kim, Y. C. (2003). College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences, 13(3), 251-258. https://doi.org/https://doi.org/10.1016/S1041-6080(02)00092-4
Liu, D., Dai, H., Zhang, Y., Li, Q., & Zhang, C. (2020). Deep knowledge tracking based on attention mechanism for student performance prediction 2020 IEEE 2nd International Conference on Computer Science and Educational Informatization (CSEI), https://doi.org/10.1109/CSEI50228.2020.9142472
Lu, F., Li, X., Liu, Q., Yang, Z., Tan, G., & He, T. (2007). Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72588-6_99
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The Scratch Programming Language and Environment. ACM Trans. Comput. Educ., 10(4), Article 16. https://doi.org/10.1145/1868358.1868363
Mazza, R., & Milani, C. (2004). Gismo: a graphical interactive student monitoring tool for course management systems International Conference on Technology Enhanced Learning, Milan,
Minn, S., Desmarais, M. C., Zhu, F., Xiao, J., & Wang, J. (2019). Dynamic Student Classiffication on Memory Networks for Knowledge Tracing Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II, Macau, China. https://doi.org/10.1007/978-3-030-16145-3_13
Minn, S., Yu, Y., Desmarais, M. C., Zhu, F., & Vie, J. (2018, 17-20 Nov. 2018). Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing 2018 IEEE International Conference on Data Mining (ICDM), https://doi.org/10.1109/ICDM.2018.00156
Montero, S., Arora, A., Kelly, S., Milne, B., & Mozer, M. (2018). Does deep knowledge tracing model interactions among skills? Proceedings of the 11th International Conference on Educational Data Mining, https://eric.ed.gov/?id=ED593213
Novianti, C., Sadipun, B., & Balan, J. M. (2020). Pengaruh Motivasi Belajar Terhadap Hasil Belajar Matematika Peserta Didik. SPEJ (Science and Physic Education Journal), 3(2), 57-75. https://doi.org/10.31539/spej.v3i2.992
Oudeyer, P.-Y., & Kaplan, F. (2009, 2009-November-02). What is intrinsic motivation? A typology of computational approaches [Original Research]. Frontiers in Neurorobotics, https://www.frontiersin.org/article/10.3389/neuro.12.006.2007
Pala, F. K., & Mıhcı Türker, P. (2021). The effects of different programming trainings on the computational thinking skills. Interactive Learning Environments, 29(7), 1090-1100. https://doi.org/10.1080/10494820.2019.1635495
Pedro, M. O., Baker, R., Bowers, A., & Heffernan, N. (2013). Predicting college enrollment from student interaction with an intelligent tutoring system in middle school Educational Data Mining 2013, https://www.sweetsanpedro.com/research/EDM2013.pdf
Pelánek, R. (2014). A Brief Overview of Metrics for Evaluation of Student Models EDM (Workshops), https://doi.org/10.1.1.444.4198
Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep Knowledge Tracing. arXiv pre-print server. https://doi.org/arxiv:1506.05908
Prada, M. Á., Domínguez, M., Vicario, J. L., Alves, P. A. V., Barbu, M., Podpora, M., Spagnolini, U., Pereira, M. J. V., & Vilanova, R. (2020). Educational Data Mining for Tutoring Support in Higher Education: A Web-Based Tool Case Study in Engineering Degrees. IEEE Access, 8, 212818-212836. https://doi.org/10.1109/ACCESS.2020.3040858
Pratiwi, N. D., Agustini, F., & Widyaningrum, A. (2019). Pengaruh motivasi belajar pada Penerapan model kooperatif tipe team Assisted individualization (TAI) terhadap hasil belajar IPA siswa kelas III SD Negeri Puri 03 Pati. Jurnal Refleksi Pembelajaran (JRP), 4(1), 9-13. https://ejurnalkotamadiun.org/index.php/JRP/article/view/270
Pu, S., Yudelson, M., Ou, L., & Huang, Y. (2020). Deep Knowledge Tracing with Transformers. In (pp. 252-256). Springer International Publishing. https://doi.org/10.1007/978-3-030-52240-7_46
Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game design and the development of a checklist for getting computational thinking into public schools Proceedings of the 41st ACM technical symposium on Computer science education, Milwaukee, Wisconsin, USA. https://doi.org/10.1145/1734263.1734357
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. https://doi.org/10.1109/TSMCC.2010.2053532
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. https://doi.org/10.1016/j.compedu.2007.05.016
Samuel, A. L. (1967). Some Studies in Machine Learning Using the Game of Checkers. II—Recent Progress. IBM Journal of Research and Development, 11(6), 601-617. https://doi.org/10.1147/rd.116.0601
Saputra, H. D., Ismet, F., & Andrizal, A. (2018). Pengaruh motivasi terhadap hasil belajar siswa SMK. Invotek: Jurnal Inovasi Vokasional dan Teknologi, 18(1), 25-30. https://doi.org/10.24036/invotek.v18i1.168
Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. https://eprints.soton.ac.uk/356481/
