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Cheating Detection Method Based on Improved Cognitive Diagnosis Model

  • Zhizhuang Li
  • Zhengzhou ZhuEmail author
  • Qiongyu Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11841)

Abstract

Cheating in examinations destroys the principles of fairness and justice in evaluation. Cheating detection is of great practical significance. Traditional cheating detection methods have many disadvantages, such as difficult to detect covert equipment cheating, multi-source cheating, difficult to distinguish plagiarists from plagiarists, difficult to distinguish plagiarists from victims, or plagiarism from coincidences. In this paper, the concept of knowledge point mastery Index is introduced to measure students’ mastery of a certain knowledge point, and a test method of cheating based on improved cognitive diagnostic model is proposed. This method calculates the weight of each knowledge point in every examination question through linear regression and EM algorithm according to students’ historical learning behavior, and then calculates students’ mastering degree of knowledge point based on historical answers. Then calculate the mastering degree of knowledge point based on the examination results. Finally, we compare the mastering degree of knowledge point based on the examination results and the historical answers to detect students’ cheating situation. The experiments show that the precision and recall rate of this method are significantly higher than those of the method based on the false-same rate, the method based on the false-same rate and the right-same rate and the method based on the Person-Fit index.

Keywords

Weight of knowledge point Cheating detection Mastering degree of knowledge point Linear regression Expectation maximization algorithm 

Notes

Acknowledgment

This paper was supported by National Key Research and Development Program of China (Grant No. 2017YFB1402400), Ministry of Education “Tiancheng Huizhi” Innovation Promotes Education Fund (Grant No. 2018B01004), National Natural Science Foundation of China (Grant No. 61402020, 61573356), and CERNET Innovation Project (Grant No. NGII20170501).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina
  2. 2.Anhui Normal UniversityWuhuChina

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