Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines is required. In this article, we investigated unsupervised semantic models (known as topic modeling techniques) to assist computer science teachers in this task and propose a method to transform Computer Science 1 teacher-provided code solutions into representative text documents, including the code structure information. By applying nonnegative matrix factorization and latent Dirichlet allocation techniques, we extract the underlying relationship between questions and validate the results using an external dataset. We consider the interpretability of the learned concepts using 14 university professors’ data, and the results confirm six semantically coherent clusters using the current dataset. Moreover, the six topics comprise the main concepts present in the test dataset, achieving 0.75 in the normalized pointwise mutual information metric. The metric correlates with human ratings, making the proposed method useful and providing semantics for large amounts of unannotated code.

中文翻译：

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使用主题建模方法对计算机科学入门练习进行聚类

手动确定一组问题中存在的概念是一个挑战性且耗时的过程。但是，该过程对于虚拟学习环境建模是必不可少的步骤，因为需要使用精通水平评估和推荐引擎在概念和问题之间进行映射。在本文中，我们研究了无监督语义模型（称为主题建模技术）以协助计算机科学教师完成此任务，并提出了一种将计算机科学1教师提供的代码解决方案转换为具有代表性的文本文档（包括代码结构信息）的方法。通过应用非负矩阵分解和潜在Dirichlet分配技术，我们提取了问题之间的潜在关系，并使用外部数据集验证了结果。我们使用14位大学教授的数据来考虑所学概念的可解释性，结果使用当前数据集确认了六个语义一致的类。此外，这六个主题包括测试数据集中存在的主要概念，在归一化的点向互信息度量中达到0.75。度量与人类评级相关，从而使所提出的方法有用，并为大量未注释的代码提供了语义。