博碩士論文 109554024 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:91 、訪客IP:3.147.85.108
姓名 羅健瑋(Chien-Wei Lo)  查詢紙本館藏   畢業系所 網路學習科技研究所
論文名稱 基於文本型程式編寫紀錄之自我調節儀表板於程式設計學習成效探究
(A Study of Applying Self-Regulation Dashboard Based on Text-based Programming Log to Enhance rogramming Learning)
相關論文
★ 基於間隔效應與知識追蹤之適性化學習演算法系統設計與應用:以多益英語學習為例★ 結合社會調節學習平台與教中學課程設計以增進大學生視覺化資料分析能力與調節學習
★ 以深度知識追蹤模型應用於程式學習系統★ 結合聊天機器人與推薦系統之閱讀學伴應用於國小閱讀
★ 視覺化閱讀歷程系統於國小身教式持續安靜閱讀之應用★ 結合重新設計之翻轉教室模型與視覺化分析系統對於程式設計學習之影響
★ 結合視覺化儀表板與合作腳本輔助VR共創活動以探討國小學童之學習行為、情感與認知參與★ 結合視覺化儀表板之專案管理平台於在職學生專案能力與資料分析學習之影響
★ 專題導向學習與調節學習儀表板應用於資料視覺化在職課程
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) 程式設計能力已成為影響學生未來競爭力的必備技能之一,程式教育的需求也隨之遽增。以數位學習系統蒐集分析學生學習歷程資料,有助於學生了解監控學習進度,增強自我調節能力。而探索程式編寫行為紀錄可供教師在學期中預測學生學習表現,有效給予學習輔助。此外,在程式設計中使用問題導向教學可促進學生技能和態度的發展,培養學生具備問題解決能力。
因此,本研究開發自我調節儀表板融入學習環境中,設計問題導向的任務作業促使學生主動參與學習,課程中使用Moodle學習平台與Jupyter Notebook專用伺服器上撰寫程式和自我調節儀表板。系統中社會調節功能包含:高分觀摩、學習建議等,學生可透過系統觀看同儕的學習成果能作為參考與目標,修正自己的學習策略提升程式技能,實踐自我學習和培養問題解決能力。研究對象為台灣北部某大學19名研究生,課程共計18週,前後測評估學生的知識掌握程度,問卷包括與程式態度和自我調節有關的項目。
本研究探討學生在學習分析儀表板對自我調節的影響與問題導向提高社會調節,主要結果分為以下四點:(1)使用自我調節儀表板幫助下學生時間管理上有顯著提升。(2)通過問題導向式學習方法,學生表示希望有更多的分組討論學習機會與提升同儕之間的互動學習行為,觀摩優秀作品提高學生對自己和他人學習過程的認識,提高共同調節與社會共享調節的互動。(3)程式編寫行為預測結果顯示,使用累計至第七週的資料來預測程式能力成績,結果有達0.70準確率的表現,可判斷這些特徵有學習預警的潛能。(4)學生常見的錯誤類型進行分群分析,發現成績較高的學生名稱錯誤次數普遍較低,可作為教學方向的改善參考依據。因此,本研究證實基於文本型程式編寫紀錄之自我調節儀表板可提高程式設計學習成效有效促進學生社會調節學習,本研究之系統與教學策略可作為將來研究者參照實行。
摘要(英) Programming skills have become one of the essential skills that affect students′ future competitiveness, and the demand for programming learning has increased dramatically. The learning system can collect and analyze students′ learning portfolio data to help them understand and monitor their learning progress and enhance their self-regulation. Exploring students’ programming logs allows teachers to predict student performance during the semester and effectively support students’ learning. In addition, using problem-based learning instruction in programming provides context to facilitate the development of students′ skills and attitudes and the development of problem-solving skills.
Therefore, this study developed a self-regulation dashboard, integrated it into the learning environment, and designed problem-based tasks to motivate students to participate in learning actively. The course applies the Moodle system, the self-regulation dashboard, and a server for Jupyter Notebook to write programs. In addition, students can use the system to observe their peer′s learning results and use them as a reference and target to modify their learning strategies to improve their programming skills, practice self-learning, and develop problem-solving skills. There are 19 graduate students from a university in northern Taiwan who participated in this study for 18 weeks. The pre/post-test and questionnaires were executed to assess the students′ learning performance and self-regulation perceptions.
In this study, we investigate the impact of students′ learning analytic dashboard on the self-regulated and problem-based improvement of social regulation. The results indicate that (1) Students showed significant improvement in time management with the self-regulation dashboard′s help. (2) Students desire more group discussions and interactive learning behaviors among peers through the problem-based learning approach. They also observed excellent work from peers to improve their learning and the interaction for co-regulation and socially shared regulation. (3) The results of the prediction of programming behaviors showed that the cumulative data up to week 7 were used to predict the learning performance with an accuracy of 0.70, which can be potential as a learning early warning. (4) The name error seldom happened to students with higher scores after analyzing the general type of error. This study confirms that the self-regulation dashboard based on text-based programming records can improve the effectiveness of programming learning and effectively facilitate students′ socially-regulated learning.
