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首页> 外文期刊>International Journal of Soft Computing and Software Engineering >Using Machine Learning to Model How Students Learn to Program
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Using Machine Learning to Model How Students Learn to Program

机译：使用机器学习来模拟学生如何编程

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摘要

Gaining insight into how students learn to program is a critical factor in improving software engineering education. Despite the potential wealth of educational indicators expressed in students' approaches to completing programming assignments, how students arrive at their final solution is largely overlooked in courses--only their final program submission is evaluated as an indicator of their understanding of how to solve a particular programming problem. In this talk, we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a programming assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class. Our eventual goal is to be able better understand students' learning and the conceptual difficulties they may encounter as novice programmers so as to be able to provide better and more personalized guidance to them during their learning process, and ultimately improve education in software engineering.
机译：深入了解学生如何学习编程是改善软件工程教育的关键因素。尽管在学生完成编程作业的方法中表达了潜在的丰富教育指标，但在课程中却很大程度上忽略了学生如何达到最终解决方案-仅评估他们提交的最终课程可以作为他们了解如何解决特定问题的指标编程问题。在本次演讲中，我们介绍一种使用机器学习技术自主创建图形模型的方法，该模型介绍入门编程课程中的学生如何通过编程作业进行学习。随后，我们证明了该模型可以预测哪些学生将在课堂上稍后介绍的材料中挣扎。我们最终的目标是能够更好地了解学生的学习以及他们作为新手程序员可能遇到的概念上的困难，以便能够在他们的学习过程中为他们提供更好，更个性化的指导，并最终改善软件工程方面的教育。

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