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1 – 2 of 2Monica Gallant and Pranab Kumar Pani
The aim of this paper is to explore the behavior pattern, perceptions, and motivations of students towards academic misconduct in a Business School with a view to mitigating its…
Abstract
Purpose
The aim of this paper is to explore the behavior pattern, perceptions, and motivations of students towards academic misconduct in a Business School with a view to mitigating its occurrence.
Design/methodology/approach
The survey was conducted among a batch of undergraduate students who responded to various dimensions of plagiarism through a structured questionnaire. Responses were measured on a Likert scale. Some non-parametric tests were done for statistical analysis.
Findings
From the perception of the prevalence of plagiarism committed by others, the authors found that the majority tend to believe that it happens in spite of the existence of preventive policies. Based on the findings, the authors conclude that the students who are more prone to cheating/plagiarism are male, with low grade point average, and are not on scholarship. Parental disapproval is found to be one of the dominant deterrent factors that have the potential to restrain unethical behavior.
Practical implications
The findings demonstrate that the managers of graduate programs should include parental roles in the deterrent mechanism to improve the quality of education.
Originality/value
Very little evidence exists about the perception of academic dishonesty among the students of a multicultural academic institution located in a middle-east country. There are not many studies that analyze the effectiveness of deterrents from a student’s perspective. In this paper, the authors have attempted to assess the value of various deterrents and their effectiveness as perceived by the students.
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Pranab Kumar Pani and Pallavi Kishore
There is growing evidence that learning is faster, measurably better and more productive in a classroom setting when a student attends classes regularly. Each student brings in…
Abstract
Purpose
There is growing evidence that learning is faster, measurably better and more productive in a classroom setting when a student attends classes regularly. Each student brings in his/her experience, skills, and unique learning styles to a class – thus a classroom environment can potentially create positive externalities through which a student can gain substantially from various strengths of his/her peers. However, students do remain absent from their classes for a variety of reasons. One of the measurable effects of regular non-attendance in a university class, where students from various cultures and regions interact, is the academic performance. The purpose of this paper is to determine if there is any potential causal link between absenteeism (attendance) and academic performance.
Design/methodology/approach
Data were culled from the records of three batches of students in a British university campus in the Middle East. Quantile regression methods were used to establish the causal relationship between absenteeism and academic performance.
Findings
A quantile regression analysis reveals that absenteeism has negative impact on academic performance. This also suggests that low performers are worse affected by absenteeism as compared to the high performers.
Research limitations/implications
Inclusion of some other factors, such as study habits, additional hours spent on quantitative modules, student’s ethnicity background, particularly in the context of United Arab Emirates, could have emboldened the robustness of the study. Non-availability or paucity of this information, to some degree, has limited the conclusions of this study.
Originality/value
Proponents of mandatory attendance argue that there is a positive correlation between attendance and performance. But, one very important issue which gets overlooked is who actually benefits more by attending classes – are the shirkers who have a poor attendance record or the ones who are more sincere, more regular, and active participants in a class? This study uses quantile regression analysis to address this issue.
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