關鍵字(中) ★ 自我調節
★ 社會調節
★ 問題導向學習
★ 學習分析儀表板
★ 程式設計學習
關鍵字(英) ★ Self-regulation
★ Social regulation
★ Problem-based learning
★ Learning analytic dashboards
★ Programming learning
論文目次 中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
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 2
1-3研究問題 3
1-4名詞定義 3
二、文獻探討 4
2-1程式設計教學 4
2-1-1運算思維重要性 5
2-1-2程式設計教學相關研究 6
2-1-3程式設計教學創新方法 7
2-2問題導向式學習 8
2-2-1學習動機與問題解決 9
2-2-2科技應用與問題導向 10
2-3社會調節 11
2-3-1自我調節學習重要性 11
2-3-2合作學習重要性 13
2-3-3社會調節相關研究 14
2.4教育資料探勘 16
2-4-1學習分析 18
2-4-2學習分析儀表板 20
三、研究方法 22
3-1 參與者 22
3-2課程設計 22
3-3教學系統的建置 24
3-4工具 25
3-4-1測驗及問卷 26
3-4-2質性編碼方式 28
3-4-3統計檢定 28
3-4-4分析預測 29
四、自我調節儀表板建置 31
4.1 學生儀表板 31
4.2 教師儀表板 37
五、研究結果 44
5-1 程式設計學習成效 44
5-1-1測驗成績 44
5-1-2 k-Means分群 44
5-1-3 教學輔助工具 45
5-2學習分析儀表板發現自我調節學習結構的變化 48
5-2-1自我調節學習結構的變化 48
5-2-2各組自我調節學習結構的變化 49
5-2-3自我調節與儀表板使用次數 51
5-2-4開放式問題自我調節學習結構的變化 52
5-2-5使用者對課程儀表板的反應與回饋 52
5-3問題導向程式態度的變化 54
5-3-1程式態度變化 54
5-3-2各組程式態度變化 55
5-3-3對於問題導向學習促進社會調節達到同儕學習 57
5-4 機器學習預測期末測驗 57
5-5學生不同的特徵差異分群 59
六、討論 64
6-1課程儀表板對自我調節的探討 64
6-2問題導向對程式態度的探討 65
七、結論與建議 67
7-1結論 67
7-1-1學習儀表板可幫助學生自我調節,且協助教師快速掌握學生學習情形 67
7-1-2問題導向式預習作業有利於同儕的相互學習,提升學生社會調節能力 67
7-1-3分析學生程式編寫行為,能有效預測學生學習成效,以利教師輔助學生 68
7-1-4不同類型語法錯誤次數能影響學生期末成績,可依據錯誤類型提供教學改善參考 68
7-2研究限制 69
7-3未來展望 69
參考文獻 70
附錄 84
附錄1、知情同意書 84
附錄2、前測驗內容 86
附錄3、背景和自我調節與程式態度問卷 92
附錄4、課程雷達圖中自我調節問卷 100
附錄5、開放式問題與工具問卷 101
附錄6、後測驗內容 104
附錄7、作業任務實作題 111
附錄8、學生使用儀表板與優秀作品分享討論 113
參考文獻 參考文獻
吳宥葶, 孫之元, & 李威儀. (2013). 大專院校開放式課程學習者之自我調節問卷研發與編製. 國立臺灣科技大學人文社會學報, 9(3), 189-208. https://doi.org/10.29506/JLASS
陳雅玲. (2021). 於混合學習中基於學習分析儀表板系統應用之自我調節學習方法. 數位學習科技期刊, 13(3), 27-42. https://doi.org/10.3966/2071260X2021071303002
Ackermann, E. (1996). Constructionist in practice: Designing, thinking, and learning in a digital world. Routledge.
Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527-1543. https://doi.org/10.1007/s10639-018-9839-7
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/10.1016/j.heliyon.2019.e01250
Agarwal, S. (2012). Data Mining in Education: Data Classification and Decision Tree Approach. International Journal of e-Education, e-Business, e-Management and e-Learning, 2(2), 140. https://doi.org/10.7763/ijeeee.2012.V2.97
Aguilar, S. J., Karabenick, S. A., Teasley, S. D., & Baek, C. (2021). Associations between learning analytics dashboard exposure and motivation and self-regulated learning. Computers & Education, 162, 104085. https://doi.org/10.1016/j.compedu.2020.104085
Ala-Mutka, K. M. (2005). A Survey of Automated Assessment Approaches for Programming Assignments. Computer Science Education, 15(2), 83-102. https://doi.org/10.1080/08993400500150747
Ali, L., Hatala, M., Gasevic, D., & Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470-489. https://doi.org/10.1016/j.compedu.2011.08.030
Almarabeh, H. (2017). Analysis of Students′ Performance by Using Different Data Mining Classifiers. International Journal of Modern Education and Computer Science, 9(8), 9-15. https://doi.org/10.5815/ijmecs.2017.08.02
Altadmri, A., & Brown, N. C. (2015). 37 million compilations: Investigating novice programming mistakes in large-scale student data. Proceedings of the 46th ACM technical symposium on computer science education, 522-527. https://doi.org/10.1145/2676723.2677258
Altintas, T., Gunes, A., & Sayan, H. (2016). A peer-assisted learning experience in computer programming language learning and developing computer programming skills. Innovations in Education and Teaching International, 53(3), 329-337. https://doi.org/10.1080/14703297.2014.993418
Ananiadou, K., & Claro, M. (2009). 21st Century Skills and Competences for New Millennium Learners in OECD Countries. OECD Education Working Papers, No. 41. OECD Publishing (NJ1). https://doi.org/10.1787/218525261154
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students′ performance using educational data mining. Computers & Education, 113, 177-194. https://doi.org/10.1016/j.compedu.2017.05.007
Astrachan, O., Hambrusch, S., Peckham, J., & Settle, A. (2009). The present and future of computational thinking. ACM SIGCSE Bulletin, 41(1), 549-550. https://doi.org/10.1145/1539024.1509053
Azevedo, A. (2019). Data Mining and Knowledge Discovery in Databases. In Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics (pp. 502-514). IGI Global. https://doi.org/10.4018/978-1-5225-7598-6.ch037
Baepler, P., & Murdoch, C. (2010). Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217
Baker, R. (2010). Data mining for education. International encyclopedia of education, 7(3), 112-118. https://doi.org/10.1016/B978-0-08-044894-7.01318-X
Baker, R., & Siemens, G. (2014). Learning analytics and educational data mining. Cambridge handbook of the leaning sciences (2nd edn). Cambridge University Press: New York, NY, 253-272. https://doi.org/10.1017/CBO9781139519526.016
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM| Journal of Educational Data Mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657
Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23(1), 537-553. https://doi.org/10.1007/s10639-017-9616-z
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2010). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12. ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based learning: An approach to medical education (Vol. 1). Springer Publishing Company.
Beck, L., & Chizhik, A. (2013). Cooperative Learning Instructional Methods for CS1: Design, Implementation, and Evaluation. ACM Transactions on Computing Education, 13(3), 1-21. https://doi.org/10.1145/2492686
Bers, M. U. (2008). Blocks to Robots Learning with Technology in the Early Childhood Classroom. Teachers College Press, New York.
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
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012). Defining Twenty-First Century Skills. In Assessment and Teaching of 21st Century Skills (pp. 17-66). Springer. https://doi.org/10.1007/978-94-007-2324-5_2
Bjögvinsson, E., Ehn, P., & Hillgren, P.-A. (2012). Design Things and Design Thinking: Contemporary Participatory Design Challenges. Design Issues, 28(3), 101-116. https://doi.org/10.1162/DESI_a_00165
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, 110-116. https://doi.org/10.1145/2090116.2090132
Bodily, R., & Verbert, K. (2017). Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems. IEEE Transactions on Learning Technologies, 10(4), 405-418. https://doi.org/10.1109/Tlt.2017.2740172
Bossert, S. T. (2016). Chapter 6: Cooperative Activities in the Classroom. Review of Research in Education, 15(1), 225-250. https://doi.org/10.3102/0091732x015001225
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada, 1, 25. http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
Bull, G., Garofalo, J., & Hguyen, N. R. (2020). Thinking about computational thinking. Journal of Digital Learning in Teacher Education, 36(1), 6-18. https://doi.org/10.1080/21532974.2019.1694381
Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69. http://www.inf.ed.ac.uk/publications/online/1245.pdf
Butler, D. L., & Winne, P. H. (1995). Feedback and Self-Regulated Learning - a Theoretical Synthesis. Review of Educational Research, 65(3), 245-281. https://doi.org/10.3102/00346543065003245
Chang, C. S., Chung, C. H., & Chang, J. A. (2020). Influence of problem-based learning games on effective computer programming learning in higher education. Etr&D-Educational Technology Research and Development, 68(5), 2615-2634. https://doi.org/10.1007/s11423-020-09784-3
Chang, Y.-H., Song, A.-C., & Fang, R.-J. (2018). Integrating ARCS Model of Motivation and PBL in Flipped Classroom: a Case Study on a Programming Language. EURASIA Journal of Mathematics, Science and Technology Education, 14(12). https://doi.org/10.29333/ejmste/97187
Chao, J. Y., Tzeng, P. W., & Po, H. Y. (2016). The Study of Problem Solving Process of E-book PBL Course of Atayal Senior High School Students in Taiwan. EURASIA Journal of Mathematics, Science and Technology Education, 13(3), 1001-1012. https://doi.org/10.12973/eurasia.2017.00654a
Chen, C. H., & Su, C. Y. (2019). Using the BookRoll E-Book System to Promote Self-Regulated Learning, Self-Efficacy and Academic Achievement for University Students. Educational Technology & Society, 22(4), 33-46. https://www.jstor.org/stable/26910183
Chen, C. Y., & Teng, K. C. (2011). The design and development of a computerized tool support for conducting senior projects in software engineering education. Computers & Education, 56(3), 802-817. https://doi.org/10.1016/j.compedu.2010.10.022
Chis, A. E., Moldovan, A. N., Murphy, L., Pathak, P., & Muntean, C. H. (2018). Investigating Flipped Classroom and Problem-based Learning in a Programming Module for Computing Conversion Course. Educational Technology & Society, 21(4), 232-247. https://www.jstor.org/stable/26511551
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29. https://doi.org/10.1109/tlt.2016.2616312
Corrin, L., & De Barba, P. (2015). How do students interpret feedback delivered via dashboards? https://doi.org/10.1145/2723576.2723662
da Silva Cintra, C., & Bittencourt, R. A. (2015). Being a PBL teacher in computer engineering: an interpretative phenomenological analysis. 2015 IEEE Frontiers in Education Conference (FIE), 1-8. https://doi.org/10.1109/FIE.2015.7344234
Daly, J. E. (2009). Special Issue. Journal of Educational Technology Systems, 37(3), 247-249. https://doi.org/10.2190/ET.37.3.a
Delisle, R. (1997). How to use problem-based learning in the classroom. Ascd.
