Abstract
Computational thinking (CT), as one of the key skills in the twenty-first century, has been integrated into educational programming as an important learning goal. This study aims to explore CT processes involved in pair programming with the support of visual flow design. Thirty freshmen participated, working in pairs to solve two programming problems. Their discourses were recorded, transcribed, and coded based on a CT framework encompassing cognitive, practical, and social perspectives. Both quantitative and qualitative methods were applied to analyze the data. In particular, Epistemic Network Analysis (ENA) was applied to explore the patterns of their CT processes. The findings revealed that social perspectives emerged the most frequently in all pairs’ discourses. The high-level groups (HLGs) focused more on practical and social perspectives whereas the low-level groups (LLGs) emphasized more on cognitive perspectives. The ENA networks revealed that social perspectives mostly centered around cognitive perspectives for all pairs with CT process patterns in HLGs crossing the three perspectives more frequently. In addition, HLGs exhibited a more complicated and developmental trend in solving the two problems, while LLGs displayed a relatively similar CT pattern. The current study provides insights into the design and implementation of collaborative learning activities in educational programming.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Asunda, P. A. (2018). Infusing computer science in engineering and technology education: An integrated STEM perspective. The Journal of Technology Studies,44(1), 2–13. Retrieved March 9, 2024, from https://www.jstor.org/stable/26730725
Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems,75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008
Bai, H., Wang, X., & Zhao, L. (2021). Effects of the problem-oriented learning model on middle school students’ computational thinking skills in a python course. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.771221
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
Borreguero Zuloaga, M., & De Marco, A. (2021). The role of immersion and non-immersion contexts in L2 acquisition: A study based on the analysis of interactional discourse markers. Corpus Pragmatics,5(1), 121–151. https://doi.org/10.1007/s41701-020-00093-x
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association, 1, 25.
Budny, D., Lund, L., Vipperman, J., & Patzer, J. L. I. I. I. (2002). Four steps to teaching C programming. 32nd Annual Frontiers in Education, 2, F1G-18-F1G-22. https://doi.org/10.1109/FIE.2002.1158140
Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research,87(4), 834–860. https://doi.org/10.3102/0034654317710096
Chao, P.-Y. (2016). Exploring students’ computational practice, design and performance of problem-solving through a visual programming environment. Computers & Education,95, 202–215. https://doi.org/10.1016/j.compedu.2016.01.010
Charntaweekhun, K., &Wangsiripitak, S. (2006). Visual programming using flowchart. 2006International Symposium on Communications and Information Technologies (pp. 1062–1065). https://doi.org/10.1109/ISCIT.2006.339940
Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology,12(2), ep272. https://doi.org/10.30935/cedtech/8247
Chiu, C.-F. (2020). Facilitating K-12 teachers in ceating apps by visual programming and project-based learning. International Journal of Emerging Technologies in Learning (iJET),15(01), 103–118. https://doi.org/10.3991/ijet.v15i01.11013
Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator,15(3), 6–11.
Csanadi, A., Eagan, B., Kollar, I., Shaffer, D. W., & Fischer, F. (2018). When coding-and-counting is not enough: Using epistemic network analysis (ENA) to analyze verbal data in CSCL research. International Journal of Computer-Supported Collaborative Learning,13(4), 419–438. https://doi.org/10.1007/s11412-018-9292-z
CSTA, & ISTE. (2011). Operational definition of computational thinking for K–12 education. Retrieved February 14, 2023, from http://www.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf
Dagienė, V., & Futschek, G. (2008). Bebras international contest on informatics and computer literacy: Criteria for good tasks. In R. T. Mittermeir & M. M. Sysło (Eds.), Informatics Education—Supporting Computational Thinking (Vol. 5090, pp. 19–30). Springer. https://doi.org/10.1007/978-3-540-69924-8_2
Dagienė, V., & Stupurienė, G. (2016). Bebras—A sustainable community building model for the concept based learning of informatics and computational thinking. Informatics in Education, 15(1), 25–44. https://doi.org/10.15388/infedu.2016.02
Dale, N. B., & Weems, C. (2014). Programming and problem solving with C++ (sixth edition). Jones & Bartlett Publishers.
Davies, A., Fidler, D., & Gorbis, M. (2011). Future Work Skills 2020. Institute for the Future for University of Phoenix Research Institute.
