| Sumario: | Background: Small private online courses (SPOCs) are one of the strategies to introduce the massive open online courses (MOOCs) within the university environment and to have these courses validates for academic credit. However, numerous researchers have highlighted that academic dishonesty is greatly facilitated by the online context in which SPOCs are offered. And while numerous algorithms have already been proposed, no research has been performed on how to transfer this information to instructors, so that they can intervene and decrease the prevalence of this issue. Objectives: In this article, we present a qualitative evaluation of a tool for detecting and monitoring students suspected of academic dishonesty in SPOCs in Selene, a Colombian instance of Open edX. Methods: The evaluation was carried out through semi‐structured interviews with four instructors who taught SPOCs with academic recognition at the University of Cauca. Results: The evaluation results indicated that participants found the dashboard reliable and appropriate to detect academic dishonesty behaviours in order to intervene in these cases. Implications: But interventions are difficult to systematise, need an institutional policy, and there is uncertainty about whether these interventions can actually contribute to decreasing academic dishonesty. Lay Description: What is currently known about the subject matter: There are studies that detect academic dishonesty behaviour through webcams in massive open online courses (MOOCs).There are studies based on Learning Analytics techniques for the detection of academic dishonesty behaviour in MOOCs.There are dashboards for monitoring students' learning activities in online courses. What their paper adds to this: An algorithm for real‐time detection of academic dishonesty behaviour in small private online courses (SPOCs) with academic recognition is presented.A dashboard for the visualisation of students' academic dishonesty behaviour in SPOCs is presented.A qualitative evaluation of a dashboard for the detection of academic dishonesty in SPOCs with academic recognition is presented. Implications of study findings for practitioners: Our work opens an avenue for exploration of different ways to detect and follow up on academic dishonesty in online courses.Implies that trainers should have a basic knowledge of data analysis.
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