Modeling programming knowledge for mentoring at scale

A. Pai, Philip J. Guo, Rob Miller
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引用次数: 2

Abstract

In large programming classes, MOOCs or online communities, it is challenging to find peers and mentors to help with learning specific programming concepts. In this paper we present first steps towards an automated, scalable system for matching learners with Python programmers who have expertise in different areas. The learner matching system builds a knowledge model for each programmer by analyzing their authored code and extracting features that capture domain knowledge and style. We demonstrate the feasibility of a simple model that counts the references to modules from the standard library and Python Package Index in a programmers' code. We also show that programmers exhibit self-selection using which we can extract the modules a programmer is best at, even though we may not have all of their code. In our future work we aim to extend the model to encapsulate more features, and apply it for skill matching in a programming class as well as personalizing answers on StackOverflow.
为大规模指导建模编程知识
在大型编程课程、mooc或在线社区中,很难找到同伴和导师来帮助学习特定的编程概念。在本文中,我们提出了实现自动化、可扩展系统的第一步,该系统用于将学习者与具有不同领域专业知识的Python程序员相匹配。学习者匹配系统通过分析每个程序员编写的代码并提取捕获领域知识和风格的特征,为每个程序员构建知识模型。我们演示了一个简单模型的可行性,该模型计算了程序员代码中对标准库和Python包索引模块的引用。我们还展示了程序员表现出的自我选择,使用它我们可以提取程序员最擅长的模块,即使我们可能没有他们所有的代码。在我们未来的工作中,我们的目标是扩展模型以封装更多的特征,并将其应用于编程课程中的技能匹配以及StackOverflow上的个性化答案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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