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arXiv Machine Learning By Lighton Phiri, Mutune Chaibela, Ivy Chisha, David Pungwa, Danny Siabbaba, Bydon Simukoko

Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs

Read the original on arXiv Machine Learning →

The study explores whether combining traditional and digital learning analytics can predict failure in a first‑year CS1 course. Using data from 284 students across four cohorts, the authors identified ten candidate factors and built a logistic regression model that achieved 74.7% accuracy and 0.742 macro F1, with 87% recall for failing students. Weighted academic momentum, basic demographics, and LMS activity emerged as the most predictive features, suggesting that simple digital markers can enable early‑warning systems by week five.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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