Journal
MATHEMATICS
Volume 9, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/math9141684
Keywords
academic cheating; contract cheating; partial-least-squares regression (PLSR); Pearson correlation tests; statistical analysis
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Funding
- Scientific Research Start-up Foundation for Talent Introduction of Nantong University [19R87]
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This article conducted a statistical analysis of contract cheating in Chinese universities, finding that the main motivation for students to engage in contract cheating is to achieve good grades. Clear regulations, strict penalties for cheating, and the use of cheating-detection software at universities can directly reduce cheating behaviors. Additionally, timely feedback from teachers on cheating assignments and increasing students' sense of achievement in course learning are effective strategies to reduce the likelihood of cheating.
Contract cheating refers to students using third-party online resources to complete their coursework. It is not only a unilateral result of the student, but also has a relationship with educators, as well as social resources. However, little work has been performed to analyze the complex behavioral aspects behind contract cheating in Chinese universities. To this end, this article presents a statistical analysis of contract cheating in Chinese universities. First, a unique parallel survey of educators and students was conducted to collect data from August 2018 to August 2020. Next, statistical analyses were performed to explore students' experiences and attitudes toward contract cheating and the contextual factors that relate to these behaviors. Additionally, Pearson correlation tests were conducted on the survey data to find potential factors for contract cheating. Finally, a multivariate statistical technique, partial-least-squares regression (PLSR), was applied to interpret the results. The results of the statistical analysis showed that the main motivation for contract cheating is to receive good grades (the correlation coefficient rho is 0.1309) from the perspective of students' personal learning; from the side of university management, clear regulations (rho=-0.1378), penalties for cheating (rho=-0.1275), and the use of cheating-detection software (rho=-0.1186) can directly reduce cheating; from the perspective of teachers' teaching, lecturers' feedback on cheating on assignments (rho=-0.1510) can effectively reduce students' cheating behavior; in addition, increasing students' sense of achievement in course learning (rho=-0.2619) also helps to reduce the probability of cheating.
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