博碩士論文 109522115 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:71 、訪客IP:18.191.120.117
姓名 董育汝(Yu-Ru Tung)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 透過分析程式設計模式提供干預措施以提升學習成效
(Interventions to enhance learning outcomes by analyzing coding patterns)
相關論文
★ 應用智慧分類法提升文章發佈效率於一企業之知識分享平台★ 家庭智能管控之研究與實作
★ 開放式監控影像管理系統之搜尋機制設計及驗證★ 資料探勘應用於呆滯料預警機制之建立
★ 探討問題解決模式下的學習行為分析★ 資訊系統與電子簽核流程之總管理資訊系統
★ 製造執行系統應用於半導體機台停機通知分析處理★ Apple Pay支付於iOS平台上之研究與實作
★ 應用集群分析探究學習模式對學習成效之影響★ 應用序列探勘分析影片瀏覽模式對學習成效的影響
★ 一個以服務品質為基礎的網際服務選擇最佳化方法★ 維基百科知識推薦系統對於使用e-Portfolio的學習者滿意度調查
★ 學生的學習動機、網路自我效能與系統滿意度之探討-以e-Portfolio為例★ 藉由在第二人生內使用自動對話代理人來改善英文學習成效
★ 合作式資訊搜尋對於學生個人網路搜尋能力與策略之影響★ 數位註記對學習者在線上學習環境中反思等級之影響
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-8-1以後開放)
摘要(中) 在現今的程式教學中,線上程式系統已經成為大家常用的方式,允許學生可以不受時間和地點限制的練習程式設計。此外,因應疫情的影響,遠距課程需求日益增加,學生需要在沒有教師面對面指導的情況下獨立調整自己的學習。在過去的研究中發現透過行為分析相較於考試成績預測自我調節學習的能力更好,程式設計行為分析也可以幫助教師更了解學生的學習狀況和程式設計的過程。除此之外,線上程式系統的應用分成教育中程式評量、線上編譯器等,在過去的研究中大多只分析單一應用的行為,且大多僅透過作答次數、錯誤次數、時間等特徵,其分析結果容易忽略程式內容。
本研究將學生使用線上程式系統中的程式設計行為,定義出於練習(線上編譯)及程式評量中的各種狀態,生成程式設計行為序列並轉換為機率轉移矩陣,透過K-Means++分群演算法分析出9種程式設計方式。為了進一步研究程式設計方式,將學生停留於各個程式設計方式的機率與程式測驗成績進行相關性分析,發現邏輯及語法極為困擾模式停留機率高的學生與程式測驗成績有顯著的負相關。接著,透過學生在各個程式設計方式的停留機率區分出3種不同的程式設計模式(精熟群、困擾群、放棄群)的學生。為了了解各個程式設計模式的學生特性,本研究將各個程式設計模式的學生在程式測驗成績、自我調節學習能力各面向、各個程式設計方式的停留機率進行變異數分析。最後針對不同程式設計模式的學生特性,給予相對應的回饋,並推薦邏輯及語法極為困擾模式停留機率高的學生對應的練習題目。
研究結果顯示各個程式設計方式的停留機率與程式測驗成績、自我調節學習能力具有不同程度關聯性,而程式設計模式不同的學生雖然在程式測驗成績上沒有顯著差異,但在各個程式設計方式的停留機率及自我調節學習之複誦、組織、控制信念能力上亦有不同程度之顯著的差異。在干預措施之成效上,實驗組的學生在程式語法上相較於干預前更為熟悉,且相較於控制組在程式測驗成績上的表現顯著較佳,但在自我調節學習各面向的能力上則較無顯著差異。
摘要(英) In recent programming teaching, the online programming system has become a common method, allowing students to practice coding regardless of time and place. In addition, with the increasing demand for distance courses in response to the impact of Covid-19, students need to independently adjust their learning without face-to-face guidance from teachers. In past studies, it has been found that behavioral analysis is better than test scores to predict the ability of self-regulated learning, and programming behavioral analysis can also help teachers better understand students′ learning status and coding process. In addition, the application of the online programming system is divided into program evaluation in education, online compiler, etc. In the past research, most of them only analyzed the behavior of a single application, and most of them only analyzed the characteristics of the number of answers, the number of errors, and the time, and the analysis results tend to ignore the program content.
