使用深度学习来定位学生代码提交中的错误

Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2 Pub Date : 2022-03-03 DOI:10.1145/3478432.3499048

Shion Fujimori, Mohamed Harmanani, Owais Siddiqui, Lisa Zhang

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引用次数: 2

摘要

我们探索了RNN和CodeBERT深度学习模型，这些模型突出了学生提交的Python编码问题中的错误。我们发现，像AUC这样的标准自动度量并不能很好地与人类的评估相对应，并且只有在使用人类评估时才能看到迁移学习和预训练的收益规模。

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Using Deep Learning to Localize Errors in Student Code Submissions

We explore RNN and CodeBERT deep learning models that highlight errors in student submissions to Python coding problems. We find that a standard automatic metric like AUC does not correspond well to human evaluation, and that the scale of the benefits of transfer learning and pre-training are only seen when using human evaluation.

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Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2

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