| 研究生: |
王士誠 Wang, Shih-Cheng |
|---|---|
| 論文名稱: |
基於視覺化程式的學習追蹤系統於線上運算思維課程之分析與探討 Analyzing and Exploring of the Learning Tracking System Based on Visual Programming in Online Computational Thinking Courses |
| 指導教授: |
黃悅民
Huang, Yueh-Min |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 工程科學系 Department of Engineering Science |
| 論文出版年: | 2021 |
| 畢業學年度: | 109 |
| 語文別: | 中文 |
| 論文頁數: | 94 |
| 中文關鍵詞: | 運算思維 、視覺化程式設計 、學習追蹤系統 、學習歷程 |
| 外文關鍵詞: | Computational Thinking, Visual Programming, Learning Tracking System, Learning Process |
| 相關次數: | 點閱:173 下載:0 |
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運算思維被認為是當前最重要的核心能力之一,它不僅是電腦科學家才需擁有的能力,也是每個人都應該具備的能力,因此如何培養學生具備運算思維能力解決問題備受到學者們的重視。目前,基於程式設計課程培養運算思維是主要的學習方式之一,然而傳統以文字為基礎的程式設計語言,其中隱含的抽象概念對於初學者或是低年級學生是困難且難以入門的;而透過視覺化程式設計則可以有效的幫助學生,經由拖拉圖形元素的程式積木組合程式來學習程式設計,同時使用視覺化程式語言是有助於發展學生的運算思維能力。儘管如此,如若單純使用學生程式專案作為最終學習成果,是無法充分瞭解學生的學習過程,因此若能導入學習歷程與學習分析技術在課程活動中,便可以協助教師更加了解學生的學習狀況,並提供協助與調整課程內容,也能對學習運算思維有所幫助。
有鑑於此,本研究建置一運算思維學習平台整合學習追蹤系統,並支援線上教學形式以應對COVID-19所帶來的影響;為此,本研究以台南市國小五、六年級生為實驗對象,探討學生參與線上課程活動的學習成效、學習體驗、運算思維使用頻率以及程式設計自我效能,並透過學習追蹤系統記錄學生在課程中的學習歷程。實驗結果顯示,學生參與課程後運算思維能力有顯著提升;從學習行為編碼分析結果得到編碼與運算思維之間存在顯著關聯,此外還瞭解到程式初學者較易對程式任務進行嘗試錯誤,編碼次數的結果也顯著於高成就的學生。因此,基於本研究所提出之平台,可以有效地幫助教師即時掌握課程專案與學生學習進度,並透過學習分析系統給予協助與課程內容調整。
Computational thinking is one of the most important abilities; it is not only a capability that a computer scientist needs to possess, but also a capability that everyone should have. For the cultivation of computational thinking, the visual programming allows learners to assemble programs by dragging and dropping blocks, which can effectively learn programming and computational thinking ability. However, to understand the student's learning outcome, only student's final project is not enough. Tracking learners' learning process and programming process can help teachers to better understand the student's learning, which will provide assistance or adjust course content for students, and also support for computational thinking. Therefore, this study developed a learning tracking system based on visual programming to explore the learning effectiveness and learning experience when students participating in online course. This study takes fifth and sixth graders of elementary school in Tainan City as the experimental subjects. The experimental results show that the students' computational thinking has improved significantly and a correlation between learning behavior coding and computational thinking. Meanwhile, these findings show that beginners made trial and error for programming tasks. Overall, this study proposed the system can effectively support teachers to grasp the course project and students' learning progress in real time. According to the result of the learning analysis system can assist students or adjust the content of the course.
林育慈與吳正己(民105)。運算思維與中小學資訊科技課程。教育脈動,6,5-20。
教育部(民105)。教育部運算思維推動計畫。取自: http://compthinking.csie.ntnu.edu.tw
Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470-489.
Anderson, N. D. (2016). A call for computational thinking in undergraduate psychology. Psychology Learning & Teaching, 15(3), 226-234.