Demir, Ö., & Seferoglu, S. S. (2021). The effect of determining pair programming groups according to various individual difference variables on group compatibility, flow, and coding performance. Journal of Educational Computing Research,59(1), 41–70. https://doi.org/10.1177/0735633120949787
Echeverría, L., Cobos, R., & Morales, M. (2019). Improving the students computational thinking skills with collaborative learning techniques. IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje,14(4), 196–206. https://doi.org/10.1109/RITA.2019.2952299
Fang, J.-W., Shao, D., Hwang, G.-J., & Chang, S.-C. (2022). From critique to computational thinking: A peer-assessment-supported problem identification, flow definition, coding, and testing approach for computer programming instruction. Journal of Educational Computing Research,60(5), 1301–1324. https://doi.org/10.1177/07356331211060470
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
Gašević, D., Joksimović, S., Eagan, B. R., & Shaffer, D. W. (2019). SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior,92, 562–577. https://doi.org/10.1016/j.chb.2018.07.003
Ghanizadeh, A. (2017). The interplay between reflective thinking, critical thinking, self-monitoring, and academic achievement in higher education. Higher Education,74(1), 101–114. https://doi.org/10.1007/s10734-016-0031-y
Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education,25(2), 199–237. https://doi.org/10.1080/08993408.2015.1033142
Hannay, J. E., Dybå, T., Arisholm, E., & Sjøberg, D. I. (2009). The effectiveness of pair programming: A meta-analysis. Information and Software Technology, 51(7), 1110–1122. https://doi.org/10.1016/j.infsof.2009.02.001
Harvey, B. (1997). Computer science logo style: Symbolic computing (Vol. 1). MIT Press. Retrieved March 9, 2024, from https://sc.panda321.com/extdomains/books.google.com/books/about/Computer_Science_Logo_Style_Symbolic_com.html?hl=zh-CN&id=BmuqURW0G5UC
He, X., Fang, J., Cheng, H. N. H., Men, Q., & Li, Y. (2023). Investigating online learners’ knowledge structure patterns by concept maps: A clustering analysis approach. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11633-8
Hopcan, S., Polat, E., & Albayrak, E. (2022). Collaborative behavior patterns of students in programming instruction. Journal of Educational Computing Research,60(4), 1035–1062. https://doi.org/10.1177/07356331211062260
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. https://doi.org/10.1016/j.compedu.2018.07.004
Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages & Computing,13(3), 259–290. https://doi.org/10.1006/jvlc.2002.0237
Ibrahim, N., Saifuzzin, N. F. S., Seman, A. A., Wahab, N. A., & Osman, A. (2018). Flowchart discovery game for basic programming course (FlowGame). Journal of Applied and Fundamental Sciences,10(1S), 1109–1122. https://doi.org/10.4314/jfas.v10i1s.81
Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education,82, 263–279. https://doi.org/10.1016/j.compedu.2014.11.022
ISTE. (2015). CT Leadership toolkit. Retrieved March 9, 2024, from https://www.iste.org/standards/iste-standards-for-computational-thinking
Kafai, Y. B. (2016). From computational thinking to computational participation in K–12 education. Communications of the ACM,59(8), 26–27. https://doi.org/10.1145/2955114
Kolloffel, B., Eysink, T. H. S., & de Jong, T. (2011). Comparing the effects of representational tools in collaborative and individual inquiry learning. International Journal of Computer-Supported Collaborative Learning,6(2), 223–251. https://doi.org/10.1007/s11412-011-9110-3
Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior,72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior,19(3), 335–353. https://doi.org/10.1016/S0747-5632(02)00057-2
Lai, X., & Wong, G. K. (2022). Collaborative versus individual problem solving in computational thinking through programming: A meta-analysis. British Journal of Educational Technology,53(1), 150–170. https://doi.org/10.1111/bjet.13157
Lee, Y.-J. (2011). Empowering teachers to create educational software: A constructivist approach utilizing Etoys, pair programming and cognitive apprenticeship. Computers & Education,56(2), 527–538. https://doi.org/10.1016/j.compedu.2010.09.018
Li, W., Liu, C.-Y., & Tseng, J. C. R. (2023). Effects of the interaction between metacognition teaching and students’ learning achievement on students’ computational thinking, critical thinking, and metacognition in collaborative programming learning. Education and Information Technologies,28(10), 12919–12943. https://doi.org/10.1007/s10639-023-11671-2
Lodi, M. (2020). Informatical Thinking. OLYMPIADS IN INFORMATICS, 113–132. https://doi.org/10.15388/ioi.2020.09
López-Pellisa, T., Rotger, N., & Rodríguez-Gallego, F. (2021). Collaborative writing at work: Peer feedback in a blended learning environment. Education and Information Technologies,26(1), 1293–1310. https://doi.org/10.1007/s10639-020-10312-2
Lui, D., Walker, J. T., Hanna, S., Kafai, Y. B., Fields, D., & Jayathirtha, G. (2020). Communicating computational concepts and practices within high school students’ portfolios of making electronic textiles. Interactive Learning Environments,28(3), 284–301. https://doi.org/10.1080/10494820.2019.1612446
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
Marquart, C. L., Hinojosa, C., Swiecki, Z., Eagan, B., & Shaffer, D. W. (2018). Epistemic network analysis [Software] Version 1.6. 0. [Computer software]. epistemicnetwork. org.
