What Category Are They Anyway?: Proposing a New Taxonomy for Factors that May Influence Students' Likelihood to E-Cheat

What Category Are They Anyway?: Proposing a New Taxonomy for Factors that May Influence Students' Likelihood to E-Cheat

Zeenath Reza Khan (University of Wollongong – Dubai, UAE)
Copyright: © 2017 |Pages: 28
DOI: 10.4018/978-1-5225-1610-1.ch006
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Abstract

As the world of academia grapples with the scandals shrouding some of the oldest and prestigious universities around the globe, it is becoming clear that a better understanding of the problem of academic dishonesty is the need of the hour. This chapter paves the path towards providing such an understanding by proposing first a new taxonomy of possible factors that may influence students' attitude towards academic dishonesty. First, this chapter provides a review of existing literature to propose a definition of e-cheating, highlighting the need to study not only traditional forms of cheating and academic dishonesty, but also the act as transformed by the digital age. This work posits that the underlying flaw in approaches to battling the issue of academic dishonesty lies in trying to curb it, rather than understanding ‘why' students are likely to indulge in such behavior. This chapter develops and validates a comprehensive factor-model to identify factors that may influence students' likelihood to e-cheat by first critically reviewing existing classifications and then proposing a new model of factors, including possible technological factors.
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Introduction

I’m for truth no matter who tells it. I’m for justice no matter who it is for or against. I’m a human being first and foremost, and as such I am for whoever and whatever benefits humanity as a whole. Malcolm X

Cheating, by any means, is not a new phenomenon. In formal education settings, students seem to be getting progressively smarter, more conniving, and more hands-on with new technologies that come their way to enable cheating to be faster, easier and more cost-effective. Annually, Higher Education Institutions (HEIs) highlight their concerns over the growing number of cheating cases. In 2012, Harvard University announced 125 cases of cheating in final examinations of a course titled Introduction to Congress (Harrington, 2012). Educators around the world have tried to come up with new techniques to combat cheating in and out of their classrooms. Schools and universities spend a proportion of their annual budget by either buying products or software which the developers and marketers promise to help curb cheating. These products are also advertised to train faculty, restructure curricula to help prevent cheating behaviors in their institutions in the hope that this may increase AI and potentially increase annual revenue or world-rankings. As some universities are using technology to combat and reduce cheating, online websites are using technologies to facilitate cheating by selling research papers and assignments to students, The Herald and Fairfax investigation in 2014 exposed an online business that provided more than 900 assignments to students from almost every university of New South Wales (NSW) in Australia, putting Australian education’s reputation at risk (Harrison, 2014).

But why do students cheat? It is believed that the answer to this question is the key to understanding the phenomenon of why students cheat or e-cheat. It is also believed that understanding why students cheat or e-cheat can help in the development of tools and techniques that will aid teachers to develop policies, introduce them and improve teaching tools that will help reduce cheating and e-cheating. To this effect, it is believed that a better conceptual model of the factors that leads to e-cheating should be developed, given the increasing focus on technology in learning environments. In order to do this, it is necessary to produce a comprehensive list of factors which can be used to develop and then validate the factor-model. Research suggests that, before producing such a list, it is imperative that an ordered set of related categories be produced, which can be used to group data according to their similarities (CSO, 2014).

This chapter aims to produce a comprehensive review of existing literature on Higher Education (HE), cheating and e-cheating, and propose a taxonomy of factors that will pave the way for producing a comprehensive list of factors which can be used to scientifically develop and validate a factor model that will be of benefit to educational leaders, teachers, parents and policy-makers globally in understanding students’ likelihood to e-cheat.

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