Researchers have developed a predictive model using learning analytics to identify first-year computer science students at risk of failing. The model combines traditional academic data with digital markers from learning platforms, such as Moodle interaction logs. A key finding is that weighted academic momentum, a metric derived from assignment scores, is the strongest predictor of failure, especially when combined with engagement on the learning management system. AI
IMPACT This research demonstrates how AI can be used to proactively identify struggling students in educational settings, enabling timely interventions.
RANK_REASON This is a research paper detailing a new methodology and model for predicting student outcomes. [lever_c_demoted from research: ic=1 ai=0.4]
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