Balanskat, A., & Engelhardt, K. (2015). Computing our future. Computer programming and coding Priorities, school curricula and initiatives across Europe. European Schoolnet.
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? Acm Inroads, 2(1), 48-54.
Bau, D., Gray, J., Kelleher, C., Sheldon, J., & Turbak, F. (2017). Learnable programming: blocks and beyond. Communications of the ACM, 60(6), 72-80.
Benton, L., Hoyles, C., Kalas, I., & Noss, R. (2017). Bridging primary programming and mathematics: Some findings of design research in England. Digital Experiences in Mathematics Education, 3(2), 115-138.
Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1-2), 205-220.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at the Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada.
Buckley, P., & Doyle, E. (2016). Gamification and student motivation. Interactive Learning Environments, 24(6), 1162-1175.
Chalmers, C. (2018). Robotics and computational thinking in primary school. International Journal of Child-Computer Interaction, 17, 93-100.
Chen, C., Sonnert, G., Sadler, P. M., & Malan, D. J. (2020). Computational thinking and assignment resubmission predict persistence in a computer science MOOC. Journal of Computer Assisted Learning, 36(5), 581-594.
Cheng, G. (2019). Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools. Computers in Human Behavior, 92, 361-372.
Chien, Y.-C. (2018). Evaluating the learning experience and performance of computational thinking with visual and tangible programming tools for elementary school students. (PhD Thesis). National Cheng Kung University, Tainan, Taiwan.
Chiu, M. M., & Klassen, R. M. (2010). Relations of mathematics self-concept and its calibration with mathematics achievement: Cultural differences among fifteen-year-olds in 34 countries. Learning and Instruction, 20(1), 2-17.
Chou, P.-N. (2020). Using ScratchJr to Foster Young Children’s Computational Thinking Competence: A Case Study in a Third-Grade Computer Class. Journal of Educational Computing Research, 58(3), 570-595.
Council, N. R. (2011). Report of a workshop on the pedagogical aspects of computational thinking: National Academies Press.
Cutumisu, M., Adams, C., & Lu, C. (2019). A Scoping Review of Empirical Research on Recent Computational Thinking Assessments. Journal of Science Education and Technology, 28(6), 651-676.
Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277-296.
Fasihuddin, H., Skinner, G., & Athauda, R. (2017). Towards adaptive open learning environments: Evaluating the precision of identifying learning styles by tracking learners’ behaviours. Education and Information Technologies, 22(3), 807-825.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317.
Filvà, D. A., Forment, M. A., García-Peñalvo, F. J., Escudero, D. F., & Casañ, M. J. (2019). Clickstream for learning analytics to assess students’ behavior with Scratch. Future Generation Computer Systems, 93, 673-686.
Futschek, G., & Moschitz, J. (2011). Learning algorithmic thinking with tangible objects eases transition to computer programming. Paper presented at the International conference on informatics in schools: Situation, evolution, and perspectives.
González, M. R. (2015). Computational thinking test: Design guidelines and content validation. Paper presented at the Proceedings of EDULEARN15 conference.
Google. (2010). Exploring Computational Thinking. Retrieved from https://ai.googleblog.com/2010/10/exploring-computational-thinking.html
Google. (2012). Introduction to Blockly. Retrieved from https://developers.google.com/blockly/guides/overview.
Grover, S. (2017). Assessing algorithmic and computational thinking in K-12: Lessons from a middle school classroom. In Emerging research, practice, and policy on computational thinking (pp. 269-288): Springer.
Grover, S., Cooper, S., & Pea, R. (2014). Assessing computational learning in K-12. Paper presented at the Proceedings of the 2014 conference on Innovation & technology in computer science education.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43.
Grover, S., & Pea, R. (2018). Computational Thinking: A competency whose time has come. Computer science education: Perspectives on teaching and learning in school, 19.
Harvey, B., & Mönig, J. (2010). Bringing “no ceiling” to Scratch: Can one language serve kids and computer scientists. Proc. Constructionism, 1-10.
Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., & Lim, H. (2021). An adaptive educational computer game: Effects on students' knowledge and learning attitude in computational thinking. Computers in Human Behavior, 114, 106575.
Hou, H.-T. (2012). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233.
Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310.
Hu, C. (2011). Computational thinking: what it might mean and what we might do about it. Paper presented at the Proceedings of the 16th annual joint conference on Innovation and technology in computer science education.
Hu, M., Winikoff, M., & Cranefield, S. (2012). Teaching novice programming using goals and plans in a visual notation. Paper presented at the Proceedings of the Fourteenth Australasian Computing Education Conference-Volume 123.
Hung, H.-T., Yang, J. C., Hwang, G.-J., Chu, H.-C., & Wang, C.-C. (2018). A scoping review of research on digital game-based language learning. Computers & Education, 126, 89-104.
Hwang, G.-J., Hsu, T.-C., Lai, C.-L., & Hsueh, C.-J. (2017). Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns. Computers & Education, 106, 26-42.
Iskrenovic-Momcilovic, O. (2019). Pair programming with scratch. Education and Information Technologies, 24(5), 2943-2952.
Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2021). A Log-Based Analysis of the Associations Between Creativity and Computational Thinking. Journal of Educational Computing Research, 59(5), 926-959.
Jiang, B., Zhao, W., Zhang, N., & Qiu, F. (2019). Programming trajectories analytics in block-based programming language learning. Interactive Learning Environments, 1-14.
Juhaňák, L., Zounek, J., & Rohlíková, L. (2019). Using process mining to analyze students' quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496-506.
Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code. org. Computers in Human Behavior, 52, 200-210.
Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83-137.
Kesselbacher, M., & Bollin, A. (2019). Discriminating Programming Strategies in Scratch: Making the Difference between Novice and Experienced Programmers. Paper presented at the Proceedings of the 14th Workshop in Primary and Secondary Computing Education.
Kong, S.-C., Chiu, M. M., & Lai, M. (2018). A study of primary school students' interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178-189.
Korkmaz, Ö., Cakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558-569.
Kucuk, S., & Sisman, B. (2017). Behavioral patterns of elementary students and teachers in one-to-one robotics instruction. Computers & Education, 111, 31-43.
Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: A causal analysis. Computers in Human Behavior, 14(1), 125-146.
Lämsä, J., Hämäläinen, R., Koskinen, P., Viiri, J., & Mannonen, J. (2020). The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes. Computers & Education, 143, 103674.
Lu, O. H., Huang, J. C., Huang, A. Y., & Yang, S. J. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234.
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.
Matcha, W., Gasevic, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies.
Mladenović, M., Boljat, I., & Žanko, Ž. (2018). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 23(4), 1483-1500.
Moreno-León, J., Robles, G., & Román-González, M. (2015). Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. RED. Revista de Educación a Distancia(46), 1-23.
Nardelli, E. (2019). Do we really need computational thinking? Communications of the ACM, 62(2), 32-35.
Nation, S. (2017). About Smart Nation. Last updated November, 9.
Papadakis, S. (2020). Evaluating a game-development approach to teach introductory programming concepts in secondary education. International Journal of Technology Enhanced Learning, 12(2), 127-145.
Park, Y., & Jo, I.-H. (2019). Factors that affect the success of learning analytics dashboards. Educational Technology Research and Development, 67(6), 1547-1571.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Qu, H., & Chen, Q. (2015). Visual analytics for MOOC data. IEEE computer graphics and applications, 35(6), 69-75.
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., . . . Silverman, B. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.
Rich, P. J., & Hodges, C. B. (2017). Emerging research, practice, and policy on computational thinking: Springer.
Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678-691.
Sands, P., Yadav, A., & Good, J. (2018). Computational thinking in K-12: In-service teacher perceptions of computational thinking. In Computational thinking in the STEM disciplines (pp. 151-164): Springer.
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2018). Linking learning behavior analytics and learning science concepts: designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior.
