First-Month LMS Behaviours Associated with Success in an AI-Permissive Software Engineering Course

Abstract

This extended abstract profiles the highest-performing behavioural cluster during the first milestone of a Software Engineering course governed by an explicit artificial-intelligence (AI) usage policy. Moodle traces from two post-AI cohorts (n = 146) yielded three clusters: an integrator group (n = 32), an intermediate group (n = 73), and an at-risk group (n = 41). Integrators achieved a higher mean milestone grade than at-risk students (6.37 versus 4.81; Hedges’ g = 0.92) and a higher pass rate (71.9% versus 44.0%). Their profile combined access volume and anticipation: they accessed theoretical materials, task specifications, rubrics, and modelling tools more often, and reached theoretical materials about ten days earlier. A logistic model based on theoretical-material volume, task volume, and task first-access timing separated integrators from the remaining students with an ROC AUC of 0.86. The results support first-month onboarding that introduces rubrics and modelling tools early, triggers early task access, uses analytics for positive recognition, and treats forum activity as a complement rather than a substitute for task- and rubric-anchored engagement.