Identifying and characterizing students suspected of academic dishonesty in SPOCs for credit through learning analytics

Abstract

Massive Open Online Massive Open Online Courses (MOOCs) have been transitioning slowly from being completely open and without clear recognition in universities or industry, to private settings through the emergence of Small and Massive Private Online Courses (SPOCs and MPOCs). Courses in these new formats are often for credit and have clear market value through the acquisition of competencies and skills. However, the endemic issue of academic dishonesty remains lingering and generating untrustworthiness regarding what students did to complete these courses. In this case study, we focus on SPOCs with academic recognition developed at the University of Cauca in Colombia and hosted in their Open edX instance called Selene Unicauca. We have developed a learning analytics algorithm to detect dishonest students based on submission time and exam responses providing as output a number of indicators that can be easily used to identify students. Our results in two SPOCs suggest that 17% of the students that interacted enough with the courses have performed academic dishonest actions, and that 100% of the students that were dishonest passed the courses, compared to 62% for the rest of students. Contrary to what other studies have found, in this study, dishonest students were similarly or even more active with the courseware than the rest, and we hypothesize that these might be working groups taking the course seriously and solving exams together to achieve a higher grade. With MOOC-based degrees and SPOCs for credit becoming the norm in distance learning, we believe that if this issue is not tackled properly, it might endanger the future of the reliability and value of online learning credentials.