近年來，電腦科學家和教育學者們提倡將程式設計列為國民必修的通識教育之一。然而，儘管用於教學和學習入門程式設計的方法和工具多元且進步，然而還是有許多人在學習程式設計入門課程時仍遇到挑戰和困難。過去研究顯示，透過探究學生對特定知識領域的學習觀點，可以理解學生對該學科的學習信念。同時，先前研究也指出，學習者的學習觀點與他們的學習策略，以及自我效能和學習成效具有高度相關。因此，本研究旨在分析大學生的程式設計學習觀點，學習策略和程式設計自我效能。研究分為兩個階段進行。第一階段是了解台灣學生的程式設計學習觀點與學習策略。在這一階段採用的研究方法是文獻回顧與現象圖學法，透過先前相關研究的學習觀點框架，以及本次研究的質性訪談、轉錄與分析，對參與者的回饋進行紮根理論式的分類，從而發展出程式設計學習觀點與學習策略兩份問卷。第二階段是驗證此兩份問卷，並確定此次研究學習者的程式設計學習觀點、學習策略，與程式設計自我效能之間的關聯性。此階段採用的研究方法包括探索性因素分析、驗證性因素分析、Pearson相關分析，和結構方程模式。研究進行期間為2019年7月至2020年7月。第一階段和第二階段的參與者分別為31名和307名大學生。研究結果發現，台灣大學生的程式設計學習觀點可以分為以下六類：1學習程式設計是一種記憶程式設計概念和語法的方法（記憶）；2學習程式設計是一種不斷地練習程式語法撰寫的技能（練習）；3學習程式設計是紓解壓力的一種手段（紓壓）；4學習程式設計是能夠將技巧運用於不同的情境，並提升知識（應用與理解問題和提升知識）；5. 學習程式設計是透過學習程式設計來提昇知能，並培養從不同角度看待事物的方法（知能提昇和以嶄新的觀點看待事物）；以及6學習程式設計是參與社群，並與程式設計同儕和程式使用者互動，解決他人問題，從而建構面對問題有不同觀點的一種歷程（透過同儕互動習得以嶄新的觀點看待事物）。前兩類為較低階的程式設計學習觀點，後四類為較高階的程式設計學習觀點。另外，驗證性因素分析顯示這6類的內部一致性分別為：0.87、0.77、0.81、0.85、0.86和0.85，總體一致性為0.89。同時，相關性分析與結構方程模式顯示，持有低階程式設計學習觀點的學生傾向於採用淺層的學習策略來學習程式設計，例如複製他人，教科書或教師的程式碼，而這些學生的程式設計自我效能也顯得較低。相反地，持有較高階程式設計學習觀點的學生傾向於採用更深層次的學習策略來學習程式設計，例如在課堂上專心學習、期待上課，以及在學習程式設計時會整合內容等，他們程式設計自我效能也較高。最後，研究發現第三類（紓壓）以及第六類（透過同儕互動習得以嶄新的觀點看待事物）與深層的學習策略以及自我效能各個構面皆呈顯著正相關。因此，建議程式設計老師可以建構同儕互動的程式設計環境，使學習者能夠逐步解決問題以獲致成就感。一旦個別學習者獲得成就感或感到紓壓及療癒，以及得到同儕鼓勵與刺激時，他們很可能會主動尋找更多程式設計的挑戰，從而提高程式設計自我效能。

In recent years, computer scientists and educators have advocated programming learning as one of the required areas of general education. However, despite the advances in methods and tools for teaching and learning introductory programming, individuals still encounter challenges and difficulties when learning it. Studies have suggested that to better understand individuals’ beliefs about or their interpretations of learning in a specific knowledge domain, it is necessary to investigate their conceptions of learning for that subject. In addition, studies have also found that leaners’ conceptions of learning are highly correlated with their approaches to learning, their learning self-efficacy, and learning outcomes. Therefore, this study aims to reveal college students’ conceptions of programming learning (CoPL), their approaches to programming learning (APL), and their computer programming self-efficacy (CPSE). Two phases of studies were carried out. The first phase was to uncover Taiwanese students’ CoPL and APL in a hierarchical order. The research method adapted in this phase was the phenomenographic analysis approach via qualitative interviews, discourse transcription, and data analysis. The CoPL and APL questionnaires were then developed. The second phase was to verify the CoPL and APL questionnaire items and to identify the relationship among learners’ CoPL, APL and CPSE. Research methods adapted in this phase included exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Pearson correlation analysis, and structure equation modeling (SEM). The study was conducted from July of 2019 to July of 2020. Participants in phases I and II were 31 and 307 college students, respectively. Results showed that, first of all, Taiwanese college students’ conceptions of programming learning can be classified into the following six categories: 1. Programming learning as a means of memorizing programming concepts and syntax (Memorizing), 2. Programming learning as a means to constantly practice the skills in program writing (Computing and Practicing), 3. Programming learning as a means of