Element of Surprise and Its Efficacy in Maintaining Exam Integrity in Online Exams

Abstract

In view of off-site assessments being adopted by Higher Education Institutions (HEI) especially in response to Covid-19, questions can be raised regarding the extent of possible unethical practices employed by students when sitting for their online exams. Such unethical practices could take various forms including working in groups during individual assessments, accessing resources beyond what is allowed, presenting someone else’s work as their own, etc. In this context, we examine the impact of element of surprise in preserving exam integrity when common monitoring protocols or other paid outsourced services cannot be implemented. Using artificial intelligence to compare images, text-pattern and multiple choice question (MCQ) response pattern across students, our results show that introducing surprise by assigning students to random versions of exams can be helpful in thwarting possible malpractices. Though the benefit in this regard for MCQ is limited, the positive impact is more pronounced for image-sharing practices and text-sharing (copy-paste) practices. Our findings also show that active collaboration during online exams tend to take place in pairs.

Keywords: academic integrity; online assessment; textual analysis; image analysis; pattern-matching; linguistic analysis

JEL Classification: D83, I20, O33