Different assignments as different contexts: predictors across assignments and outcome measures in CS1

2020 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2020-10-02 DOI:10.1109/IETC47856.2020.9249217

John Edwards, Joseph Ditton, Bishal Sainju, Joshua Dawson

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引用次数: 2

Abstract

This paper reports an analysis of quantitative data obtained during four weeks of a CS1 course. The data consists of programming events logged while students complete eight programming projects and include keystrokes, text pastes, task switches, and run attempts. We analyze the data to answer two related research questions. The first is which commonly studied student programming behaviors generalize well as predictors across programming assignments. The second question is which commonly studied student programming behaviors generalize well as predictors across outcome measures. We find that of the attributes we tested only a small subset are consistent predictors of success across projects, although most have some correlation in some projects. Few attributes were consistent across performance measures. Considering that many intervention strategies use small numbers of projects for student classification, our results suggest that care should be taken in drawing conclusions from data analyzed in the aggregate, both across programming projects and across performance measures.

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不同的作业作为不同的情境:CS1中跨作业的预测因素和结果测量

本文报告了在CS1课程的四周中获得的定量数据的分析。这些数据包括学生完成8个编程项目时记录的编程事件，包括按键、文本粘贴、任务切换和运行尝试。我们分析数据来回答两个相关的研究问题。首先，通常研究的学生编程行为在编程作业中可以很好地概括为预测因素。第二个问题是，哪些通常被研究的学生编程行为可以很好地概括为结果测量的预测因素。我们发现，在我们测试的属性中，只有一小部分是跨项目成功的一致预测因素，尽管大多数在某些项目中有一些相关性。很少有属性在性能度量中是一致的。考虑到许多干预策略使用少量的项目来对学生进行分类，我们的结果表明，在从总体分析的数据中得出结论时，无论是跨规划项目还是跨绩效衡量指标，都应该小心谨慎。

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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2020 Intermountain Engineering, Technology and Computing (IETC)

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