relieving pressure (Relieving), 4. Programming learning as a means of solving problems in the form of programs, applying the programming skill in different contexts, and increasing ones’ knowledge (Applying, understanding, and increasing), 5. Programming learning as a means to improve one’s competences as well as cultivating one’s way of seeing the world from different perspectives (Improvement and Seeing in a new way), and 6. Programming learning as a means of engaging in the programming community and interacting with both programmers and users to solve others’ problems (Seeing in a new way via collaboration). Of these, the former two were categorized as lower-level (fragmented) CoPL, and the latter four were categorized as higher-level (cohesive) CoPL. Next, the CFA shows that the Cronbach’s alpha of these six categories are: 0.87, 0.77, 0.81, 0.85, 0.86, and 0.85, and the overall alpha is 0.89. Furthermore, the correlation and SEM showed that those who hold lower-level CoPL tended to adopt surface approaches to programming learning, such as copying from others, from textbooks, or from teachers, and these students have lower self-efficacy in performing programming activities. On the contrary, students who hold higher-level CoPL tended to adopt deep approaches to programming learning, such as paying attention in class, and combining separate contents when learning programming, and they have high self-efficacy in performing programming activities. Last but not least, the category “Relieving” and “Seeing in a new way via collaboration” show significant positive correlations with deep approaches of programming learning, and all categories of computer programming self-efficacy. It is suggested that programming teachers can create a peer-interactive programming learning environment that allows learners to gain achievement by solving problems step by step and to get inspired by their peers. Once learners gain the sense of stress release or the sense of achievement, they could possibly look for more programming challenges and thereby build up their programming self-efficacies.

Aler, R., Borrajo, D., & Isasi, P. (2001). Learning to solve planning problems efficiently by means of genetic programming. Evolutionary computation, 9(4), 387-420.
Altun, A. & Mazman, S. G. (2012). Developing computer programming self-efficacy scale. Journal of Measurement and Evaluation in Education and Psychology, 3(2), 297–308.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986, 23-28.
Bandura, A. (1997). The nature and structure of self-efficacy. Self-efficacy: the exercise of control. New York, NY: WH Freeman and Company, 37-78.
Bandura, A. (2010). Self‐efficacy. The Corsini encyclopedia of psychology, 1-3.
Bandura, A., & Walters, R. H. (1977). Social learning theory (Vol. 1). Englewood Cliffs, NJ: Prentice-hall.
Barr, D., Conery, L., & Harrison, J. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20–23.
Bau, D., Gray, J., Kelleher, C., Sheldon, J., & Turbak, F. (2017). Learnable programming: blocks and beyond. Communications of the ACM, 60(6), 72-80.
Ben-Ari, M. (2004). Situated learning in computer science education. Computer Science Education, 14(2), 85-100.
Berland, M., Lee, V. R. (2011) Collaborative strategic board games as a site for distributed computational thinking. International Journal of Game-Based Learning 1(2), 65–81.
