In this paper, we propose a quantitative evaluation method of students’
thinking in group learning. Thinking evaluation will become increasingly
important in programming education in Japan. However, it is impossible for
instructors to single-handedly evaluate their students’ thinking at the same
time. It is necessary to provide a quantitative evaluation method that can be
applied to a variety of educational situations in order to help instructors. We
define coding vectors based on students’ source code that will serve as an indicator of evaluation. Moreover, we judge
students’ prospects through a 3-step analysis with their coding vectors.
We analyzed coding vectors for 22 participants obtained through a task
experiment. We evaluated students’ thinking from three perspectives:
visualization, distance, and direction. As a result, all three ways had the
ability to grasp students’ thinking content. Coding vectors allow us to
comprehensively judge students’ coding steps and their prospects. In this
paper, we discuss the expressive power of coding vectors for coding content,
and task settings appropriate for them.
References
[1]
Bosch, N., Chen, Y., & D’Mello, S. (2014). It’s Written on Your Face: Detecting Affective States from Facial Expressions While Learning Computer Programming. In Intelligent Tutoring Systems: 12th International Conference, ITS 2014 (pp. 39-44). Springer International Publishing. https://doi.org/10.1007/978-3-319-07221-0_5
[2]
Cosma, G., & Joy, M. (2008). Towards a Definition of Source-Code Plagiarism. IEEE Transactions on Education, 51, 195-200. https://doi.org/10.1109/TE.2007.906776
[3]
Duric, Z., & Gasevic, D. (2013). A Source Code Similarity System for Plagiarism Detection. The Computer Journal, 56, 70-86. https://doi.org/10.1093/comjnl/bxs018
[4]
Fagerlund, J., Hakkinen, P., Vesisenaho, M., & Viiri, J. (2021). Computational Thinking in Programming with Scratch in Primary Schools: A Systematic Review. Computer Applications in Engineering Education, 29, 12-28. https://doi.org/10.1002/cae.22255
[5]
Grafsgaard, J., Wiggins, J., Boyer, K. E., Wiebe, E., & Lester, J. (2014). Predicting Learning and Affect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue. In Proceedings of the 7th International Conference on Educational Data Mining (EDM) (pp. 122-129).
[6]
Guggemos, J. (2021). On the Predictors of Computational Thinking and Its Growth at the High-School Level. Computers & Education, 161, Article 104060.
https://doi.org/10.1016/j.compedu.2020.104060
[7]
Jaques, N., Conati, C., Harley, J. M., & Azevedo, R. (2014). Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System. In Intelligent Tutoring Systems: 12th International Conference, ITS 2014 (pp. 29-38). Springer International Publishing. https://doi.org/10.1007/978-3-319-07221-0_4
[8]
Lazar, T., & Bratko, I. (2014). Data-Driven Program Synthesis for Hint Generation in Programming Tutors. In Intelligent Tutoring Systems: 12th International Conference, ITS 2014 (pp. 306-311). Springer International Publishing.
https://doi.org/10.1007/978-3-319-07221-0_38
[9]
Molina, A. I., Navarro, ó., Ortega, M., & Lacruz, M. (2018). Evaluating Multimedia Learning Materials in Primary Education Using Eye Tracking. Computer Standards & Interfaces, 59, 45-60. https://doi.org/10.1016/j.csi.2018.02.004
[10]
Sun, L., Hu, L., & Zhou, D. (2021). Which Way of Design Programming Activities Is More Effective to Promote K-12 Students’ Computational Thinking Skills? A Meta-Analysis. Journal of Computer Assisted Learning, 37, 1048-1062.
https://doi.org/10.1111/jcal.12545
[11]
Tanigawa, K., Harada, F., & Shimakawa, H. (2011). Detecting Learning Patterns during Exercise from Function Call Logs. International Journal of Advanced Computer Science, 1, 30-35.
[12]
Tedre, M., & Denning, P. J. (2016). The Long Quest for Computational Thinking. In Proceedings of the 16th Koli Calling International Conference on Computing Education Research (pp. 120-129). Association for Computing Machinery.
https://doi.org/10.1145/2999541.2999542
[13]
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, Article 104083.
https://doi.org/10.1016/j.compedu.2020.104083
[14]
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, Article 104023.
https://doi.org/10.1016/j.compedu.2020.104023
[15]
Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49, 33-35.
https://doi.org/10.1145/1118178.1118215
