Why students engage in cyber-cheating through a collective movement: A case of deviance and collusion

Parks, Rachida; Lowry, Paul Benjamin; Wigand, Rolf T.; Agarwal, Nitin; Williams, Therese L.

doi:10.1016/j.compedu.2018.04.003

Cited by 31 publications

(25 citation statements)

References 35 publications

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“…Journal of Service Science and Management Because there is no way to verify the authenticity of the student's quizzes, tests, essays, and assignments, this can inadvertently encourage cheating, fabrication, plagiarism, and collusion (Northcutt et al, 2016) [52]. Honest students may choose to cheat because they think they will be unfairly disadvantaged if they do not cheat as well (Parks et al, 2018) [53]. Honest students may also cheat if they think the education system is unfair.…”

Section: Issue Of Academic Cheatingmentioning

confidence: 99%

Smart Library and Smart Campus

Chan¹,

Chan²

2018

JSSM

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The consequence of digitalization, Internet proliferation, and technology advancement, our way of teaching and learning has been changing. The role of library has to be converted into a place for discourse, peer collaboration, social learning, and exhibition concourse. Some scholars believed that the future Library is tied to the technological development of Web4.0 which is characterized by the terminology of Convergence, Remixability, Standardization, Participation, Usability, Economy, and Design. This concept has been extended to Campus. Some universities have started to adopt the latest technology to convert their Campus and Library into Smart ones for the interests of students. The findings of this paper support that the latest development of Smart Campus and library aligns with the new trend of education system, and creates positive impact on the competitiveness of a city. The applications in Smart Camus and Library in reality are also revealed.

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Section: Issue Of Academic Cheatingmentioning

confidence: 99%

Smart Library and Smart Campus

Chan¹,

Chan²

2018

JSSM

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“…A result of a study gave an emphasis on the college students who involved in plagiarism activity of working on written tasks with different ways and the low penalties for the misconduct kept these practices existing (Lawa, Tingb, & Jeromeb, 2013). Another research found that sophisticated ICT and its development in the form of social media gave access to the students to be more creative to drag over cyber-cheating behaviors (Parks, Lowry, Wigand, Agarwal, & Williams, 2018). Another report showed that there was not significant difference dealing with cheating practices by gender, but it showed a positive correlation between self-reported cheating and the frequencies of this behavior in mates (David, 2015).…”

Section: Introductionmentioning

confidence: 99%

How Does Rasch Model Reveal Dishonesty between Coastal Students and Easy Grammar Test?

Agust

2019

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Academic dishonesty can occur with the supports of the technology devices and it also can be prevented with the help of the technology with its applications like Rasch Model. It can give detail information of the analyzed data and can trace the academic dishonesty like cheating. The aims of this research are to (1) analyze the grammar test items whether they are difficult items or easy ones using Rasch Model, (2) know the percentage of those who are assumed to do cheating based upon their origins and sex, and (3) expose their patterns in working on the grammar test in the form of multiple choice through the Rasch Model analysis. The researcher hypothesized that the academic dishonesty i.e. cheating was undergone by students who are from rural and urban areas of Riau Archipelago Province. The results of this research were: (1) through the students’ responses analyzed by Rasch Model, the grammar test was for medium ability, (2) The Rasch Model revealed that the percentage came to the number 5.71% or 4 of 70 students who were identified to cheat while working on the grammar test. They were two female students from rural area and the other two male students from urban area, and (3) The Rasch Model revealed that their responses did not represent their ability. The Rasch Model has helped the researcher to exposes the cheating deeds on exams. The practitioners just need methods, approaches, strategies, techniques, and media to prevent them in the future Keywords: Rasch Model, Wright Maps, Grammar Test.

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“…However, academic dishonesty is changing as methods and available technologies evolve and as control and inspection strategies vary [13,14]. For example, the proliferation of the World Wide Web, the popularity of smartphones, and widespread access to social media (Twitter, Facebook, YouTube, microblogs, WhatsApp, WeChat, Tencent QQ, etc.)…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis for Contract Cheating in Chinese Universities

Wang

2021

Mathematics

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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 ρ is 0.1309) from the perspective of students’ personal learning; from the side of university management, clear regulations (ρ=−0.1378), penalties for cheating (ρ=−0.1275), and the use of cheating-detection software (ρ=−0.1186) can directly reduce cheating; from the perspective of teachers’ teaching, lecturers’ feedback on cheating on assignments (ρ=−0.1510) can effectively reduce students’ cheating behavior; in addition, increasing students’ sense of achievement in course learning (ρ=−0.2619) also helps to reduce the probability of cheating.

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Why students engage in cyber-cheating through a collective movement: A case of deviance and collusion

Cited by 31 publications

References 35 publications

Smart Library and Smart Campus

Smart Library and Smart Campus

How Does Rasch Model Reveal Dishonesty between Coastal Students and Easy Grammar Test?

Statistical Analysis for Contract Cheating in Chinese Universities

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