Berry, M. (2013). Computing in the national curriculum. A guide for primary teachers. Computing at school, Bedford.
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.
Bieliková, M., & Návrat, P. (1998). Use of program schemata in Lisp programming: an evaluation of its impact on learning. Informatica, 9(1), 5-20.
Biggs, J. (1994). Approaches to learning: Nature and measurement of. The international encyclopedia of education, 1, 319-322.
Boldbaatar, N., & Şendurur, E. (2019). Developing Educational 3D Games With StarLogo: The Role of Backwards Fading in the Transfer of Programming Experience?. Journal of Educational Computing Research, 57(6), 1468-1494.
Booth, S. (1992) Learning to Program: A Phenomenographic Perspective. Goteborg Studies in Educational Sciences 89. Acta Univesitatis Gothoburgenis, Gothenburg.
Booth, S. (1993). A study of learning to program from an experiential perspective. Computers in human behavior, 9(2-3), 185-202.
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological methods & research, 21(2), 230-258.
Bruce, C., Buckingham, L., Hynd, J., McMahon, C., Roggenkamp, M., & Stoodley, I. (2004). Ways of experiencing the act of learning to program: A phenomenographic study of introductory programming students at university. Journal of Information Technology Education: Research, 3(1), 145-160.
Buck, D., & Stucki, D. J. (2001, February). JKarelRobot: a case study in supporting levels of cognitive development in the computer science curriculum. In Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education (pp. 16-20).
Byrne, B. M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International journal of testing, 1(1), 55-86.
Chao, P. Y. (2016). Exploring students' computational practice, design and performance of problem-solving through a visual programming environment. Computers & Education, 95, 202–215.
Chen, C., Haduong, P., Brennan, K., Sonnert, G., & Sadler, P. (2019). The effects of first programming language on college students’ computing attitude and achievement: a comparison of graphical and textual languages. Computer Science Education, 29(1), 23-48.
Chen, W. K., & Cheng, Y. C. (2007). Teaching object-oriented programming laboratory with computer game programming. IEEE Transactions on Education, 50(3), 197-203.
Chiou, G. L., & Liang, J. C. (2012). Exploring the structure of science self-efficacy: A model built on high school students’ conceptions of learning and approaches to learning in science. Asia-Pacific Education Researcher, 21, 83–91.
Chiou, G. L., Lee, M. H., & Tsai, C. C. (2013). High school students’ approaches to learning physics with relationship to epistemic views on physics and conceptions of learning physics. Research in Science & Technological Education, 31(1), 1-15.
Chiou, G. L., Liang, J. C. & Tsai. C. C. (2012). Undergraduate Students’ Conceptions of and Approaches to Learning in Biology: A Study of their Structural Models and Gender Differences. International Journal of Science Education, 34 (2), 167-95.
Chorfi, A., Hedjazi, D., Aouag, S., & Boubiche, D. (2020). Problem-based collaborative learning groupware to improve computer programming skills. Behaviour & Information Technology, 1-20.
Çoban, E., Korkmaz, Ö., Çakır, R., & Erdoğmuş, F. U. (2020). Attitudes of IT teacher candidates towards computer programming and their self-efficacy and opinions regarding to block-based programming. Education and Information Technologies, 1-18.
Corradini, I., Lodi, M., & Nardelli, E. (2017, June). Computational Thinking in Italian Schools: Quantitative Data and Teachers' Sentiment Analysis after Two Years of" Programma il Futuro". In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education (pp. 224-229).
Deng, W., Pi, Z., Lei, W., Zhou, Q., & Zhang, W. (2020). Pencil Code improves learners' computational thinking and computer learning attitude. Computer Applications in Engineering Education, 28(1), 90-104.
diSessa, A. A. (2001). Changing minds: Computers, learning, and literacy. Mit Press.
