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Exploring the Relationship Between Programming Difficulty and Web Accesses

机译:探索编程难度与Web访问之间的关系

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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.
机译:这项工作解决了Web支持的编程中的困难。我们进行了一项实验室研究,参与者在其中完成了涉及使用Java Swing / AWT API的编程任务。我们发现,有关参与者Web访问的信息可以进一步了解所面临的困难类型以及如何检测到这些困难。通过网络搜索无法完全解决的难题包括在AWT / Swing教程,2-D图形,组件和事件中查找信息,其中2-D图形引起最多的问题。现有的用于预测难度的算法可挖掘编程环境行为的各个方面,当它使用从访问网页的时间派生的其他功能时,会发现更多的困难。该结果与我们的观察结果一致,即在某些困难中,受试者与编程环境之间的交互很少,在困难时期他们进行了更多的Web访问,并且新功能增加了修改后的现有算法的功能无法获得的信息。但是,绝大多数困难都不涉及网络交互,新功能导致误报的数量增加,这与非困难和困难时期的网络访问差异很大。

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