In this study, students use the programming behavior in the online programming system to define various states in practice and evaluation, generate the programming behavior sequence and convert it into a probability transition matrix, through the K-Means++ clustering algorithm separated 9 coding modes. In order to further study the coding modes, the correlation analysis was carried out between the probability of students staying in each coding mode and the scores of the programming test. Then, students with 3 different coding patterns (adept, struggle, and abandonment group) were distinguished by the students′ staying probability in each coding mode. In order to understand the characteristics of students of each coding pattern, we conducted an ANOVA analysis of the students of each coding pattern in the programming exam scores, self-regulated learning, and the probability of staying in each coding mode. Finally, according to the characteristics of students with different coding patterns, give corresponding feedback, and recommend the corresponding practice questions for students who are extremely troubled by logic and syntax and have a high probability of staying in the mode.
The study results show that the probability of staying in each coding mode is related to the programming exam scores and self-regulation learning to varying degrees. Although there is no significant difference in the programming exam scores of students with different coding patterns, the probability of staying in each coding mode is significant differences in different degrees in the ability to rehearsal, organization, and control beliefs. In terms of the effect of the intervention, the students in the experimental group were more familiar with the syntax of the program than before the intervention, and were significantly better than the control group in the performance of the programming exam. In addition, there was no significant difference in all aspects of self-regulated learning.
關鍵字(中) ★ 程式設計模式
★ 程式設計方式
★ 干預措施
★ 學習成效
★ 自我調節學習
★ 線上程式系統
關鍵字(英) ★ Coding pattern
★ Coding mode
★ Intervention
★ Learning outcome
★ Self-regulated learning
★ Online programming system
論文目次 摘要 i
Abstract iii
致謝 v
目錄 vi
圖目錄 viii
表目錄 ix
1. 緒論 1
2. 文獻探討 2
2.1. 自我調節學習(Self-Regulated Learning, SRL) 2
2.2. 程式設計行為分析(Coding behavior analysis) 3
2.3. 程式設計干預措施 5
3. 基於Coding patterns的干預措施 6
4. 研究方法 8
4.1. Online Judge程式評量系統 8
4.2. VisCode程式練習系統 9
4.3. 程式設計狀態序列 Sequence of coding states 10
4.4. 機率轉移矩陣 Probability Transition Matrix 11
4.5. Coding mode 12
4.5.1. Coding mode 1: 精熟 13
4.5.2. Coding mode 2: 重複練習,確認再評量 13
4.5.3. Coding mode 3: 重複評量,確認已通過 14
4.5.4. Coding mode 4: 評量後仍重複練習 15
4.5.5. Coding mode 5: 即時修正語法錯誤 15
4.5.6. Coding mode 6: 較易忽略邏輯條件 16
4.5.7. Coding mode 7: 較少練習且評量常有錯誤 17
4.5.8. Coding mode 8: 程式邏輯方面極為困擾 17
4.5.9. Coding mode 9: 程式語法方面極為困擾 18
4.6. 相關性分析 Correlation analysis 19
4.7. Coding pattern 21
4.8. 干預措施 Intervention 24
4.8.1. 推薦題目 24
4.8.2. 回饋 26
5. 實驗設計 28
5.1. 實驗對象 28
5.2. 實驗流程 28
5.3. 自我調節學習問卷 29
6. 結果 30
6.1. Coding patterns不同的學生,程式測驗成績、自我調節學習能力是否不同 30
6.2. 基於Coding patterns的干預措施對於不同patterns的學生影響是否不同 33
6.3. 基於Coding patterns的干預措施是否能提升程式測驗成績 36
6.4. 基於Coding patterns的干預措施是否能提升自我調節學習能力 38
7. 討論與結論 41
參考文獻 44
附錄 49
自我調節學習問卷(MSLQ) 49
參考文獻 Arakawa, K., Hao, Q., Greer, T., Ding, L., Hundhausen, C. D., & Peterson, A. (2021). In Situ Identification of Student Self-Regulated Learning Struggles in Programming Assignments. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education,
Araya, I., Beas, V., Stamulis, K., & Allende-Cid, H. (2022). Predicting student performance in computing courses based on programming behavior. Computer Applications in Engineering Education. https://doi.org/10.1002/cae.22519
Arthur, D., & Vassilvitskii, S. (2006). k-means++: The advantages of careful seeding.