Dembo, M. H., & Eaton, M. J. (2000). Self-regulation of academic learning in middle-level schools. Elementary School Journal, 100(5), 473-490. https://doi.org/10.1086/499651
dos Santos, S. C., Reis, P. B. S., Reis, J. F. S., & Tavares, F. (2021). Two Decades of PBL in Teaching Computing: A Systematic Mapping Study. IEEE Transactions on Education, 64(3), 233-244. https://doi.org/10.1109/te.2020.3033416
Duke, R., Salzman, E., Burmeister, J., Poon, J., & Murray, L. (2000). Teaching programming to beginners-choosing the language is just the first step. Proceedings of the Australasian conference on Computing education, 79-86. https://doi.org/10.1145/359369.359381
Durall, E., & Gros, B. (2014). Learning Analytics as a Metacognitive Tool. CSEDU (1), 380-384. https://doi.org/10.5220/0004933203800384
Edens, K. M. (2010). Preparing Problem Solvers for the 21st Century through Problem-Based Learning. College Teaching, 48(2), 55-60. https://doi.org/10.1080/87567550009595813
English, M. C., & Kitsantas, A. (2013). Supporting Student Self-Regulated Learning in Problem- and Project-Based Learning. Interdisciplinary Journal of Problem-Based Learning, 7(2), 6. https://doi.org/10.7771/1541-5015.1339
Ferguson, R., & Shum, S. B. (2011). Learning analytics to identify exploratory dialogue within synchronous text chat. Proceedings of the 1st international conference on learning analytics and knowledge, 99-103. https://doi.org/10.1145/2090116.2090130
Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-Regulated Learning strategies using e-learning tools for Computer Science. Computers & Education, 123, 150-163. https://doi.org/10.1016/j.compedu.2018.05.006
García-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education. Computers in Human Behavior, 80, 407-411. https://doi.org/10.1016/j.chb.2017.12.005
Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., & Warschauer, M. (2019). Predicting Success, Preventing Failure. In Utilizing Learning Analytics to Support Study Success (pp. 249-273). Springer. https://doi.org/10.1007/978-3-319-64792-0_14
Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection. In CHI′12 Extended Abstracts on Human Factors in Computing Systems (pp. 869-884). https://doi.org/10.1145/2212776.2212860
Gravill, J., & Compeau, D. (2008). Self-regulated learning strategies and software training. Information & Management, 45(5), 288-296. https://doi.org/10.1016/j.im.2008.03.001
Greening, T., Kay, J., Kingston, J. H., & Crawford, K. (1996). Problem-based learning of first year computer science. Proceedings of the 1st Australasian conference on Computer science education, 13-18. https://doi.org/10.1145/369585.369588
Grout, V., & Houlden, N. (2014). Taking Computer Science and Programming into Schools: The Glyndŵr/BCS Turing Project. Procedia - Social and Behavioral Sciences, 141, 680-685. https://doi.org/10.1016/j.sbspro.2014.05.119
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
Gupta, S., & Dubey, S. K. (2012). Automatic assessment of programming assignment. Computer Science & Engineering, 2(1), 67. https://doi.org/10.5121/csit.2012.2129
Hadwin, A., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. https://doi.org/10.4324/9781315697048-6
Hadwin, A., & Oshige, M. (2011). Self-Regulation, Coregulation, and Socially Shared Regulation: Exploring Perspectives of Social in Self-Regulated Learning Theory. Teachers College Record, 113(2), 240-264. https://doi.org/10.1177/016146811111300204
Henderson, P. B., Cortina, T. J., & Wing, J. M. (2007). Computational thinking. Proceedings of the 38th SIGCSE technical symposium on Computer science education, 195-196. https://doi.org/10.1145/1227310.1227378
Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16(3), 235-266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learn about complex systems. Journal of the Learning Sciences, 9(3), 247-298. https://doi.org/10.1207/S15327809jls0903_2
Hooshyar, D., Kori, K., Pedaste, M., & Bardone, E. (2019). The potential of open learner models to promote active thinking by enhancing self-regulated learning in online higher education learning environments. British Journal of Educational Technology, 50(5), 2365-2386. https://doi.org/10.1111/bjet.12826
Hou, X., Yang, H.-b., & Liu, J.-b. (2010). A problem-based teaching method in XML course. 2010 5th International Conference on Computer Science & Education, 399-402. https://doi.org/10.1109/ICCSE.2010.5593600
Hussain, S., Abdulaziz Dahan, N., Ba-Alwi, F. M., & Ribata, N. (2018). Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447-459. https://doi.org/10.11591/ijeecs.v9.i2.pp447-459
