Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:32:47am America, Santiago
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Daily Overview |
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3A: Computer Science Virtual location: VIRTUAL: Agora Meetings | |
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12:12pm - 12:20pm
Comparison of Boosting Machine Learning Models for Classifying Students in Blended Learning Programs in Higher Education During The Period 2020–2024 Servicio Nacional de Adiestramiento en Trabajo Industrial (SENATI), Perú This paper presents a comparative analysis of the performance of four supervised machine learning algorithms based on Boosting models: XGBoost, LightGBM, CatBoost, and Gradient Boosting Machine. The objective is to classify students according to their enrollment modality: face-to-face or blended learning at a National University of Education in Peru during the period 2020–2024. A dataset from the Peruvian government's national open data platform was used, which includes academic and demographic information about students. The four machine learning models were then trained using the stratified cross-validation technique to ensure that each class was adequately represented. To analyze the performance of the evaluated machine learning models, the following evaluation metrics were used: Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC). Of all the models evaluated, the CATBOOST algorithm stood out for offering a balance between precision and efficiency. These final findings highlight the importance and usefulness of implementing automated systems that support enrollment planning and management in both face-to-face and blended learning modalities in Peruvian higher education. | |