Dwyer, H., Hill, C., Hansen, A., Iveland, A., Franklin, D., & Harlow, D. (2015, August). Fourth grade students reading block-based programs: predictions, visual cues, and affordances. In Proceedings of the eleventh annual International Conference on International Computing Education Research (pp. 111-119).
Echeverría, L., Cobos, R., Machuca, L., & Claros, I. (2017). Using collaborative learning scenarios to teach programming to non‐CS majors. Computer applications in engineering education, 25(5), 719-731.
Edwards, S. H. (2004, March). Using software testing to move students from trial-and-error to reflection-in-action. In Proceedings of the 35th SIGCSE technical symposium on Computer science education (pp. 26-30).
Ellis, R. A., Goodyear, P., Prosser, M., & O’Hara, A. (2006). How and what university students learn through online and faceto-face discussion: Conceptions, intentions and approaches. Journal of Computer Assisted Learning, 22, 244–256.
Falkner, K., Vivian, R., & Falkner, N. (2014, January). The Australian digital technologies curriculum: challenge and opportunity. In Proceedings of the Sixteenth Australasian Computing Education Conference-Volume 148 (pp. 3-12).
Gov.UK. (2013). Statutory Guidance-National Curriculum in England: Computing Programmes of Study. Retrieved from https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study/national-curriculum-in-england-computing-programmes-of-study
Granberg, C. (2016). Discovering and addressing errors during mathematics problem-solving—A productive struggle?. The Journal of Mathematical Behavior, 42, 33-48.
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., Basu, S., Bienkowski, M., Eagle, M., Diana, N., & Stamper, J. (2017). A framework for using hypothesis-driven approaches to support data-driven learning analytics in measuring computational thinking in block-based programming environments. ACM Transactions on Computing Education (TOCE), 17(3), 14.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.
Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive learning environments, 1(1), 1-32.
Hsin, C. T., Liang, J. C., Hsu, C. Y., Shih, M., Sheu, F. R., & Tsai, C. C. (2019). Young Children’s Conceptions of Learning: A Cross-Sectional Study of the Early Years of Schooling. The Asia-Pacific Education Researcher, 28(2), 127-137.
Ingerman, Å., & Booth, S. (2003). Expounding on physics: A phenomenographic study of physicists talking of their physics. Int. J. Sci. Educ., 25(12), 1489-1508.
Jeong, Y. (2016). Needs Analysis of Software Education Curriculum at National Universities of Education for the 2015 Revised National Curriculum. Journal of The Korean Association of Information Education, 20(1), 83-92.
Jurado, F., Redondo, M. A., & Ortega, M. (2007). An architecture to support programming algorithm learning by problem solving. In Innovations in Hybrid Intelligent Systems (pp. 470-477). Springer, Berlin, Heidelberg.
Kaasbøll, J. J. (1998). Exploring didactic models for programming. In NIK 98–Norwegian Computer Science Conference (pp. 195-203).
Kafai, Y. B., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61-65.
Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code. org. Computers in Human Behavior, 52, 200-210.
Kalelioglu, F., & Gülbahar, Y. (2014). The Effects of Teaching Programming via Scratch on Problem Solving Skills: A Discussion from Learners' Perspective. Informatics in Education, 13(1), 33-50.
Kember, D., Biggs, J., & Leung, D. Y. (2004). Examining the multidimensionality of approaches to learning through the development of a revised version of the Learning Process Questionnaire. British Journal of Educational Psychology, 74(2), 261-279.
Kenworthy, L., & Kielstra, P. (2015). Driving the skills agenda: Preparing students for the future. The Economist Intelligence Unit Limited.
Kong, S. C., Li, R. K. Y., & Kwok, R. C. W. (2019). Measuring Parents’ Perceptions of Programming Education in P-12 Schools: Scale Development and Validation. Journal of Educational Computing Research, 57(5), 1260-1280.
Kong, S. C., & Wang, Y. Q. (2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior, 106, 106230.
Kumar, A. N. (2003). Learning programming by solving problems. In Informatics curricula and teaching methods (pp. 29-39). Springer, Boston, MA.
