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首页> 外文会议>IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training >Characterizing the Pedagogical Benefits of Adaptive Feedback for Compilation Errors by Novice Programmers
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Characterizing the Pedagogical Benefits of Adaptive Feedback for Compilation Errors by Novice Programmers

机译：新手程序员对编译错误的自适应反馈的教学效益

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Can automated adaptive feedback for correcting erroneous programs help novice programmers learn to code better? In a large-scale experiment, we compare student performance when tutored by human tutors, and when receiving automated adaptive feedback. The automated feedback was designed using one of two well-known instructional principles: (i) presenting the correct solution for the immediate problem, or (ii) presenting generated examples or analogies that guide towards the correct solution. We report empirical results from a large-scale (N = 480,10,000+ person hour) experiment assessing the efficacy of these automated compilation-error feedback tools. Using the survival analysis on error rates of students measured over seven weeks, we found that automated feedback allows students to resolve errors in their code more efficiently than students receiving manual feedback. However, we also found that this advantage is primarily logistical and not conceptual; the performance benefit seen during lab assignments disappeared during exams wherein feedback of any kind was withdrawn. We further found that the performance advantage of automated feedback over human tutors increases with problem complexity, and that feedback via example and specific repair have distinct, non-overlapping relative advantages for different categories of programming errors. Our results offer a clear and granular delimitation of the pedagogical benefits of automated feedback in teaching programming to novices.
机译：可以自动化的自适应反馈来纠正错误的程序，帮助新手程序员学会更好地编码吗？在大规模的实验中，我们在人体导师辅导时比较学生表现，以及在接收自动适应性反馈时。使用两个公知的教学原理之一设计自动化反馈：（i）呈现出直接问题的正确解决方案，或（ii）呈现出朝向正确溶液的产生的实施例或类似物。我们向大规模（n = 480,10,000多个人小时）实验报告了实证结果，评估了这些自动编译错误反馈工具的功效。利用七周测量的学生误差率的生存分析，我们发现自动反馈允许学生比接受手动反馈的学生更有效地解决其代码中的错误。但是，我们还发现，这一优势主要是物流的，而不是概念;在考试期间，实验室分配期间看到的性能受益消失，其中任何类型的反馈都被撤回。我们进一步发现，对人道辅导器的自动反馈的性能优势随着问题的复杂性而增加，并且通过示例和特定修复的反馈对不同类别的编程误差具有不同的，不重叠的相对优势。我们的结果提供了对新手教学规划中自动反馈的教学效益的明确和粒度划界。

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