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Interpreting Latent Student Knowledge Representations in Programming Assignments

2024-05-13 22:01:03
Nigel Fernandez, Andrew Lan


Recent advances in artificial intelligence for education leverage generative large language models, including using them to predict open-ended student responses rather than their correctness only. However, the black-box nature of these models limits the interpretability of the learned student knowledge representations. In this paper, we conduct a first exploration into interpreting latent student knowledge representations by presenting InfoOIRT, an Information regularized Open-ended Item Response Theory model, which encourages the latent student knowledge states to be interpretable while being able to generate student-written code for open-ended programming questions. InfoOIRT maximizes the mutual information between a fixed subset of latent knowledge states enforced with simple prior distributions and generated student code, which encourages the model to learn disentangled representations of salient syntactic and semantic code features including syntactic styles, mastery of programming skills, and code structures. Through experiments on a real-world programming education dataset, we show that InfoOIRT can both accurately generate student code and lead to interpretable student knowledge representations.

Abstract (translated)

近年来,人工智能在教育领域的进步主要依赖于生成式大型语言模型,包括使用这些模型预测开放性学生答案,而不仅仅是正确性。然而,这些模型的黑盒性质限制了学习到的学生知识表示的可解释性。在本文中,我们首先对通过InfoOIRT(信息 regularized Open-ended Item Response Theory 模型)解释潜在学生知识表示进行了探索。InfoOIRT 通过简单先验分布强制指定固定子集的潜在学生知识状态,并能够在生成学生编程问题的情况下鼓励潜在学生知识状态具有可解释性。InfoOIRT 最大化强制简单先验分布与生成的学生代码之间的互信息,这鼓励模型学习显著的语义和语序代码特征,包括语义风格、编程技能和代码结构。通过在现实世界的编程教育数据集上进行的实验,我们发现InfoOIRT 既能准确生成学生代码,又能产生具有可解释性的学生知识表示。



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