Martin, A. J., & Collie, R. J. (2019). Teacher–student relationships and students’ engagement in high school: Does the number of negative and positive relationships with teachers matter? Journal of Educational Psychology,111(5), 861–876. https://doi.org/10.1037/edu0000317
McCormick, D., & Ross, S. M. (1990). Effects of computer access and flowcharting on students’ attitudes and performance in learning computer programming. Journal of Educational Computing Research,6(2), 203–213. https://doi.org/10.2190/E3DQ-YN2T-7U0V-JQ5N
Meier, A., Spada, H., & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning,2(1), 63–86. https://doi.org/10.1007/s11412-006-9005-x
Mohaghegh, M., & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer Science and Information Technologies,7(3), 1524–1530. Retrieved March 9, 2024, from http://www.ijcsit.com/docs/Volume%207/vol7issue3/ijcsit20160703104.pdf
Nassi, I., & Shneiderman, B. (1973). Flowchart techniques for structured programming. ACM SIGPLAN Notices,8(8), 12–26. https://doi.org/10.1145/953349.953350
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
Olsen, J. K., Sharma, K., Rummel, N., & Aleven, V. (2020). Temporal analysis of multimodal data to predict collaborative learning outcomes. British Journal of Educational Technology,51(5), 1527–1547. https://doi.org/10.1111/bjet.12982
Ouyang, F., Tang, Z., Cheng, M., & Chen, Z. (2023). Using an integrated discourse analysis approach to analyze a group’s collaborative argumentation. Thinking Skills and Creativity,47, 101227. https://doi.org/10.1016/j.tsc.2022.101227
Papert, S. A. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. BasicBooks.
Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning,1(1), 95–123. https://doi.org/10.1007/BF00191473
Plonka, L., Sharp, H., van der Linden, J., & Dittrich, Y. (2015). Knowledge transfer in pair programming: An in-depth analysis. International Journal of Human-Computer Studies,73, 66–78. https://doi.org/10.1016/j.ijhcs.2014.09.001
Rahman, K., & Nordin, M. J. (2007). A review on the static analysis approach in the automated programming assessment systems. National Conference on Software Engineering and Computer Systems.
