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Research Article

The Effects of Three Scaffoldings on Computer-Supported, Robot-Assisted Collaborative Programming in Higher Education

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Received 02 Jan 2024, Accepted 13 May 2024, Published online: 24 May 2024

References

  • Amelia, R., Handayanto, S. K., & Muhardjito, M. (2016). The influence of V diagram procedural scaffolding in group investigation towards students with high and low prior knowledge. Journal Pendidikan IPA Indonesia, 5(1), 108–115. https://doi.org/10.15294/jpii.v5i1.5799
  • Andersen, R., Mørch, A. I., & Litherland, K. T. (2022). Collaborative learning with block-based programming: Investigating human-centered artificial intelligence in education. Behaviour & Information Technology, 41(9), 1830–1847. https://doi.org/10.1080/0144929X.2022.2083981
  • 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
  • Blikstein, P. (2013). Multimodal learning analytics. In R. F. Kizilcec, C. Piech, E. Schneider, D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), Proceedings of the third international conference on learning analytics and knowledge (pp. 102–106). ACM. https://doi.org/10.1145/2460296.2460316
  • Byrne, D., & Callaghan, G. (2014). Complexity theory and the social sciences. Routledge.
  • Chittum, J. R., Jones, B. D., Akalin, S., & Schram, Á. B. (2017). The effects of an afterschool STEM program on students’ motivation and engagement. International Journal of STEM Education, 4(1), 11–26. https://doi.org/10.1186/s40594-017-0065-4
  • Clarke, L. W., & Bartholomew, A. (2014). Digging beneath the surface: Analyzing the complexity of instructors’ participation in asynchronous discussion. Online Learning, 18(3), 1–2. https://doi.org/10.24059/olj.v18i3.414
  • Damon, W., & Phelps, E. (1989). Critical distinctions among three approaches to peer education. International Journal of Educational Research, 13(1), 9–19. https://doi.org/10.1016/0883-0355(89)90013-X
  • Darvishi, A., Khosravi, H., Sadiq, S., & Gašević, D. (2022). Incorporating AI and learning analytics to build trustworthy peer assessment systems. British Journal of Educational Technology, 53(4), 844–875. https://doi.org/10.1111/bjet.13233
  • Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.) Collaborative-learning: Cognitive and computational approaches (pp. 1–19). Elsevier.
  • Erdei, R., Springer, J. A., & Whittinghill, D. M. (2017). An impact comparison of two instructional scaffolding strategies employed in our programming laboratories: Employment of a supplemental teaching assistant versus employment of the pair programming methodology. In 2017 IEEE Frontiers in Education Conference (FIE) (pp. 1–6). IEEE. https://doi.org/10.1109/FIE.2017.8190650
  • Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37. https://doi.org/10.18637/jss.v040.i04
  • Hong, H. Y., & Lin, P. Y. (2019). Elementary students enhancing their understanding of energy-saving through idea-centered collaborative knowledge-building scaffolds and activities. Educational Technology Research and Development, 67(1), 63–83. https://doi.org/10.1007/s11423-018-9606-x
  • Hong, H. Y., & Sullivan, F. R. (2009). Towards an idea-centered, principle-based design approach to support learning as knowledge creation. Educational Technology Research and Development, 57(5), 613–627. https://doi.org/10.1007/s11423-009-9122-0
  • Janssen, J., Cress, U., Erkens, G., & Kirschner, P. A. (2013). Multilevel analysis for the analysis of collaborative learning. In C. E. Hmelo-Silver, C. A. Chinn, C. K. K. Chan, & A. M. O’Donnell (Eds.), The international handbook of collaborative learning (pp. 124–137). Routledge.
  • Johnson, E. K. (2019). Waves: Scaffolding self-regulated learning to teach science in a whole-body educational game. Journal of Science Education and Technology, 28(2), 133–151. https://doi.org/10.1007/s10956-018-9753-1
  • Julià, C., & Antolí, J. Ò. (2016). Spatial ability learning through educational robotics. International Journal of Technology and Design Education, 26(2), 185–203. https://doi.org/10.1007/s10798-015-9307-2
  • Kim, S., & Lee, Y. (2016). The effect of robot programming education on attitudes towards robots. Indian Journal of Science and Technology, 9(24), 1–11. https://doi.org/10.17485/ijst/2016/v9i24/96104
