Automated Plagiarism Detection for Computer Programming Exercises Based on Patterns of Resubmission
Automated Plagiarism Detection for Computer Programming Exercises Based on Patterns of Resubmission
复制标题
基于重新提交模式的计算机编程练习自动抄袭检测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Noelle
中科院分区：
文献类型：
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作者：
Narjes Tahaei;D. Noelle
计算机程序设计习题的抄袭检测是一个难题。传统的策略是比较一个班级所有学生的提交，寻找暗示抄袭的提交之间的相似之处。存在自动化工具来比较提交以帮助进行这种搜索。然而，越来越多的教师允许学生提交多个解决方案，在提交之间接收形成性反馈，反馈通常由自动评估系统生成。允许多次提交允许一个全新的方式来检测剽窃。具体来说，学生可能会在练习中挣扎，直到沮丧导致他们提交不属于自己的作品。我们提出了一种方法，用于检测剽窃的序列由个别学生提交。我们已经探索了各种各样的措施，计划的变化提交，我们已经发现了一组功能，可以转换，使用逻辑回归，到得分捕捉剽窃的可能性。我们已经应用这种方法的数据从四个练习从本科编程类。我们发现，我们自动生成的分数与专家讲师对剽窃的评估密切相关。因此，分数可以作为搜索学术不诚实案例的有力工具。
Plagiarism detection for computer programming exercises is a difficult problem. A traditional strategy has been to compare the submissions from all of the students in a class, searching for similarities between submissions suggestive of copying. Automated tools exist that compare submissions in order to help with this search. Increasingly, however, instructors have allowed students to submit multiple solutions, receiving formative feedback between submissions, with feedback often generated by automated assessment systems. Allowing multiple submissions allows for a fundamentally new way to detect plagiarism. Specifically, students may struggle with an exercise until frustration leads them to submit work that is not their own. We present a method for detecting plagiarism from the sequence of submissions made by an individual student. We have explored a variety of measures of program change over submissions, and we have found a set of features that can be transformed, using logistic regression, into a score capturing the likelihood of plagiarism. We have applied this method to data from four exercises from an undergraduate programming class. We show that our automatically generated scores are strongly correlated with the assessments of plagiarism made by an expert instructor. Thus, the scores can act as a powerful tool for searching for cases of academic dishonesty.