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Rodrigues, D., Lopes, A. L., Mauritti, R, Roque Ferreira, M. & Pintassilgo, S. (2025). Early Detection of At-Risk University Students Using Machine Learning: a Study of Model Performance in Evolving Academic Environments. In EDULEARN 2025. (pp. 6538-6545).

D. M. Rodrigues et al.,  "Early Detection of At-Risk University Students Using Machine Learning: a Study of Model Performance in Evolving Academic Environments", in EDULEARN 2025, 2025, pp. 6538-6545

@inproceedings{rodrigues2025_1765119395197,
	author = "Rodrigues, D. and Lopes, A. L. and Mauritti, R and Roque Ferreira, M. and Pintassilgo, S.",
	title = "Early Detection of At-Risk University Students Using Machine Learning: a Study of Model Performance in Evolving Academic Environments",
	booktitle = "EDULEARN 2025",
	year = "2025",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.21125/edulearn.2025.1610",
	pages = "6538-6545",
	publisher = "",
	address = "",
	organization = "",
	url = "https://iated.org/edulearn"
}

TY  - CPAPER
TI  - Early Detection of At-Risk University Students Using Machine Learning: a Study of Model Performance in Evolving Academic Environments
T2  - EDULEARN 2025
AU  - Rodrigues, D.
AU  - Lopes, A. L.
AU  - Mauritti, R
AU  - Roque Ferreira, M.
AU  - Pintassilgo, S.
PY  - 2025
SP  - 6538-6545
SN  - 2340-1117
DO  - 10.21125/edulearn.2025.1610
UR  - https://iated.org/edulearn
AB  - This study explores the early detection of at-risk students using machine learning (ML) models trained on historical academic data. We constructed two datasets from university enrollment records spanning the 2016/2017 to 2022/2023 academic years, focusing on first-time enrollees in their first curricular year. The first dataset included only pre-enrollment information available at the start of the academic year, while the second incorporated first-semester performance data. Each dataset was used to predict two target outcomes: academic success and dropout. We evaluated multiple ML models, including Random Forest, Support Vector Machine (SVM), Naïve Bayes, Decision Tree, AdaBoost, K-Nearest Neighbors (KNN), and Logistic Regression. To optimize model performance, we employed Optuna for hyperparameter tuning, conducting hundreds of trials per algorithm. The best-performing models were tested on two datasets: one from historical student data (2016/2017–2022/2023) and another from the 2023/2024 academic year, reflecting a real-world shift due to a newly implemented university intervention strategy aimed at providing personalized support for at-risk students. Although the intervention itself was not included as a model variable, it may have influenced student success and dropout rates in 2023/2024, potentially impacting model predictions. By comparing performance on pre- and post-intervention data, this study assesses the robustness and generalizability of ML-based early warning systems in dynamically evolving academic environments.
ER  -