Machine learning models for the early detection of risk factors in the academic performance of university students using socio-technological variables
DOI:
https://doi.org/10.55204/trc.v6i2.e754Keywords:
machine learning, academic risk, socio-technological variables, early detection, LMS, ensemble methods, PRISMAAbstract
This study examines machine learning models applied to the early detection of risk factors in university students' academic performance, with particular emphasis on integrating socio-technological variables derived from Learning Management System (LMS) platforms. A systematic literature review was conducted following the PRISMA 2020 protocol, analyzing 40 studies published between 2020 and 2026 retrieved from international and Latin American databases. The main findings indicate that ensemble methods, namely Random Forest, XGBoost, and Gradient Boosting, represent the current state of the art in academic risk prediction, achieving accuracy values between 85% and 93% with an area under the curve (AUC-ROC) exceeding 0.90. Furthermore, the incorporation of digital behavior variables — LMS access frequency, session duration, forum participation, and assignment submission punctuality was found to increase the predictive power of models by 7 to 14 percentage points compared to the exclusive use of historical grades. A hybrid architecture integrating supervised predictive models with reinforcement learning agents is proposed for generating personalized pedagogical interventions in real time within institutional infrastructure.Downloads
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