Copying@Scale: Using Harvesting Accounts for Collecting Correct Answers in a MOOC

Abstract

This paper presents a detailed study of a form of academic dishonesty that involves the use of multiple accounts for harvesting solutions in a Massive Open Online Course (MOOC). It is termed CAMEO -Copying Answers using Multiple Existence Online. A person using CAMEO sets up one or more harvesting accounts for collecting correct answers; these are then submitted in the user's master account for credit. The study has three main goals: Determining the prevalence of CAMEO, studying its detailed characteristics, and inferring the motivation(s) for using it. For the physics course that we studied, about 10% of the certificate earners used this method to obtain more than 1% of their correct answers, and more than 3% of the certificate earners used it to obtain the majority (>50%) of their correct answers. We discuss two of the likely consequences of CAMEO: jeopardizing the value of MOOC certificates as academic credentials, and generating misleading conclusions in educational research. Based on our study, we suggest methods for reducing CAMEO. Although this study was conducted on a MOOC, CAMEO can be used in any learning environment that enables students to have multiple accounts.

Evaluating the Robustness of Learning Analytics Results Against Fake Learners

“…The data in fake learners logs is largely an artifact with respect to explaining their performance. As was pointed out in [4], this can bias LA and EDM results. For example, fake learners typically make minimal interaction with the learning materials, yet show high success; this can lead to false conclusions regarding the effectiveness of different learning paths or the pedagogic efficacy of course materials.…”

“…Fake Learners. However, several studies revealed that there are a considerable amount of users who use cynical means to succeed in the courses, such as Cheating Using Multiple Accounts [3,18,4,15], or unauthorized collaboration [19]. Such users break the 'genuine learning behavior' assumption, thus we refer to them as fake learners.…”

“…To answer these, we challenge the findings reported in one of the first studies of pedagogic efficacy in MOOCs [7], by replicating its results with and without fake learners data. To identify the fake learners, we use the algorithms published in [4,19].…”

Student success prediction in MOOCs

“…The predictive modeling research evaluated in this work is situated in the context of a much larger and broader body of MOOC-related research. Prior research on MOOCs has covered a broad variety of topics, including changes in learner discourse over time (Dowell et al, 2017), interventions to improve student completion (Kizilcec and Cohen, 2017), demographics and participation rates and the relationship to course activity (Guo and Reinecke, 2014), and student plagiarism and academic honesty issues (Alexandron et al, 2017). Additionally, the researchers addressing this topic, both in the predictive context and more broadly, come from a wide variety of academic perspectives, including learning theory, social and experimental psychology, computer science, statistics, economics, design, and linguistics.…”

HCI expertise needed! Personalisation and feedback optimisation in online education

“…Visual tools (e.g., emoticons, colour, visual imagery and similar, see Dixson et al, 2016) may support self-reflection, and also engagement with, feedback on activities (see also Govaerts et al, 2010). HCI experts may be able to implement notifications (e.g., D'Antoni et al, 2015) and actual real-time detection methods to track attempts at manipulation (e.g., in online tests; see also Alexandron et al, 2017). Sharing such information with online students may increase their utilisation of feedback and foster actual learning, while reducing attempts to game the online system.…”