Abstract
In learning programming, it is important to learn from the best of other people’s code. So far, we have developed a code-sharing platform with a mechanism for sharing only the code of others who are close to the learner’s level. We believe that sharing code that is close to learners’ strategies is beneficial for helping them learn from others, so in this study we propose a method for evaluating the strategies of code and a method for calculating the closeness of strategies as similarity. We implemented a filtering method that displays only code with a certain level of similarity to the ranking, and conducted experiments to compare the rankings of our proposed method and the conventional method for expert programmers. The results suggest that the ranking of our method promotes better learning.
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Maeda, S., Koike, K., Tomoto, T. (2024). Code-Sharing Platform in Programming Learning: A Proposal for a Strategy-Aware Code-Sharing Methodology. In: Mori, H., Asahi, Y. (eds) Human Interface and the Management of Information. HCII 2024. Lecture Notes in Computer Science, vol 14691. Springer, Cham. https://doi.org/10.1007/978-3-031-60125-5_23
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