2017/04/19 by Mohammed Saqr, Uno Fors, Matti Tedre · 186 citations
Computer Science · Medicine · Psychology · #Analytics #Artificial intelligence #Big data #Blended learning #Computer science #Consistency (knowledge bases) #Data mining #Data science #Educational technology #Formative assessment #Identification (biology) #Intelligent Tutoring Systems and Adaptive Learning #Learning Management #Learning Styles and Cognitive Differences #Learning analytics #Mathematics education #Medical education #Medicine #Multimedia #Online Learning and Analytics #Psychology
paper · doi:10.1080/0142159x.2017.1309376
published in Medical Teacher 39(7), 757-767 (Taylor & Francis)
openalex publication_date 2017/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
AIM: Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. METHODS: This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. RESULTS: At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. CONCLUSIONS: The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.