Rolim, V., Ferreira, R., Lins, R. D., & Gǎsević, D. (2019). A network-based analytic approach to uncovering the relationship between social and cognitive presences in communities of inquiry. The Internet and Higher Education,42, 53–65. https://doi.org/10.1016/j.iheduc.2019.05.001
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. https://doi.org/10.1016/j.chb.2016.08.047
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. https://doi.org/10.1016/j.compedu.2016.03.003
Salleh, S. M., Shukur, Z., & Judi, H. M. (2018). Scaffolding model for efficient programming learning based on cognitive load theory. International Journal of Pure and Applied Mathematics, 118(7), 77–83. Retrieved March 9, 2024, from https://acadpubl.eu/jsi/2018-118-7-9/articles/7/10.pdf
Sankaranarayanan, S., Kandimalla, S. R., Bogart, C., Murray, R. C., Hilton, M., Sakr, M. F., & Rose, C. P. (2022). Collaborative programming for work-relevant learning: Comparing programming practice with example-based reflection for student learning and transfer task performance. IEEE Transactions on Learning Technologies,15(5), 594–604. https://doi.org/10.1109/TLT.2022.3169121
Satratzemi, M., Xinogalos, S., Tsompanoudi, D., & Karamitopoulos, L. (2021). A two-year evaluation of distributed pair programming assignments by undergraduate students. In T. Tsiatsos, S. Demetriadis, A. Mikropoulos, & V. Dagdilelis (Eds.), Research on E-Learning and ICT in Education (pp. 35–57). Springer International Publishing. https://doi.org/10.1007/978-3-030-64363-8_3
Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition. 18th Annual Conference on Innovation and Technology in Computer Science Education. Retrieved March 9, 2024, from https://eprints.soton.ac.uk/356481/
Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 3. https://doi.org/10.18608/jla.2016.33.3
Siddiq, F., & Scherer, R. (2017). Revealing the processes of students’ interaction with a novel collaborative problem solving task: An in-depth analysis of think-aloud protocols. Computers in Human Behavior,76, 509–525. https://doi.org/10.1016/j.chb.2017.08.007
Smith, G. F., & Browne, G. J. (1993). Conceptual foundations of design problem solving. IEEE Transactions on Systems, Man, and Cybernetics,23(5), 1209–1219. https://doi.org/10.1109/21.260655
Soller, A., & Lesgold, A. (2007). Modeling the process of collaborative learning. In H. U. Hoppe, H. Ogata, & A. Soller (Eds.), The Role of Technology in CSCL (pp. 63–86). Springer US. https://doi.org/10.1007/978-0-387-71136-2_5
Soto, M. S., & Figueroa, I. (2018). Heuristic evaluation of code::blocks as a tool for first year programming courses. 37th International Conference of the Chilean Computer Science Society (SCCC), 1–8. https://doi.org/10.1109/SCCC.2018.8705158
Su, Q., Zhang, W., Wang, H., & Li, H. (2022). Research on project-based learning of information technology curriculum for cultivating senior high school students’ computational thinking. China Academic Journal Electronic Publishing House, 43(8), 109–115+122. https://doi.org/10.13811/j.cnki.eer.2022.08.014
Sun, L., & Zhou, L. (2023). Does text-based programming improve K-12 students’ CT skills? Evidence from a meta-analysis and synthesis of qualitative data in educational contexts. Thinking Skills and Creativity,49, 101340. https://doi.org/10.1016/j.tsc.2023.101340
Sun, M., Wang, M., Wegerif, R., & Peng, J. (2022). How do students generate ideas together in scientific creativity tasks through computer-based mind mapping? Computers & Education,176, 104359. https://doi.org/10.1016/j.compedu.2021.104359
Supena, I., Darmuki, A., & Hariyadi, A. (2021). The influence of 4C (constructive, critical, creativity, collaborative) learning model on students’ learning outcomes. International Journal of Instruction,14(3), 873–892. https://doi.org/10.29333/iji.2021.14351a
Swiecki, Z., Ruis, A. R., Farrell, C., & Shaffer, D. W. (2020). Assessing individual contributions to collaborative problem solving: A network analysis approach. Computers in Human Behavior,104, 105876. https://doi.org/10.1016/j.chb.2019.01.009
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
Threekunprapa, A., & Yasri, P. (2020). Unplugged coding using flowblocks for promoting computational thinking and programming among secondary school students. International Journal of Instruction,13(3), 207–222. https://doi.org/10.29333/iji.2020.13314a
Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review. Computers & Education,162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
Tsan, J., Vandenberg, J., Zakaria, Z., Wiggins, J. B., Webber, A. R., Bradbury, A., Lynch, C., Wiebe, E., & Boyer, K. E. (2020). A comparison of two pair programming configurations for upper elementary students. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 346–352. https://doi.org/10.1145/3328778.3366941
Tsingos, C., Bosnic-Anticevich, S., & Smith, L. (2015). Learning styles and approaches: Can reflective strategies encourage deep learning? Currents in Pharmacy Teaching and Learning,7(4), 492–504. https://doi.org/10.1016/j.cptl.2015.04.006
Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard University Press. Retrieved March 9, 2024, from https://xs.zidianzhan.net/books/about/Mind_in_Society.html?hl=zh-CN&id=RxjjUefze_oC
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. https://doi.org/10.1016/j.compedu.2012.01.007
Wei, X., Lin, L., Meng, N., Tan, W., & Kong, S. C. (2021). The effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers & Education,160, 104023. https://doi.org/10.1016/j.compedu.2020.104023
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. https://doi.org/10.1007/s10956-015-9581-5
Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: Measuring computational thinking in middle school. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, 215–220. https://doi.org/10.1145/2157136.2157200
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.https://doi.org/10.1098/rsta.2008.0118
Wing, J. M. (2011). Research notebook: Computational thinking—What and why? The Link Magazine, 6, 20–23. Retrieved March 9, 2024, from http://link.cs.cmu.edu/files/11-399_The_Link_Newsletter-3.pdf
Wu, B., Hu, Y., Ruis, A. R., & Wang, M. (2019). Analysing computational thinking in collaborative programming: A quantitative ethnography approach. Journal of Computer Assisted Learning,35(3), 421–434. https://doi.org/10.1111/jcal.12348
Xiao, M., & Yu, X. (2017). A model of cultivating computational thinking based on visual programming. International Conference of Educational Innovation through Technology (EITT),2017, 75–80. https://doi.org/10.1109/EITT.2017.26
Xu, H., Huang, D., Leng, J., & Xu, X. (2020). Investigating the developmental trajectory of critical thinking in online discourse among college students: An epistemic network analysis. The Interdisciplinarity of the Learning Sciences,1, 509–512. Retrieved March 9, 2024, from https://repository.isls.org//handle/1/6681.
Yağcı, M. (2019). A valid and reliable tool for examining computational thinking skills. Education and Information Technologies,24(1), 929–951. https://doi.org/10.1007/s10639-018-9801-8
Yücel, Ü. A., & Usluel, Y. K. (2016). Knowledge building and the quantity, content and quality of the interaction and participation of students in an online collaborative learning environment. Computers & Education,97, 31–48. https://doi.org/10.1016/j.compedu.2016.02.015
Zhang, J.-H., Meng, B., Zou, L.-C., Zhu, Y., & Hwang, G.-J. (2021). Progressive flowchart development scaffolding to improve university students’ computational thinking and programming self-efficacy. Interactive Learning Environments,31(6), 3792–3809. https://doi.org/10.1080/10494820.2021.1943687
Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education,141, 103607. https://doi.org/10.1016/j.compedu.2019.103607
Zhang, S., Gao, Q., Sun, M., Cai, Z., Li, H., Tang, Y., & Liu, Q. (2022). Understanding student teachers’ collaborative problem solving: Insights from an epistemic network analysis (ENA). Computers & Education,183, 104485. https://doi.org/10.1016/j.compedu.2022.104485
Zhang, S., Li, H., Wen, Y., Zhang, Y., Guo, T., & He, X. (2023). Exploration of a group assessment model to foster student teachers’ critical thinking. Thinking Skills and Creativity,47, 101239. https://doi.org/10.1016/j.tsc.2023.101239
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. https://doi.org/10.1177/0735633115608444
Acknowledgements
This research was supported by National Natural Science Foundation of China (NSFC) for the Project “A Study on the Perception and Attribution Analysis of Learners' Higher-Order Thinking Activities” (No.: 62177023), and the project of the Faculty of Artificial Intelligence In Education of Central China Normal University (No.: 2022XY014). We are grateful for the support from NSFC and Central China Normal University. Any opinions expressed herein are those of the authors and do not necessarily represent the funds' views. We thank the teacher and students for their participation.
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Ruijie Zhou contributed the central idea, performed the research, analyzed most of the data, and wrote the initial draft of the paper. Yangyang Li contributed to refining the ideas and carrying out additional analyses. Xiuling He developed the idea for the study, formed overall research objectives, and provided an implementable environment for experiments. Chunlian Jiang contributed to refining the ideas and finalizing this paper. Jing Fang contributed to refining the ideas. Yue Li contributed to refining the ideas.
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Zhou, R., Li, Y., He, X. et al. Understanding undergraduates’ computational thinking processes: Evidence from an integrated analysis of discourse in pair programming. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12597-z
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DOI: https://doi.org/10.1007/s10639-024-12597-z