Hwang, G. J., Wang, S. Y., & Lai, C. L. (2021). Effects of a social regulation-based online learning framework on students′ learning achievements and behaviors in mathematics. Computers & Education, 160. https://doi.org/10.1016/j.compedu.2020.104031
Ifenthaler, D. (2020). Change Management for Learning Analytics. In Artificial Intelligence Supported Educational Technologies (pp. 261-272). Springer. https://doi.org/10.1007/978-3-030-41099-5_15
Ifenthaler, D., & Gibson, D. (2020). Adoption of data analytics in higher education learning and teaching. Springer.
Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21(3), 379-393. https://doi.org/10.1016/j.learninstruc.2010.05.002
Ioannidou, A., Bennett, V., Repenning, A., Koh, K. H., & Basawapatna, A. (2011). Computational Thinking Patterns. Online Submission. http://files.eric.ed.gov/fulltext/ED520742.pdf
Jarvenoja, H., & Jarvela, S. (2009). Emotion control in collaborative learning situations: do students regulate emotions evoked by social challenges? Br J Educ Psychol, 79(Pt 3), 463-481. https://doi.org/10.1348/000709909X402811
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. European Conference on Technology Enhanced Learning, 82-96. https://doi.org/10.1007/978-3-319-66610-5_7
Järvelä, S., & Hadwin, A. F. (2013). New Frontiers: Regulating Learning in CSCL. Educational Psychologist, 48(1), 25-39. https://doi.org/10.1080/00461520.2012.748006
Järvelä, S., Järvenoja, H., & Näykki, P. (2013). Analyzing Regulation of Motivation as an Individual and Social Process: A Situated Approach. In Interpersonal Regulation of Learning and Motivation (pp. 184-201). Routledge. https://doi.org/10.4324/9780203117736-15
Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., Koivuniemi, M., & Järvenoja, H. (2014). Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools. Educational Technology Research and Development, 63(1), 125-142. https://doi.org/10.1007/s11423-014-9358-1
Kalaian, S. A., & Kasim, R. M. (2015). Small-Group vs. Competitive Learning in Computer Science Classrooms. In Innovative Teaching Strategies and New Learning Paradigms in Computer Programming (pp. 46-64). IGI Global. https://doi.org/10.4018/978-1-4666-7304-5.ch003
Kalelioglu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200-210. https://doi.org/10.1016/j.chb.2015.05.047
Kaufman, D. M., & Mann, K. V. (1996). Comparing students′ attitudes in problem-based and conventional curricula. Acad Med, 71(10), 1096-1099. https://doi.org/10.1097/00001888-199610000-00018
Kay, J., & Bull, S. (2015). New opportunities with open learner models and visual learning analytics. International Conference on Artificial Intelligence in Education, 666-669. https://doi.org/10.1007/978-3-319-19773-9_87
Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. Acm Computing Surveys, 37(2), 83-137. https://doi.org/10.1145/1089733.1089734
Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women′s university in South Korea. Computers & Education, 127, 233-251. https://doi.org/10.1016/j.compedu.2018.08.023
Kim, J., Jo, I. H., & Park, Y. (2016). Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17(1), 13-24. https://doi.org/10.1007/s12564-015-9403-8
Kizilcec, R. F., & Halawa, S. (2015). Attrition and achievement gaps in online learning. Proceedings of the second (2015) ACM conference on learning@ scale, 57-66. https://doi.org/10.1145/2724660.2724680
Kizilcec, R. F., Perez-Sanagustin, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18-33. https://doi.org/10.1016/j.compedu.2016.10.001
Korkmaz, Ö. (2012). A validity and reliability study of the Online Cooperative Learning Attitude Scale (OCLAS). Computers & Education, 59(4), 1162-1169. https://doi.org/10.1016/j.compedu.2012.05.021
Kostopoulos, G., Kotsiantis, S., Pierrakeas, C., Koutsonikos, G., & Gravvanis, G. A. (2018). Forecasting students′ success in an open university. International Journal of Learning Technology, 13(1), 26-43. https://doi.org/10.1504/ijlt.2018.091630
Kuo, H. C., Yang, Y. T. C., Chen, J. S., Hou, T. W., & Ho, M. T. (2022). The Impact of Design Thinking PBL Robot Course on College Students′ Learning Motivation and Creative Thinking. IEEE Transactions on Education, 65(2), 124-131. https://doi.org/10.1109/Te.2021.3098295
Lajis, A., Baharudin, S. A., Ab Kadir, D., Ralim, N. M., Nasir, H. M., & Aziz, N. A. (2018). A review of techniques in automatic programming assessment for practical skill test. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-5), 109-113. https://jtec.utem.edu.my/jtec/article/view/4394/3251