  • Kim, N. J., Vicentini, C. R., & Belland, B. R. (2022). Influence of scaffolding on information literacy and argumentation skills in virtual field trips and problem-based learning for scientific problem solving. International Journal of Science and Mathematics Education, 20(2), 215–236. https://doi.org/10.1007/s10763-020-10145-y
  • Marquart, C. L., Hinojosa, C., Swiecki, Z., Eagan, B., & Shaffer, D. W. (2018). Epistemic network analysis (Version 1.6.0.) [Software]. epistemicnetwork.org
  • Medina, R., & Stahl, G. (2021). Analysis of group practices. In U. Cress, C. Rosé, A. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning (pp. 199–218). Springer.
  • Mu, S., Cui, M., & Huang, X. (2020). Multimodal data fusion in learning analytics: A systematic review. Sensors (Basel, Switzerland), 20(23), 6856. https://doi.org/10.3390/s20236856
  • Nguyen, H. (2022). Let’s teach Kibot: Discovering discussion patterns between student groups and two conversational agent designs. British Journal of Educational Technology, 53(6), 1864–1884. https://doi.org/10.1111/bjet.13219
  • Ochoa, X., Lang, A. C., & Siemens, G. (2017). Multimodal learning analytics. In Handbook of learning analytics (pp. 129–141). Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.011
  • Ortiz, O. O., Franco, J. A. P., Garau, P. M. A., & Martin, R. H. (2017). Innovative mobile robot method: Improving the learning of programming languages in engineering degrees. IEEE Transactions on Education, 60(2), 143–148. https://doi.org/10.1109/TE.2016.2608779
  • Ouyang, F., Dai, X., & Chen, S. (2022). Applying multimodal learning analytics to examine the immediate and delayed effects of instructor scaffoldings on small groups’ collaborative programming. International Journal of STEM Education, 9(1), 45. https://doi.org/10.1186/s40594-022-00361-z
  • Ouyang, F., Wu, M., Zhang, L., Xu, W., Zheng, L., & Cukurova, M. (2023). Making strides towards AI-supported regulation of learning in collaborative knowledge construction. Computers in Human Behavior, 142, 107650. https://doi.org/10.1016/j.chb.2023.107650
  • Ouyang, F., & Xu, W. (2022). The effects of three instructor participatory roles on a small group’s collaborative concept mapping. Journal of Educational Computing Research, 60(4), 930–959. https://doi.org/10.1177/07356331211057283
  • Ouyang, F., & Xu, W. (2024). The effects of educational robotics in STEM education: A multilevel meta-analysis. International Journal of STEM Education, 11(1), 7. https://doi.org/10.1186/s40594-024-00469-4
  • Ouyang, F., Xu, W., & Cukurova, M. (2023). An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective. International Journal of Computer-Supported Collaborative Learning, 18(1), 39–66. https://doi.org/10.1007/s11412-023-09387-z
  • Park, J. B. H., Schallert, D. L., Sanders, A. J. Z., Williams, K. M., Seo, E., Yu, L. T., Vogler, J. S., Song, K., Williamson, Z. H., & Knox, M. C. (2015). Does it matter if the teacher is there? A teacher’s contribution to emerging patterns of interactions in online classroom discussions. Computers & Education, 82, 315–328. https://doi.org/10.1016/j.compedu.2014.11.019
  • Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702
  • Pietarinen, T., Palonen, T., & Vauras, M. (2021). Guidance in computer-supported collaborative inquiry learning: Capturing aspects of affect and teacher support in science classrooms. International Journal of Computer-Supported Collaborative Learning, 16(2), 261–287. https://doi.org/10.1007/s11412-021-09347-5
  • Przybylla, M., & Romeike, R. (2014). Physical computing and its scope - towards a constructionist computer science curriculum with physical computing. Informatics in Education, 13(2), 225–240. https://doi.org/10.15388/infedu.2014.14
  • Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257. https://doi.org/10.1007/S11412-009-9070-Z/FIGURES/4
  • Rogat, T. K., & Adams-Wiggins, K. R. (2015). Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation. Computers in Human Behavior, 52, 589–600. https://doi.org/10.1016/j.chb.2015.01.026
  • Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88(3), 345–372. https://doi.org/10.1002/sce.10130
  • 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
  • Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469–495. https://doi.org/10.1007/s10639-016-9482-0
  • 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), 9–45. https://doi.org/10.18608/jla.2016.33.3