Lahtinen, S. P., Sutinen, E., & Tarhio, J. (1998). Automated animation of algorithms with Eliot. Journal of Visual Languages & Computing, 9(3), 337-349.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge university press.
Lazar, T., Sadikov, A., & Bratko, I. (2017). Rewrite Rules for Debugging Student Programs in Programming Tutors. IEEE Transactions on Learning Technologies, 11(4), 429-440.
Lee, M. H., Johanson, R. E., & Tsai, C. C. (2008). Exploring Taiwanese high school students' conceptions of and approaches to learning science through a structural equation modeling analysis. Science Education, 92(2), 191-220.
Lee, M. H., Lin, T. J., & Tsai, C. C. (2013). Proving or improving science learning? Understanding high school students’ conceptions of science assessment in Taiwan. Science Education, 97(2), 244-270.
Lee, Y., & Cho, J. (2020). Knowledge representation for computational thinking using knowledge discovery computing. Information Technology and Management, 21(1), 15-28.
Liang, J. C., & Tsai, C. C. (2010). Relational analysis of college science‐major students’ epistemological beliefs toward science and conceptions of learning science. International Journal of Science Education, 32(17), 2273-2289.
Liang, J. C., Su, Y. C., & Tsai, C. C. (2015). The assessment of Taiwanese college students’ conceptions of and approaches to learning computer science and their relationships. The Asia-Pacific Education Researcher, 24(4), 557-567.
Lin, T. C., Liang, J. C., & Tsai, C. C. (2015). Conceptions of memorizing and understanding in learning, and self-efficacy held by university biology majors. International Journal of Science Education, 37(3), 446-468.
Lin, T. J., & Tsai, C. C. (2013). An investigation of Taiwanese high school students’ science learning self-efficacy in relation to their conceptions of learning science. Research in Science & Technological Education, 31(3), 308-323.
Liu, J., Feenstra, W., Otero, A. S., & Magrane, K. (2014, April). STEM Education. In Proceedings of the 6th International Conference on Computer Supported Education-Volume 2 (pp. 270-274). SCITEPRESS-Science and Technology Publications, Lda.
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.
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 1-15.
Margulieux, L. E., Morrison, B. B., & Decker, A. (2020). Reducing withdrawal and failure rates in introductory programming with subgoal labeled worked examples. International Journal of STEM Education, 7, 1-16.
Marton, F. (1981). Phenomenography—describing conceptions of the world around us. Instructional science, 10(2), 177-200.
Marton, F., Dall'Alba, G., & Beaty, E. (1993). Conceptions of learning. International journal of educational research, 19(3), 277-300.
Marton, F., & Säljö, R. (1976). On qualitative differences in learning: I—Outcome and process. British journal of educational psychology, 46(1), 4-11.
Mathews, D. K. (2017). Predictors of Success in Learning Computer Programming. Doctoral dissertation, University of Rhode Island.
McMahon, G. (2009). Critical thinking and ICT integration in a Western Australian secondary school. Journal of Educational Technology & Society, 12(4), 269-281.
Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2018). A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education, 62(2), 77-90.
Mehmood, E., Abid, A., Farooq, M. S., & Nawaz, N. A. (2020). Curriculum, Teaching and Learning, and Assessments for Introductory Programming Course. IEEE Access, 8, 125961-125981.
Mselle, L. J., & Twaakyondo, H. (2012). The impact of Memory Transfer Language (MTL) on reducing misconceptions in teaching programming to novices. International Journal of Machine Learning and Applications, 1(1), 6.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory 3rd edn. New York: McGraw-Hill.
Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68(1), 463-484.
Özyurt, H., & Özyurt, Ö. (2018). Analyzing the effects of adapted flipped classroom approach on computer programming success, attitude toward programming, and programming self‐efficacy. Computer Applications in Engineering Education, 26(6), 2036-2046.