Seehorn, D., Carey, S., Fuschetto, B., Lee, I., Moix, D., O'Grady-Cunniff, D., . . . Verno, A. (2011). CSTA K--12 Computer Science Standards: Revised 2011.
Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition.
Shih, B., Koedinger, K. R., & Scheines, R. (2011). A response time model for bottom-out hints as worked examples. Handbook of educational data mining, 201-212.
Shimada, A., Konomi, S. i., & Ogata, H. (2018). Real-time learning analytics system for improvement of on-site lectures. Interactive Technology and Smart Education.
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
Slisko, J. (2017). Self-Regulated Learning in A General University Course: Design of Learning Tasks, Their Implementation and Measured Cognitive Effects. Journal of European Education, 7(2), 12-24.
SMITH, M. (2016). Computer Science For All. Retrieved from https://obamawhitehouse.archives.gov/blog/2016/01/30/computer-science-all
Sáez-López, J.-M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129-141.
Snow, E. L., Allen, L. K., Jacovina, M. E., & McNamara, D. S. (2015). Does agency matter?: Exploring the impact of controlled behaviors within a game-based environment. Computers & Education, 82, 378-392.
Soller, A., Martínez, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15(4), 261-290.
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.
Sung, W., Ahn, J., & Black, J. B. (2017). Introducing computational thinking to young learners: Practicing computational perspectives through embodiment in mathematics education. Technology, Knowledge and Learning, 22(3), 443-463.
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798.
Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review.
Tsai, C.-Y. (2019). Improving students' understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior, 95, 224-232.
Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2020). The Computational Thinking Scale for Computer Literacy Education. Journal of Educational Computing Research, 0735633120972356.
Vahdat, M., Oneto, L., Anguita, D., Funk, M., & Rauterberg, M. (2015). A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In Design for teaching and learning in a networked world (pp. 352-366): Springer.
Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2015). Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics. Computers & Education, 90, 80-94.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110.
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715-728.
Webb, M. E., Prasse, D., Phillips, M., Kadijevich, D. M., Angeli, C., Strijker, A., . . . Laugesen, H. (2018). Challenges for IT-enabled formative assessment of complex 21st century skills. Technology, Knowledge and Learning, 23(3), 441-456.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
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.
Wu, S.-Y., & Hou, H.-T. (2015). How cognitive styles affect the learning behaviors of online problem-solving based discussion activity: A lag sequential analysis. Journal of Educational Computing Research, 52(2), 277-298.
Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37, 46-57.
Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. Paper presented at the Proceedings of the 42nd ACM technical symposium on Computer science education.
Yang, T.-Y., Brinton, C. G., Joe-Wong, C., & Chiang, M. (2017). Behavior-based grade prediction for MOOCs via time series neural networks. IEEE Journal of Selected Topics in Signal Processing, 11(5), 716-728.
Yau, J. Y.-K., & Ifenthaler, D. (2020). Reflections on different learning analytics indicators for supporting study success. International Journal of Learning Analytics and Artificial Intelligence for Education: iJAI, 2(2), 4-23.
Yin, Y., Hadad, R., Tang, X., & Lin, Q. (2019). Improving and Assessing Computational Thinking in Maker Activities: the Integration with Physics and Engineering Learning. Journal of Science Education and Technology, 1-26.
Zhang, J.-H., Zhang, Y.-X., Zou, Q., & Huang, S. (2018). What learning analytics tells us: Group behavior analysis and individual learning diagnosis based on long-term and large-scale data. Journal of Educational Technology & Society, 21(2), 245-258.
Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607.
Zhang, W., Huang, X., Wang, S., Shu, J., Liu, H., & Chen, H. (2017). Student performance prediction via online learning behavior analytics. Paper presented at the 2017 International Symposium on Educational Technology (ISET).
Zhao, W., & Shute, V. J. (2019). Can playing a video game foster computational thinking skills? Computers & Education, 141, 103633.
Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562-590.
校內:2026-09-15公開