Log Analysis Using Bayesian Networks in a Card Operation Based Programming Learning Support System
Abstract
The Card Operation Programming Study Support System (COPS) was developed to help beginning students understand the structure of programs. COPS is a card-based programming method in which learners solve problems and create programs by rearranging cards according to problem statements. By sorting cards according to the problem statement, learners can visually understand the structure of the program needed to solve the problem. It is expected to have the same learning effect as the conventional coding format, and its educational effect has been suggested from the perspective of Learning Analytics (LA), which utilizes learning history data. In particular, a method for estimating learners ' thinking patterns by analyzing COPS usage logs using a Bayesian network (BN) has been proposed and its usefulness has been partially confirmed. However, several issues remain in the conventional LA approach, and factors such as the constraints of the probrems faced by the learner have not been adequately taken into account. Therefore, in this study, we propose a method to analyze COPS learning history data with BN to visualize how learners tackle problems in more detail. Specifically, we extract the correct and incorrect patterns of learners ' card operations from the logs and use BN to learn their structure in order to estimate how learners tackle and understand the problems. In our experiments, we analyzed the data obtained for basic problems in the C language. The results showed that certain card manipulation patterns were associated with incorrect answers, confirming that analysis using BN is effective in supporting learners.
References
Google： “Exploring Computational Thinking.” <https://research.google/blog/exploring-computational-thinking/> (see 3.18.2025).
Mungunsukh Jambalsuren, Zixue Cheng: "An Interactive Programming Environ-ment for Enhancing Learning Performance," The University of Aizu, pp. 201–212 (2002).
Resnick M.， et al.：“Scratch: programming for all.” Communications of the ACM，52.11，pp.60-67（2009）．
S.Matsumoto, Y.Hayashi, and T.Hirashima, Development of a program-ming learning system through card manipulation focusing on thinking about relationships between parts, Transactions of the Institute of Electrical Engineers of Japan C, Vol.138, No.8, pp.999-1010 (2018).
M.Makino, Data Mining in e-Learning, Transactions of the Japan Society for Edu-cational Technology, vol.31, no.3, pp.271-283 (2007).
Okamoto, M., Kida, I.: “A Typology of Stumbling Stones of Beginning Learners in ‘Copybook Learning’ of Programming and Its Consideration.” Bulletin of the Prac-tice Center, Vol. 22, pp. 49-53 (2014).
S.Garner, A Tool to Support the Use of Part-Complete Solutions in the Learning of Pro-gramming, Proceeding deconference, pp.222-228, 2001.
Motoki Ishii et al, Evaluation of a Programming Learning Support System Using a Card Method from the Perspective of Cognitive Load, Proceedings of the 79th Conference on Advanced Learning Science and Engineering, SIG-ALST/SIG-ALST,B5(03),pp.11-16, 2017.
S. Matsumoto, Y. Hayashi, and T. Hirashima, Development of a Programming Learning System by Card Manipulation Focused on Considering Relationships among Parts, IEEJ Transactions C, Vol. 138, No. 8, pp. 999-1010, 2018.
S. Iwamoto, S. Morinaga, S. Matsumoto, Y. Hayashi, and M. Hirashima, Learning Analytics in Programming Learning Support System in Card Operation Method, Proceedings of the 2019 Student Research and Presentation Meeting of the Associ-ation for Information Science and Systems in Education, Chugoku Region, O06, pp.187-188, 2020．
T．Hirashima, Learning task-centered learning research: redefining learning tasks as information structures and designing learning activities as structural operations. Artificial Intelligence, 30(3), 277-280.2015.
Garner S.：“Learning to program using part-complete solutions.” Computer Science，p.8-14, 2003．
Ruka Murakami, L. Morinaga, S. Matsumoto, Y. Hayashi, and T. Hirasshima, Learning Effects of a Programming Learning System with Card Operation Method, Proceedings of the 2016 JSiSE Student Research Conference, pp. 203-204, 2018.
K.E. Arnold and M.D. Pistilli. Course signals at purdue: using learn ing analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270, 2012.
Ueno M. Data Mining in e-Learning. Transactions of the Japan Society for Edu-cational Technology, Vol. 31, No. 3, pp. 271-283, 2007.
S. Iwamoto, S. Morinaga, S. Matsumoto, Y. Hayashi, and T. Hirashima. Learning Analytics in a Programming Learning Support System with Card Operation. In Proceedings of the 2019 Student Research Conference of the Association for In-formation Science and Systems in Education, pp. 187-188, 2020.
T. Okudaira, T. Shigematsu, S. Matsumoto. A Study of Learning Analytics Using Probabilistic Models in a Programming Learning Support System with Card Ma-nipulation.The 25th IEEE Hiroshima Section Student Symposium, pp.115-118, 2023.
