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AI model predicts CS1 student failure using learning analytics

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model predicts CS1 student failure using learning analytics

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · 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

    arXiv:2608.16914v1 Announce Type: cross Abstract: Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early. This paper investigates whether a combination of traditional and digital markers can pre…

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