Liang, Y., Liu, Q., Xu, J., & Wang, D. (2009). The recent development of automated programming assessment. 2009 International Conference on Computational Intelligence and Software Engineering, 1-5. https://doi.org/10.1109/CISE.2009.5365307
Lin, J. W., & Lai, Y. C. (2013). Harnessing Collaborative Annotations on Online Formative Assessments. Educational Technology & Society, 16(1), 263-274. https://www.jstor.org/stable/pdf/jeductechsoci.16.1.263.pdf
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist, 57(10), 1439-1459. https://doi.org/10.1177/0002764213479367
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012
Lykke, M., Coto, M., Mora, S., Vandel, N., & Jantzen, C. (2014). Motivating programming students by problem based learning and LEGO robots. 2014 IEEE Global Engineering Education Conference (EDUCON), 544-555. https://doi.org/10.1109/EDUCON.2014.6826146
Manovich, L. (2013). Software takes command (Vol. 5). A&C Black.
McCrudden, M. T., & Schraw, G. (2007). Relevance and goal-focusing in text processing. Educational Psychology Review, 19(2), 113-139. https://doi.org/10.1007/s10648-006-9010-7
Mergendoller, J. R., Maxwell, N. L., & Bellisimo, Y. (2006). The Effectiveness of Problem-Based Instruction: A Comparative Study of Instructional Methods and Student Characteristics. Interdisciplinary Journal of Problem-Based Learning, 1(2), 5. https://doi.org/10.7771/1541-5015.1026
Michalsky, T., & Schechter, C. (2013). Preservice teachers′ capacity to teach self-regulated learning: Integrating learning from problems and learning from successes. Teaching and Teacher Education, 30, 60-73. https://doi.org/10.1016/j.tate.2012.10.009
Michel, C., Lavoué, E., & Piétrac, L. (2012). A dashboard to regulate project-based learning. European Conference on Technology Enhanced Learning, 250-263. https://doi.org/10.1007/978-3-642-33263-0_20
Mimis, M., El Hajji, M., Es-saady, Y., Oueld Guejdi, A., Douzi, H., & Mammass, D. (2019). A framework for smart academic guidance using educational data mining. Education and Information Technologies, 24(2), 1379-1393. https://doi.org/10.1007/s10639-018-9838-8
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: a model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199-218. https://doi.org/10.1080/03075070600572090
Norris, D., Baer, L., Leonard, J., Pugliese, L., & Lefrere, P. (2008). Action analytics: Measuring and improving performance that matters in higher education. EDUCAUSE review, 43(1), 42. https://www.academia.edu/download/46259642/Action_Analytics_Norris_Lefrere_ERM0813.pdf
Nuutila, E., Törmä, S., & Malmi, L. (2005). PBL and computer programming—the seven steps method with adaptations. Computer Science Education, 15(2), 123-142. https://doi.org/10.1080/08993400500150788
Oliver, R. (1993). Measuring Hierarchical Levels of Programming Knowledge. Journal of Educational Computing Research, 9(3), 299-312. https://doi.org/10.2190/0lgx-M45x-2wbk-B7a6
Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3-12. https://www.econstor.eu/bitstream/10419/193806/1/econ-review-v10-i1-p003-012.pdf
Panadero, E., & Jarvela, S. (2015). Socially Shared Regulation of Learning: A Review. European Psychologist, 20(3), 190-203. https://doi.org/10.1027/1016-9040/a000226
Pardo, A., Poquet, O., Martínez-Maldonado, R., & Dawson, S. (2017). Provision of data-driven student feedback in la & EDM. Handbook of learning analytics, 163-174. https://doi.org/10.18608/hla17.014
Park, Y., & Jo, I.-H. (2019). Factors that affect the success of learning analytics dashboards. Educational Technology Research and Development, 67(6), 1547-1571. https://doi.org/10.1007/s11423-019-09693-0
Peng, W. (2010). Practice and experience in the application of problem-based learning in computer programming course. 2010 International Conference on Educational and Information Technology, 1, V1-170-V171-172. https://doi.org/10.1109/ICEIT.2010.5607778
Podgorelec, V., & Kuhar, S. (2011). Taking Advantage of Education Data: Advanced Data Analysis and Reporting in Virtual Learning Environments. Elektronika Ir Elektrotechnika, 114(8), 111-116. https://doi.org/10.5755/j01.eee.114.8.708
Risemberg, R., & Zimmerman, B. J. (1992). Self‐regulated learning in gifted students. Roeper Review, 15(2), 98-101. https://doi.org/10.1080/02783199209553476
Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7-12. https://doi.org/10.18608/jla.2015.21.2
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, M., Laferriere, T., & Power, T. M. (2016). The Move is On! From the Passive Multimedia Learner to the Engaged Co-creator. eLearn, 2016(3). https://doi.org/10.1145/2904374.2893358
