A Context-Aware Framework for Sentiment Analysis of Student Feedback to Inform Educational Strategies in Latin America

Bibliographic Data

ID22044726
AuthorsAnabel Pineda-Briseño (0000-0002-5296-4210, Instituto Tecnológico de Matamoros, corresponding author), Jimy Oblitas (0000-0001-7652-6672, National University of Cajamarca), Laura Cleofas Sánchez (0000-0002-6666-0461, Centro de Estudios Superiores Universitarios), Wendy Sanchez (0000-0002-8576-5621, Instituto Tecnológico de Matamoros), Rosario Baltazar (0000-0002-8847-8732, Instituto Tecnológico de León)
Year2026
Volume16
Issue3
Pages399
Publication date2026-03-05
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci16030399
OpenAlexW7133724412
LanguageEN
References cited35

Understanding student feedback is essential for informing pedagogical strategies and institutional decision-making in higher education. Sentiment analysis offers scalable mechanisms for extracting insights from open-ended student evaluations; however, many existing approaches prioritize technical performance without sufficient consideration of contextual and institutional constraints, particularly in underrepresented regions. This study proposes a context-aware framework for sentiment analysis of student feedback, designed to support educational decision-making within Latin American universities. Rather than introducing new algorithms, the framework systematically evaluates established machine learning and deep learning models through a multi-phase process that includes data preprocessing, Bayesian optimization, threshold calibration, and class balancing. The framework is validated using authentic Spanish-language student feedback collected from a public university in Peru. Experimental results indicate that while advanced models can achieve strong predictive performance, simpler and more interpretable approaches often provide comparable institutional value when deployment feasibility, computational efficiency, and transparency are considered. These findings highlight that marginal performance gains do not necessarily translate into meaningful advantages for routine educational use. Overall, this work contributes a replicable and resource-sensitive framework that bridges learning analytics research and practical educational application. By prioritizing contextual suitability and interpretability, the proposed approach enables higher education institutions to leverage student sentiment data as an actionable input for continuous improvement and evidence-based educational strategies

Educational Data Mining · Higher education · Latin Americans · Learning analytics · Sentiment analysis · Software deployment · E-Learning and Knowledge Management · Online Learning and Analytics · Sentiment Analysis and Opinion Mining

Tools

Open DOIOpen Access