Papert, S. (1972). Teaching children thinking. Programmed Learning and Educational Technology, 9(5), 245-255.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
Papert, S. (1993). The children's machine. Technology Review-Manchester NH-, 96, 28-28.
Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36(2), 1-11.
Partanen, T., Mannila, L., & Poranen, T. (2016, November). Learning programming online: A Racket-course for elementary school teachers in Finland. In Proceedings of the 16th Koli Calling International Conference on Computing Education Research (pp. 178-179).
Partovi, H., & Sahami, M. (2013). The hour of code is coming!. ACM SIGCSE Bulletin, 45(4), 5-5.
Peng, J., Wang, M., & Sampson, D. (2017). Visualizing the complex process for deep learning with an authentic programming project. Journal of Educational Technology & Society, 20(4), 275-287.
Perlis, A. (1962). The computer in the university. Computers and the World of the Future, 180- 219.
Piech, C., Sahami, M., Koller, D., Cooper, S., & Blikstein, P. (2012, February). Modeling how students learn to program. In Proceedings of the 43rd ACM technical symposium on Computer Science Education (pp. 153-160).
Qureshi, A. H., Nakamura, Y., Yoshikawa, Y., & Ishiguro, H. (2016, November). Robot gains social intelligence through multimodal deep reinforcement learning. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) (pp. 745-751). IEEE.
Rahman, M. T., Rahman, A. A., Chowdhury, J., & Zaman, A. G. M. (2020, January). Augmented Primitive Data Types of Programming Language. In Proceedings of the International Conference on Computing Advancements (pp. 1-2).
Ramalingam, V., & Wiedenbeck, S. (1998). Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4), 367-381.
Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004, June). Self-efficacy and mental models in learning to program. In Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education (pp. 171-175).
Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173-184.
Resnick, M., & Siegel, D. (2015). A different approach to coding. Bright/Medium.
Resnick, M., Maloney, J., Monroy-Herna´ndez, A., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67.
Richardson, J. T. (1999). The concepts and methods of phenomenographic research. Review of educational research, 69(1), 53-82.
Sakamoto, R., & Ohshima, T. (2019). Code Weaver: A Tangible Programming Learning Tool with Mixed Reality Interface. In SIGGRAPH Asia 2019 Posters (pp. 1-2).
Säljö, R. (1979). Learning in the learner‘s perspective. I. some commonsense conceptions (Report 76). University of Gothenburg, Institute of Education.
Sammet, J. E. (1972). Programming languages: history and future. Communications of the ACM, 15(7), 601-610.
Sapounidis, T., & Demetriadis, S. (2016, November). Educational robots driven by tangible programming languages: A review on the field. In International Conference EduRobotics 2016 (pp. 205-214). Springer, Cham.
Seow, P., Looi, C. K., How, M. L., Wadhwa, B., & Wu, L. K. (2019). Educational Policy and Implementation of Computational Thinking and Programming: Case Study of Singapore. In Computational Thinking Education (pp. 345-361). Springer, Singapore.
Shen, K. M., Lee, M. H., Tsai, C. C., & Chang, C. Y. (2016). Undergraduate students’ earth science learning: relationships among conceptions, approaches, and learning self-efficacy in Taiwan. International Journal of Science Education, 38(9), 1527-1547.
Slingerland, E., & Collard, M. (Eds.). (2011). Creating consilience: Integrating the sciences and the humanities. Oxford University Press.
Smith, V. A., & Duncan, I. (2011). Biology students building computer simulations using StarLogo TNG. Bioscience Education, 18(1), 1-9.
Tang, K. Y., Chou, T. L., & Tsai, C. C. (2020). A content analysis of computational thinking research: An international publication trends and research typology. The Asia-Pacific Education Researcher, 29(1), 9-19.
Trower, J., & Gray, J. (2015, February). Blockly language creation and applications: Visual programming for media computation and bluetooth robotics control. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 5-5).