Romli, R., Sulaiman, S., & Zamli, K. Z. (2015). Improving Automated Programming Assessments: User Experience Evaluation Using FaSt-generator. Procedia Computer Science, 72, 186-193. https://doi.org/10.1016/j.procs.2015.12.120
Roy, S., & Garg, A. (2017). Predicting academic performance of student using classification techniques. 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), 568-572. https://doi.org/10.1109/UPCON.2017.8251112
Salomon, G., & Globerson, T. (1987). Skill may not be enough: The role of mindfulness in learning and transfer. International Journal of Educational Research, 11(6), 623-637. https://doi.org/10.1016/0883-0355(87)90006-1
Salovaara, H. (2005). An exploration of students′ strategy use in inquiry-based computer-supported collaborative learning. Journal of Computer Assisted Learning, 21(1), 39-52. https://doi.org/10.1111/j.1365-2729.2005.00112.x
Sampson, V., & Clark, D. (2009). The impact of collaboration on the outcomes of scientific argumentation. Science Education, 93(3), 448-484. https://doi.org/10.1002/sce.20306
Schmidt, H. G., Vermeulen, L., & van der Molen, H. T. (2006). Longterm effects of problem-based learning: a comparison of competencies acquired by graduates of a problem-based and a conventional medical school. Med Educ, 40(6), 562-567. https://doi.org/10.1111/j.1365-2929.2006.02483.x
Schmitz, B. (2001). Self-Monitoring zur Unterstützung des Transfers einer Schulung in Selbstregulation für Studierende. Zeitschrift für Pädagogische Psychologie, 15(3/4), 181-197. https://doi.org/10.1024//1010-0652.15.34.181
Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321-1331. https://doi.org/10.1016/j.chb.2012.02.016
Shaw, R. S. (2013). The relationships among group size, participation, and performance of programming language learning supported with online forums. Computers & Education, 62, 196-207. https://doi.org/10.1016/j.compedu.2012.11.001
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
Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. Proceedings of the 2nd international conference on learning analytics and knowledge, 4-8. https://doi.org/10.1145/2330601.2330605
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
Siemens, G., & Baker, R. S. d. (2012). Learning analytics and educational data mining: towards communication and collaboration. Proceedings of the 2nd international conference on learning analytics and knowledge, 252-254. https://doi.org/10.1145/2330601.2330661
Siemens, G., & Gasevic, D. (2012). Guest Editorial - Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1-2. https://www.researchgate.net/profile/Mohamed-Mourad-Lafifi/post/Could_anybody_point_out_good_references_book_or_other_manual_about_SIEMENS_FUM_Card_such_as_FUM_230_FUM_511_and_son_on/attachment/59d64c3f79197b80779a6180/AS%3A484047265767424%401492417269115/download/Journal+of+Educational+Technology+%26+Society.pdf#page=6
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. https://doi.org/10.1016/j.chb.2021.106746
Souza, S. M., & Bittencourt, R. A. (2019). Motivation and engagement with pbl in an introductory programming course. 2019 IEEE Frontiers in Education Conference (FIE), 1-9. https://doi.org/10.1109/FIE43999.2019.9028419
Souza, S. M., & Bittencourt, R. A. (2020). Report of a CS1 Course for Computer Engineering Majors Based on PBL. 2020 IEEE Global Engineering Education Conference (EDUCON), 837-846. https://doi.org/10.1109/EDUCON45650.2020.9125121
Spector, J. M. (2015). The SAGE encyclopedia of educational technology. Sage Publications. https://doi.org/10.4135/9781483346397.n112
Splichal, J. M., Oshima, J., & Oshima, R. (2018). Regulation of collaboration in project-based learning mediated by CSCL scripting reflection. Computers & Education, 125, 132-145. https://doi.org/10.1016/j.compedu.2018.06.003
Stevenson, M. P., Hartmeyer, R., & Bentsen, P. (2017). Systematically reviewing the potential of concept mapping technologies to promote self-regulated learning in primary and secondary science education. Educational Research Review, 21, 1-16. https://doi.org/10.1016/j.edurev.2017.02.002
Tambouris, E., Panopoulou, E., Tarabanis, K., Ryberg, T., Buus, L., Peristeras, V., Lee, D., & Porwol, L. (2012). Enabling Problem Based Learning through Web 2.0 Technologies: PBL 2.0. Educational Technology & Society, 15(4), 238-251. https://www.jstor.org/stable/pdf/jeductechsoci.15.4.238.pdf
Tan, P.-N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India. https://www-users.cse.umn.edu/~kumar001/dmbook/dmsol_11_07_2021.pdf
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
