理解和衡量CS1中的增量开发

Anshul Shah, Michael Granado, Mrinal Sharma, J. Driscoll, Leo Porter, W. Griswold, Adalbert Gerald Soosai Raj
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引用次数: 3

摘要

增量开发是编写一小段代码并在继续之前对其进行测试的过程。对于学习编程入门课程的学生来说,增量开发的价值尤其高,因为他们可能会遇到更多的语法错误,缺乏解决复杂bug的熟练程度,并且在努力纠正代码时可能更容易感到沮丧。然而,为了评估旨在教授编程过程(如增量开发)的干预措施的有效性,我们需要制定评估这些过程的措施。在本文中,我们提出了一种度量增量开发的方法。通过对15个学生编码访谈进行定性分析,我们确定了与增量开发相关的编程过程中的常见行为。然后,我们利用超过1000个开发会话的数据集——在编译时大约52000个代码快照——来自动检测在我们的定性分析中确定的常见行为。最后,我们制作了一个正式的度量,称为“增量开发度量”(MID),以量化学生在编程期间使用增量开发的有效性。MID检测常见的非增量开发模式,例如在大量添加代码后过度调试,以自动评估一系列快照。MID与人类对增量的评估一致,准确率超过80%。我们的指标使新的研究方向和干预措施集中在提高学生的发展实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding and Measuring Incremental Development in CS1
Incremental development is the process of writing a small snippet of code and testing it before moving on. For students in introductory programming courses, the value of incremental development is especially higher as they may suffer from more syntax errors, lack the proficiency to address complicated bugs, and may be more prone to frustration when struggling to correct code. However, to evaluate the effectiveness of interventions that aim to teach programming processes such as incremental development, we need to develop measures to assess such processes. In this paper, we present a way to measure incremental development. By qualitatively analyzing 15 student coding interviews, we identified common behaviors in the programming process that relate to incremental development. We then leveraged a dataset of over 1000 development sessions -- about 52,000 code snapshots at compilation time -- to automatically detect the common behaviors identified in our qualitative analysis. Finally, we crafted a formal metric, called the "Measure of Incremental Development'' (MID), to quantify how effectively a student used incremental development during a programming session. The MID detects common non-incremental development patterns such as excessive debugging after large additions of code to automatically assess a sequence of snapshots. The MID aligns with human evaluations of incrementality with over 80% accuracy. Our metric enables new research directions and interventions focused on improving students' development practices.
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