Tsai, C. C. (2004). Conceptions of learning science among high school students in Taiwan: A phenomenographic analysis. International Journal of Science Education, 26(14), 1733-1750.
Tsai, C. C. (2017). Conceptions of learning in technology-enhanced learning environments: A review of case studies in Taiwan. Asian Association of Open Universities Journal, 12, 184-205.
Tsai, C. C., & Kuo, P. C. (2008). Cram school students’ conceptions of learning and learning science in Taiwan. International Journal of Science Education, 30(3), 353-375.
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., Wang, C. Y., & Hsu, P. F. (2019). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(8), 1345-1360.
Tsai, P. S., Chai, C. S., Hong, H. Y., & Koh, J. H. L. (2017). Students’ conceptions of and approaches to knowledge building and its relationship to learning outcomes. Interactive Learning Environments, 25(6), 749-761.
Turkle, S., & Papert, S. (1990). Epistemological pluralism: Styles and voices within the computer culture. Signs: Journal of women in culture and society, 16(1), 128-157.
Vee, A. (2017). Coding for Everyone and the Legacy of Mass Literacy.
Vettori, G., Vezzani, C., Bigozzi, L., & Pinto, G. (2020). Cluster profiles of university students’ conceptions of learning according to gender, educational level, and academic disciplines. Learning and Motivation, 70, 101628.
Vieira, C., Magana, A. J., Falk, M. L., & Garcia, R. E. (2017). Writing in-code comments to self-explain in computational science and engineering education. ACM Transactions on Computing Education, 17(4), 17.
Vlatko, N. (2015). The countries introducing coding into the curriculum. Retrieved from https://jaxenter.com/the-countries-introducing-coding-into-the-curriculum-120815.html
Wang, C. Y., & Tsai, C.C. (2018). Understanding students’ conceptions of and approaches to learning design. The International Journal of Design Education, 12, 31-45.
Wang, Y., Li, H., Feng, Y., Jiang, Y., & Liu, Y. (2012). Assessment of programming language learning based on peer code review model: Implementation and experience report. Computers & Education, 59(2), 412-422.
Warshauer, H. K. (2015). Productive struggle in middle school mathematics classrooms. Journal of Mathematics Teacher Education, 18(4), 375-400.
Watson, C., & Li, F. W. (2014, June). Failure rates in introductory programming revisited. In Proceedings of the 2014 conference on Innovation & technology in computer science education (pp. 39-44).
Whitehead, S. D., & Ballard, D. H. (1991). Learning to perceive and act by trial and error. Machine Learning, 7(1), 45-83.
Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course: a study of twelve factors. ACM SIGCSE Bulletin, 33(1), 184-188.
Wilson, C. (2015). Hour of code---a record year for computer science. ACM Inroads, 6(1), 22-22.
Wilson, R. (2014) “Computer programming will soon reach all Estonian schoolchildren”, Retrieved: http://www.ubuntulife.net/computer-programming-for-all-estonian-schoolchildren/
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 of London A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.
Yadav, A., Gretter, S., Good, J., & McLean, T. (2017). Computational thinking in teacher education. In Emerging research, practice, and policy on computational thinking (pp. 205-220). Springer, Cham.
Yang, H. L., & Cheng, H. H. (2009). Creative self-efficacy and its factors: An empirical study of information system analysts and programmers. Computers in Human Behavior, 25(2), 429-438.
Yang, Y. F., & Tsai, C. C. (2017). Exploring in-service preschool teachers’ conceptions of and approaches to online education. Australasian Journal of Educational Technology, 33(1).
Yildiz Durak, H. (2018). Digital story design activities used for teaching programming effect on learning of programming concepts, programming self‐efficacy, and participation and analysis of student experiences. Journal of Computer Assisted Learning, 34(6), 740-752.
Yost, D. S., Sentner, S. M., & Forlenza-Bailey, A. (2000). An examination of the construct of critical reflection: Implications for teacher education programming in the 21st century. Journal of teacher education, 51(1), 39-49.
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.