  • Sharma, K., & Giannakos, M. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology, 51(5), 1450–1484. https://doi.org/10.1111/bjet.12993
  • Silva, L., Mendes, A., Gomes, A., & Fortes, G. (2023). Fostering regulatory processes using computational scaffolding. International Journal of Computer-Supported Collaborative Learning, 18(1), 67–100. https://doi.org/10.1007/s11412-023-09388-y
  • Stolz, S. A. (2015). Embodied learning. Educational Philosophy and Theory, 47(5), 474–487. https://doi.org/10.1080/00131857.2013.879694
  • Sullivan, F. R., & Keith, P. K. (2019). Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions. British Journal of Educational Technology, 50(6), 3047–3063. https://doi.org/10.1111/bjet.12875
  • Sun, D., Ouyang, F., Li, Y., & Chen, H. (2020). Three contrasting pairs’ collaborative programming processes in China’s secondary education. Journal of Educational Computing Research, 59(4), 740–762. https://doi.org/10.1177/0735633120973430
  • Sun, D., Ouyang, F., Li, Y., & Zhu, C. (2021). Comparing learners’ knowledge, behaviors, and attitudes between two instructional modes of computer programming in secondary education. International Journal of STEM Education, 8(1), 54. https://doi.org/10.1186/s40594-021-00311-1
  • Tabak, I., & Baumgartner, E. (2004). The teacher as partner: Exploring participant structures, symmetry, and identity work in scaffolding. Cognition and Instruction, 22(4), 393–429. https://doi.org/10.1207/s1532690Xci2204_2
  • van de Pol, J., Mercer, N., & Volman, M. (2019). Scaffolding student understanding in small-group work: Students’ uptake of teacher support in subsequent small-group interaction. Journal of the Learning Sciences, 28(2), 206–239. https://doi.org/10.1080/10508406.2018.1522258
  • van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: A decade of research. Educational psychology review, 22, 271–296. https://doi.org/10.1007/s10648-010-9127-6
  • Vygotsky, L. S. (1978). Mind in society—the development of higher psychological processes. Harvard University Press.
  • Wang, L., Geng, F., Hao, X., Shi, D., Wang, T., & Li, Y. (2021). Measuring coding ability in young children: Relations to computational thinking, creative thinking, and working memory. Current Psychology, 42(10), 8039–8050. https://doi.org/10.1007/s12144-021-02085-9
  • Wei, X., Lin, L., Meng, N., Tan, W., Kong, S.-C., & Kinshuk, (2020). 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
  • Wiltshire, T. J., Steffensen, S. V., & Fiore, S. M. (2019). Multiscale movement coordination dynamics in collaborative team problem solving. Applied Ergonomics, 79, 143–151. https://doi.org/10.1016/j.apergo.2018.07.007
  • Wise, A. F., Knight, S., & Shum, S. B. (2021). Collaborative learning analytics. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning (pp. 425–444). Springer. https://doi.org/10.1007/978-3-030-65291-3_23
  • 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
  • Xu, W., Wu, Y., & Ouyang, F. (2023). Multimodal learning analytics of collaborative patterns during pair programming in higher education. International Journal of Educational Technology in Higher Education, 20(1), 8. https://doi.org/10.1186/s41239-022-00377-z
  • Zhang, D., Hwang, G. J., & Chu, S. T. (2023). Roles of computer agents in digital games: Effects on learning performance, perceptions, and behaviors. Interactive Learning Environments, 1–21. https://doi.org/10.1080/10494820.2023.2256374
  • Zheng, L. (2021). Improving programming skills through an innovative collaborative programming model: A case study. In L. Zheng (Ed.), Lecture notes in educational technology: Data-driven design for computer-supported collaborative learning (pp. 75–85). Springer. https://doi.org/10.1007/978-981-16-1718-8_6
  • Zheng, L., Zhen, Y., Niu, J., & Zhong, L. (2022). An exploratory study on fade-in versus fade-out scaffolding for novice programmers in online collaborative programming settings. Journal of Computing in Higher Education, 34(2), 489–516. https://doi.org/10.1007/s12528-021-09307-w
  • Zhong, B., & Si, Q. (2021). Troubleshooting to learn via scaffolds: Effect on students’ ability and cognitive load in a robotics course. Journal of Educational Computing Research, 59(1), 95–118. https://doi.org/10.1177/0735633120951871

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