Auvinen, T. (2015). Harmful study habits in online learning environments with automatic assessment. 2015 International Conference on Learning and Teaching in Computing and Engineering,
Bai, Y., Chen, L., Yin, G., Mao, X., Deng, Y., Wang, T., Lu, Y., & Wang, H. (2017). Quantitative analysis of learning data in a programming course. International Conference on Database Systems for Advanced Applications,
Bey, A., & Champagnat, R. (2021). An Exploratory Study to Identify Learners′ Programming Behavior Interactions. 2021 International Conference on Advanced Learning Technologies (ICALT),
Bey, A., Pérez-Sanagustín, M., & Broisin, J. (2019). Unsupervised automatic detection of learners’ programming behavior. European Conference on Technology Enhanced Learning,
Brame, C. J., & Biel, R. (2015). Test-enhanced learning: the potential for testing to promote greater learning in undergraduate science courses. CBE—Life Sciences Education, 14(2), es4.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of educational research, 65(3), 245-281.
Campbell, J., Horton, D., & Craig, M. (2016). Factors for success in online CS1. Proceedings of the 2016 acm conference on innovation and technology in computer science education,
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,
Carter, A. S., Hundhausen, C. D., & Adesope, O. (2017). Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education (TOCE), 17(3), 1-20.
Castellanos, H., Restrepo-Calle, F., González, F. A., & Echeverry, J. J. R. (2017). Understanding the relationships between self-regulated learning and students source code in a computer programming course. 2017 IEEE Frontiers in Education Conference (FIE),
Chen, H.-M., Nguyen, B.-A., Yan, Y.-X., & Dow, C.-R. (2020). Analysis of learning behavior in an automated programming assessment environment: A code quality perspective. IEEE Access, 8, 167341-167354.
Chung, C.-Y., Paredes, Y. V. M., Alzaid, M., Papakannu, K. R., & Hsiao, I.-H. (2020). A Longitudinal Study on Student Persistence in Programming Self-assessments. CSEDM@ EDM,
Cohen, J. (1988). Statistical power analysis Jbr the behavioral. Sciences. Hillsdale (NJ): Lawrence Erlbaum Associates, 18-74.
Echeverry, J. J. R., Rosales-Castro, L. F., Restrepo-Calle, F., & González, F. A. (2018). Self-regulated learning in a computer programming course. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(2), 75-83.
Estey, A., Keuning, H., & Coady, Y. (2017). Automatically classifying students in need of support by detecting changes in programming behaviour. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education,
Fu, X., Shimada, A., Ogata, H., Taniguchi, Y., & Suehiro, D. (2017). Real-time learning analytics for C programming language courses. Proceedings of the seventh international learning analytics & knowledge conference,
Fuchs, D., Mock, D., Morgan, P. L., & Young, C. L. (2003). Responsiveness‐to‐intervention: Definitions, evidence, and implications for the learning disabilities construct. Learning Disabilities Research & Practice, 18(3), 157-171.
Gao, G., Marwan, S., & Price, T. W. (2021). Early performance prediction using interpretable patterns in programming process data. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education,
González-Pienda, J. A., Fernández, E., Bernardo, A., Núñez, J. C., & Rosário, P. (2014). Assessment of a self-regulated learning intervention. The Spanish Journal of Psychology, 17.
Hawlitschek, A., Köppen, V., Dietrich, A., & Zug, S. (2019). Drop-out in programming courses–prediction and prevention. Journal of Applied Research in Higher Education.
Hsiao, I.-H., Huang, P.-K., & Murphy, H. (2017). Uncovering reviewing and reflecting behaviors from paper-based formal assessment. Proceedings of the seventh international learning analytics & knowledge conference,
Huang, T.-C., Shu, Y., Chang, S.-H., Huang, Y.-Z., Lee, S.-L., Huang, Y.-M., & Liu, C.-H. (2014). Developing a self-regulated oriented online programming teaching and learning system. 2014 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE),
Kato, T., Kambayashi, Y., Terawaki, Y., & Kodama, Y. (2017). Analysis of students’ behaviors in programming exercises using deep learning. International Conference on Smart Education and Smart E-Learning,
Kiran, E. L., & Moudgalya, K. M. (2015). Evaluation of programming competency using student error patterns. 2015 International Conference on Learning and Teaching in Computing and Engineering,
Kurnia, A., Lim, A., & Cheang, B. (2001). Online judge. Computers & Education, 36(4), 299-315.
