Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,554
result(s) for
"Academic misconduct"
Sort by:
Validation of the academic misconduct questionnaire: exploring predictors of student misconduct
Multiple instruments have been used to assess academic misconduct, yet robust psychometric evidence has been reported only for a few. This study aims to determine the validity and dimensionality of a novel Academic Misconduct Questionnaire (AMQ) and to explore differences between students who engage in distinct misbehaviours. A diverse sample of health and non-health students replied to the AMQ. Exploratory and confirmatory factor analyses were conducted using two subsamples. Predictive models were computed for the AMQ and its dimensions. The questionnaire showed good validity and reliability, revealing eight dimensions related to Cheating during (two forms) and prior Exams, Plagiarism, Fraud in Academic Work, Impersonation (assessment), Signature Forgery in attendance sheets and Not Reporting peer misconduct. The predictors of student engagement in each form of misconduct differed, except for perceiving greater peer fraud, which increased the propensity for all misbehaviours. Perceiving higher sanctions reduced the propensity to engage in most forms, while gender played a role in half of them. First-year students were more likely to Not Reporting peer misconduct and less likely to disclose Fraud in Academic Work and Signature Forgery than those in more advanced years. Health students scored higher in most misbehaviours, especially compared to Economics/Law, Social Sciences and Arts/Humanities, while the latter two disclosed higher Signature Forgery. This study proposes a valid instrument to assess academic misconduct in university students. The predictive models helped to better understand differences between students who engaged in distinct misbehaviours, enabling more targeted interventions.
Journal Article
Why College Students Cheat: A Conceptual Model of Five Factors
Though numerous studies have identified factors associated with academic misconduct, few have proposed conceptual models that could make sense of multiple factors. In this study, we used structural equation modeling (SEM) to test a conceptual model of five factors using data from a relatively large sample of 2,503 college students. The results indicated that there is a significant direct association between students' reported lack of self-control and academic misconduct. The association between these two variables was also mediated by students' degree of academic preparation, their involvement in structured and non-structured leisure activities, their perception of opportunities to cheat, and their attitude toward academic misconduct.
Journal Article
Navigating the digital learning landscape: insights into ethical dilemmas and academic misconduct among university students
The impact of COVID-19 has significantly expanded the use of the internet in education, and artificial intelligence technologies such as ChatGPT have become increasingly prominent in the educational field; however, these advancements entail challenges pertaining to academic integrity in higher education. To understand the prevalence of online academic dishonesty (E-AD), this study examines the relationships among personal characteristics, the Ethical Dissonance Index (EDI), perceived severity of harm, online academic ethical judgment, and E-AD among 522 Chinese university students. The findings reveal that science students are more likely to engage in online plagiarism than are humanities students. Male students are more likely to engage in both online plagiarism and cheating than are female students. In addition, female students also exhibit higher perceived severity of harm from both perpetrators’ and nonperpetrators’ perspectives. A cluster analysis of the EDI identified four clusters: pervasive/legitimate, uncommon/illegitimate, pervasive/illegitimate, and uncommon/legitimate. Additionally, the four types of E-AD—plagiarism, facilitation, fabrication, and cheating—exhibited significant negative correlations with perceived harm and online academic ethical judgment among both perpetrators and nonperpetrators. These dishonest behaviors were also positively correlated with each other. Regression analysis further revealed that students' online academic ethical judgments constitute a common predictor of all types of E-AD. This study provides a comprehensive understanding of E-AD among Chinese university students and offers empirical evidence that can inform educational policies and practices.
Journal Article
What Prevents Students from Reporting Academic Misconduct? A Survey of Croatian Students
Academic misconduct is widespread in all cultures, and factors that influence it have been investigated for many years. An act of reporting peers’ misconduct not only identifies and prevents misconduct, but also encourages a student to think and act morally and raises awareness about academic integrity. The aim of this study was to determine factors that prevent students from reporting academic misconduct. A questionnaire to assess views on reporting the academic misconduct of a colleague was developed and sent to all students enrolled at the University of Rijeka, Croatia. Results indicate that a tendency to protect fellow student and to comply with other opinions is the most influential factor that prevents students from reporting peers’ misbehavior. Furthermore, scientific discipline, gender, and age are all significant factors in students’ intention to report peer misconduct. Understanding the factors that influence students’ willingness to report academic misconduct enables faculties, administrators and students to strengthen the ethical culture in the academic community.
Journal Article
Paper mill challenges: past, present, and future
Paper mills are fraudulent organizations that make money by writing fake manuscripts and offering authorship slots for sale to academic customers. Mill activity differs in scale to individual academic misconduct: many thousands of fake paper mill manuscripts have been successfully published in peer-reviewed journals. Despite this, paper mill activity is still relatively unrecognized outside the publishing industry. We discuss what is known about paper mill operations and how publishers, independent organizations, and individuals are working to prevent and detect mill activity. Research readers can also have a part to play in paper mill detection, and we provide detail on what to look out for.
Journal Article
Detection of GPT-4 Generated Text in Higher Education: Combining Academic Judgement and Software to Identify Generative AI Tool Misuse
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated content, only 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved.
Journal Article
How Common is Cheating in Online Exams and did it Increase During the COVID-19 Pandemic? A Systematic Review
Academic misconduct is a threat to the validity and reliability of online examinations, and media reports suggest that misconduct spiked dramatically in higher education during the emergency shift to online exams caused by the COVID-19 pandemic. This study reviewed survey research to determine how common it is for university students to admit cheating in online exams, and how and why they do it. We also assessed whether these self-reports of cheating increased during the COVID-19 pandemic, along with an evaluation of the quality of the research evidence which addressed these questions. 25 samples were identified from 19 Studies, including 4672 participants, going back to 2012. Online exam cheating was self-reported by a substantial minority (44.7%) of students in total. Pre-COVID this was 29.9%, but during COVID cheating jumped to 54.7%, although these samples were more heterogenous. Individual cheating was more common than group cheating, and the most common reason students reported for cheating was simply that there was an opportunity to do so. Remote proctoring appeared to reduce the occurrence of cheating, although data were limited. However there were a number of methodological features which reduce confidence in the accuracy of all these findings. Most samples were collected using designs which makes it likely that online exam cheating is under-reported, for example using convenience sampling, a modest sample size and insufficient information to calculate response rate. No studies considered whether samples were representative of their population. Future approaches to online exams should consider how the basic validity of examinations can be maintained, considering the substantial numbers of students who appear to be willing to admit engaging in misconduct. Future research on academic misconduct would benefit from using large representative samples, guaranteeing participants anonymity.
Journal Article
Challenges of remote assessment in higher education in the context of COVID-19: a case study of Middle East College
Due to the unprecedented COVID-19 incident, higher education institutions have faced different challenges in their teaching-learning activities. Particularly conducting assessments remotely during COVID-19 has posed extraordinary challenges for higher education institutions owing to lack of preparation superimposed with the inherent problems of remote assessment. In the current study, the challenges of remote assessment during COVID-19 incident in higher education institutions were investigated taking Middle East College as a case study. For the study, questionnaires were prepared and data from 50 faculties were collected and analyzed. The study focused on the challenges of remote assessment in general and academic dishonesty in particular. The main challenges identified in remote assessment were academic dishonesty, infrastructure, coverage of learning outcomes, and commitment of students to submit assessments. To minimize academic dishonesty, preparing different questions to each student was found to be the best approach. Online presentation was also found to be good option to control academic integrity violations. Combining various assessment methods, for instance report submission with online presentation, helps to minimize academic dishonesty since the examiner would have a chance to confirm whether the submitted work is the work of the student.
Journal Article