Utilisation of Learning Analytics to Identify Students at Risk of Poor Academic Performance in Medical Schools.

Introduction Identifying students at risk of failure before they experience difficulties may considerably improve their outcomes. However, identification techniques can be costly, time-intensive, and of unknown efficacy. Medical educators need accessible and cost-effective ways of identifying at-risk students. The aim of this study was to investigate the relationship between student engagement in an online classroom and academic performance given the transition of many courses from in-person to online learning.  Methods A retrospective study was conducted on a group of 235 students from the University of Edinburgh Bachelor of Medicine and Surgery (MBChB) in Year One for eight weeks from the start of term, September 2020. Purposive sampling was used. Data were collected on total test submissions, total discussion board submissions, engagement scores, and overall exam scores. Learning analytics on discussion board engagement were collected for new medical students before they had sat any summative assessment. Tests completed, discussion board posts made, and their total engagement score were correlated with their first summative assessment scores at the end of semester one. Results We found a statistically significant correlation between total test submissions, total discussion board submissions, engagement scores, and overall exam scores, with small-medium effects (r = 0.281, p<0.001) (r = 0.241, p<0.001), and (r = 0.202, p<0.001). Students with more test submissions, total discussion board submissions, and total engagement had a higher overall exam score. There was a statistically significant moderate correlation between total submissions and overall exam scores (r = 0.324, p<0.001). Conclusions Students who had a higher number of submissions were more likely to perform better on assessments. Early engagement correlates with performance. Learning analytics can help identify student underperformance before they undertake any assessment, and this can be done very inexpensively and with minimal staff resources if properly planned.

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Human subjects: Consent was obtained or waived by all participants in this study. University of Edinburgh Medical Education Ethics Committee issued approval (2020/33). Students involved in the study have consented to the routine evaluation of their data. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.