探索编程难度与Web访问之间的关系

D. Long, Kun Wang, Jason Carter, P. Dewan
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引用次数: 1

摘要

这项工作解决了网络支持编程的困难。我们进行了一项实验室研究,参与者完成了一项涉及使用Java Swing/AWT API的编程任务。我们发现有关参与者网络访问的信息为了解所面临的困难类型以及如何检测这些困难提供了额外的见解。通过网络搜索无法完全解决的困难包括查找有关AWT/Swing教程、2d图形、组件和事件的信息,其中2d图形导致的问题最多。现有的一种预测难度的算法挖掘了编程环境动作的各个方面,当它使用来自网页访问时间的附加特征时,它发现了更多的难度。这一结果与我们的观察结果一致,即在一定难度下,被试与编程环境的交互很少,在困难期间他们访问网页的次数更多,并且新特征增加了修改后的现有算法特征所没有的信息。然而,绝大多数困难都不涉及网络交互,新功能导致了更高数量的误报,这与非困难和困难期间网络访问的高差异是一致的。
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Exploring the Relationship Between Programming Difficulty and Web Accesses
This work addresses difficulty in web-supported programming. We conducted a lab study in which participants completed a programming task involving the use of the Java Swing/AWT API. We found that information about participant web accesses offered additional insight into the types of difficulties faced and how they could be detected. Difficulties that were not completely solved through web searches involved finding information on AWT/Swing tutorials, 2-D Graphics, Components, and Events, with 2-D Graphics causing the most problems. An existing algorithm to predict difficulty that mined various aspects of programming-environment actions detected more difficulties when it used an additional feature derived from the times when web pages were visited. This result is consistent with our observation that during certain difficulties, subjects had little interaction with the programming environment, they made more web visits during difficulty periods, and the new feature added information not available from features of the modified existing algorithm. The vast majority of difficulties, however, involved no web interaction and the new feature resulted in higher number of false positives, which is consistent with the high variance in web accesses during both non-difficulty and difficulty periods.
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