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Slee, N. J., & Jacobs, M. H. (2017). Trialling the use of Google Apps together with online marking to enhance collaborative learning and provide effective feedback. F1000Research, 4(177), 177. https://doi.org/10.12688/f1000research.6520.2
Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of asynchronous learning networks, 16(3), 51-61. https://doi.org/10.24059/olj.v16i3.275
Su, Y.-S., & Lai, C.-F. (2021). Applying educational data mining to explore viewing behaviors and performance with flipped classrooms on the social media platform Facebook. Frontiers in Psychology, 12, 653018. https://doi.org/10.3389/fpsyg.2021.653018
Sun, J. C.-Y., & Hsieh, P.-H. (2018). Application of a gamified interactive response system to enhance the intrinsic and extrinsic motivation, student engagement, and attention of English learners. Journal of Educational Technology & Society, 21(3), 104-116. http://www.jstor.org/stable/26458511
Sun, Y., Wang, L., Xie, Q., Dong, Y., & Lin, X. (2020). Online Programming Education Modeling and Knowledge Tracing International Conference on Knowledge Science, Engineering and Management, https://doi.org/10.1007/978-3-030-55130-8_23
Tang, S., Peterson, J. C., & Pardos, Z. A. (2016). Deep neural networks and how they apply to sequential education data Proceedings of the Third (2016) ACM Conference on Learning@ Scale, https://doi.org/10.1145/2876034.2893444
Thuraisingham, B. (2000). A primer for understanding and applying data mining. It Professional, 2(1), 28-31. https://doi.org/10.1109/6294.819936
Torre, M. V., Tan, E., & Hauff, C. (2020). edX log data analysis made easy https://dx.doi.org/10.1145/3375462.3375510
Tovar, E., & Soto, Ó. (2010). The use of competences assessment to predict the performance of first year students 2010 IEEE Frontiers in Education Conference (FIE), https://doi.org/10.1109/FIE.2010.5673566
Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2021). The Computational Thinking Scale for Computer Literacy Education. Journal of Educational Computing Research, 59(4), 579-602. https://doi.org/10.1177/0735633120972356
Voos, R. (2003). Blended learning: What is it and where might it take us. Sloan-C View, 2(1), 2-5.
Wang, L., Sy, A., Liu, L., & Piech, C. (2017a). Deep Knowledge Tracing On Programming Exercises. In Proceedings of the fourth (2017) ACM conference on learning@ scale (pp. 201-204). https://doi.org/10.1145/3051457.3053985
Wang, L., Sy, A., Liu, L., & Piech, C. (2017b). Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning. International Educational Data Mining Society. https://eric.ed.gov/?id=ED596596
Wang, T., & Mitrovic, A. (2002). Using neural networks to predict student′s performance International Conference on Computers in Education, 2002. Proceedings., https://doi.org/10.1109/CIE.2002.1186127
Wei, W., Jui-Feng, W., Jun-Ming, S., & Shian-Shyong, T. (2004, 20-23 Oct. 2004). Learning portfolio analysis and mining in SCORM compliant environment. 34th Annual Frontiers in Education, 2004. FIE 2004.,
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118
Xiao, L. L., & Rahman, S. S. B. A. (2017). Predicting learning styles based on students′ learning behaviour using correlation analysis. Current Science, 113(11), 2090-2096. http://www.jstor.org/stable/26494920
Xiong, X., Zhao, S., Van Inwegen, E. G., & Beck, J. E. (2016). Going deeper with deep knowledge tracing. International Educational Data Mining Society.
Yan, Y., Hara, K., Nakano, H., Kazuma, T., & He, A. (2016). A method to describe student learning status for personalized computer programming e-learning environment 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), https://doi.org/10.1109/AINA.2016.38
Yang, H., & Cheung, L. P. (2018). Implicit heterogeneous features embedding in deep knowledge tracing. Cognitive Computation, 10(1), 3-14. https://doi.org/10.1007/s12559-017-9522-0
Zhang, J.-H., Meng, B., Zou, L.-C., Zhu, Y., & Hwang, G.-J. (2021). Progressive flowchart development scaffolding to improve university students’ computational thinking and programming self-efficacy. Interactive Learning Environments, 1-18. https://doi.org/10.1080/10494820.2021.1943687
Zhang, L., Xiong, X., Zhao, S., Botelho, A., & Heffernan, N. T. (2017). Incorporating rich features into deep knowledge tracing Proceedings of the fourth (2017) ACM conference on learning@ scale, https://doi.org/10.1145/3051457.3053976
Zhu, R., Zhang, D., Han, C., Gao, M., Lu, X., Qian, W., & Zhou, A. (2021). Programming Knowledge Tracing: A Comprehensive Dataset and A New Model. arXiv preprint arXiv:2112.08273. https://doi.org/10.48550/arXiv.2112.08273

指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2022-7-26

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