López-Pernas, S., Saqr, M., & Viberg, O. (2021). Putting it all together: Combining learning analytics methods and data sources to understand students’ approaches to learning programming. Sustainability, 13(9), 4825.
Law, C.-Y., Grundy, J., Cain, A., Vasa, R., & Cummaudo, A. (2017). User perceptions of using an open learner model visualisation tool for facilitating self-regulated learning. Proceedings of the Nineteenth Australasian Computing Education Conference,
Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016). Learning to program: Gender differences and interactive effects of students′ motivation, goals, and self-efficacy on performance. Proceedings of the 2016 ACM Conference on International Computing Education Research,
Lu, O. H., Huang, A. Y., Huang, J. C., Huang, C. S., & Yang, S. J. (2016). Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners′ Engagement Rate. 2016 International Conference on Educational Innovation through Technology (EITT),
Luo, J., & Wang, T. (2020). Analyzing Students′ Behavior in Blended Learning Environment for Programming Education. Proceedings of the 2020 The 2nd World Symposium on Software Engineering,
Luxton-Reilly, A., Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., & Szabo, C. (2018). Introductory programming: a systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education,
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability,
Magno, C. (2011). Validating the academic self-regulated learning scale with the motivated strategies for learning questionnaire (MSLQ) and learning and study strategies inventory (LASSI). The International Journal of Educational and Psychological Assessment, 7(2).
McKay, T., Miller, K., & Tritz, J. (2012). What to do with actionable intelligence: E2Coach as an intervention engine. Proceedings of the 2nd international conference on learning analytics and knowledge,
Morshed Fahid, F., Tian, X., Emerson, A., B. Wiggins, J., Bounajim, D., Smith, A., Wiebe, E., Mott, B., Elizabeth Boyer, K., & Lester, J. (2021). Progression Trajectory-Based Student Modeling for Novice Block-Based Programming. Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization,
Ott, C., Robins, A., Haden, P., & Shephard, K. (2015). Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1. Computer Science Education, 25(2), 174-198.
Öztürk, M. (2021). The effect of self-regulated programming learning on undergraduate students’ academic performance and motivation. Interactive Technology and Smart Education.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8, 422.
Paredes, Y. V., & Hsiao, I.-H. (2021). WebPGA: An Educational Technology That Supports Learning by Reviewing Paper-Based Programming Assessments. Information, 12(11), 450.
Pedrosa, D., Cravino, J., Morgado, L., & Barreira, C. (2017). Self-regulated learning in higher education: strategies adopted by computer programming students when supported by the SimProgramming approach. Production, 27.
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Handbook of self-regulation (pp. 451-502). Elsevier.
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychological measurement, 53(3), 801-813.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
Silva, L. (2021). Fostering Programming Students Regulation of Learning Using a Computer-Based Learning Environment. 2021 International Symposium on Computers in Education (SIIE),
Song, D., Hong, H., & Oh, E. Y. (2021). Applying computational analysis of novice learners′ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, 120, 106746.
Wasik, S., Antczak, M., Badura, J., Laskowski, A., & Sternal, T. (2018). A survey on online judge systems and their applications. ACM Computing Surveys (CSUR), 51(1), 1-34.
Watson, C., Li, F. W., & Godwin, J. L. (2013). Predicting performance in an introductory programming course by logging and analyzing student programming behavior. 2013 IEEE 13th international conference on advanced learning technologies,
Wong, B. T.-m., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7-28.
Yang, S. J. (2021). Guest Editorial: Precision Education-A New Challenge for AI in Education. Journal of Educational Technology & Society, 24(1).
Zhao, X., Zhang, J., Li, W., Kahn, K., Lu, Y., & Winters, N. (2021). Learners’ non-cognitive skills and behavioral patterns of programming: A sequential analysis. 2021 International Conference on Advanced Learning Technologies (ICALT),
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Elsevier.
Zinovieva, I., Artemchuk, V., Iatsyshyn, A. V., Popov, O., Kovach, V., Iatsyshyn, A. V., Romanenko, Y., & Radchenko, O. (2021). The use of online coding platforms as additional distance tools in programming education. Journal of Physics: Conference Series,
指導教授 楊鎮華(Jhen-Hua Yang) 審核日期 2022-7-11
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明