Ullah, Z., Lajis, A., Jamjoom, M., Altalhi, A., Al-Ghamdi, A., & Saleem, F. (2018). The effect of automatic assessment on novice programming: Strengths and limitations of existing systems. Computer Applications in Engineering Education, 26(6), 2328-2341. https://doi.org/10.1002/cae.21974
Unal, A., & Topu, F. B. (2021). Effects of teaching a computer programming language via hybrid interface on anxiety, cognitive load level and achievement of high school students. Education and Information Technologies, 26(5), 5291-5309. https://doi.org/10.1007/s10639-021-10536-w
Vauras, M., Iiskala, T., Kajamies, A., Kinnunen, R., & Lehtinen, E. (2003). Shared-regulation and motivation of collaborating peers: A case analysis. Psychologia, 46(1), 19-37. https://doi.org/10.2117/psysoc.2003.19
Veenman, M. V. J. (2013). Assessing Metacognitive Skills in Computerized Learning Environments. In International handbook of metacognition and learning technologies (pp. 157-168). Springer. https://doi.org/10.1007/978-1-4419-5546-3_11
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10), 1500-1509. https://doi.org/10.1177/0002764213479363
Volet, S., Vauras, M., & Salonen, P. (2009). Self- and Social Regulation in Learning Contexts: An Integrative Perspective. Educational Psychologist, 44(4), 215-226. https://doi.org/10.1080/00461520903213584
von Matt, u. (1994). Kassandra. ACM SIGCUE Outlook, 22(1), 26-40. https://doi.org/10.1145/182107.182101
Wang, Q. Y., & Woo, H. L. (2007). Comparing asynchronous online discussions and face-to-face discussions in a classroom setting. British Journal of Educational Technology, 38(2), 272-286. https://doi.org/10.1111/j.1467-8535.2006.00621.x
Wang, T. H. (2011). Developing Web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-Learning environment. Computers & Education, 57(2), 1801-1812. https://doi.org/10.1016/j.compedu.2011.01.003
Williams, L., Wiebe, E., Yang, K., Ferzli, M., & Miller, C. (2002). In Support of Pair Programming in the Introductory Computer Science Course. Computer Science Education, 12(3), 197-212. https://doi.org/10.1076/csed.12.3.197.8618
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
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
Winne, P. H. (2017). Learning analytics for self-regulated learning. Handbook of learning analytics, 241-249. https://doi.org/10.18608/hla17.021
Winne, P. H., & Hadwin, A. F. (2010). nStudy: Tracing and supporting self-regulated learning in the Internet. International handbook of metacognition and learning technologies, 293-308. https://doi.org/10.1007/978-1-4419-5546-3_20
Wood, D. F. (2008). Problem based learning. BMJ, 336(7651), 971. https://doi.org/10.1136/bmj.39546.716053.80
Yadav, A., Mayfield, C., Zhou, N. E., Hambrusch, S., & Korb, J. T. (2014). Computational Thinking in Elementary and Secondary Teacher Education. ACM Transactions on Computing Education, 14(1), 1-16. https://doi.org/10.1145/2576872
Yadav, A., Stephenson, C., & Hong, H. (2017). Computational Thinking for Teacher Education. Communications of the ACM, 60(4), 55-62. https://doi.org/10.1145/2994591
Yağcı, M. (2017). Web-Mediated Problem-Based Learning and Computer Programming: Effects of Study Approach on Academic Achievement and Attitude. Journal of Educational Computing Research, 56(2), 272-292. https://doi.org/10.1177/0735633117706908
Yi-Ran, H., Cheng, Z., Feng, Y., & Meng-Xiao, Y. (2010). Research on teaching operating systems course using problem-based learning. 2010 5th International Conference on Computer Science & Education, 691-694. https://doi.org/10.1109/ICCSE.2010.5593517
Yoo, Y., Lee, H., Jo, I.-H., & Park, Y. (2015). Educational dashboards for smart learning: Review of case studies. Emerging issues in smart learning, 145-155. https://doi.org/10.1007/978-3-662-44188-6_21
Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers′ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789-801. https://doi.org/10.1111/bjet.12453
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (2000). Attaining Self-Regulation. In Handbook of Self-Regulation (pp. 13-39). Elsevier. https://doi.org/10.1016/b978-012109890-2/50031-7
Zimmerman, B. J., & Risemberg, R. (1997). Becoming a self-regulated writer: A social cognitive perspective. Contemporary Educational Psychology, 22(1), 73-101. https://doi.org/10.1006/ceps.1997.0919
Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and an overview. Handbook of self-regulation of learning and performance, 15-26. https://doi.org/10.4324/9780203839010
Zimmerman, B. J., & Schunk, D. H. (2013). Self-Regulated Learning and Academic Achievement. Routledge. https://doi.org/10.4324/9781410601032
指導教授 洪暉鈞(Hui-Chun Hung) 審核日期 2022-7-27
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