Search Results (4)

Motivation is a key predictor of success in second language (L2) acquisition, yet how culture and identity shape it among Latin American learners remains under-examined. This study used a quasi-experimental design with non-equivalent control and experimental groups (n = 100) to test the effect of an eight-week culturally responsive pedagogical intervention on the motivation of intermediate-level English-as-a-second-language learners at a public university in Ecuador, measured with the validated Spanish version of Gardner’s Attitude/Motivation Test Battery (AMTB). An independent-samples t-test revealed a statistically significant difference in post-intervention motivation scores between the experimental group (M = 156.1, SD = 31.2) and the control group (M = 134.5, SD = 28.4), t(98) = 3.91, p = 0.002, Cohen’s d = 0.72. The intervention was associated with a medium-to-large increase in motivation, consistent with sociocultural theory, the socio-educational model, the L2 Motivational Self System, and identity-investment theory; because the control group did not receive an equally novel activity, this finding should be read as preliminary evidence for the cultural/identity component specifically. Implications for culturally sensitive language teaching in diverse Hispanic contexts are derived. Full article

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. Full article

This work presents the development of NOVA, an educational virtual assistant designed for the Parallel Computing course, built using a Retrieval-Augmented Generation (RAG) architecture combined with Large Language Models (LLMs). The assistant operates entirely in Spanish, supporting native-language learning and increasing accessibility for students in Latin American academic settings. It integrates vector and relational databases to provide an interactive, personalized learning experience that supports the understanding of complex technical concepts. Its core functionalities include the automatic generation of questions and answers, quizzes, and practical guides, all tailored to promote autonomous learning. NOVA was deployed in an academic setting at Universidad Politécnica Salesiana. Its modular architecture includes five components: a relational database for logging, a vector database for semantic retrieval, a FastAPI backend for managing logic, a Next.js frontend for user interaction, and an integration server for workflow automation. The system uses the GPT-4o mini model to generate context-aware, pedagogically aligned responses. To evaluate its effectiveness, a test suite of 100 academic tasks was executed—55 question-and-answer prompts, 25 practical guides, and 20 quizzes. NOVA achieved a 92% excellence rating, a 21-second average response time, and 72% retrieval coverage, confirming its potential as a reliable AI-driven tool for enhancing technical education. Full article

Creating effective mechanisms to detect misogyny online automatically represents significant scientific and technological challenges. The complexity of recognizing misogyny through computer models lies in the fact that it is a subtle type of violence, it is not always explicitly aggressive, and it can even hide behind seemingly flattering words, jokes, parodies, and other expressions. Currently, it is even difficult to have an exact figure for the rate of misogynistic comments online because, unlike other types of violence, such as physical violence, these events are not registered by any statistical systems. This research contributes to the development of models for the automatic detection of misogynistic texts in Latin American Spanish and contributes to the design of data augmentation methodologies since the amount of data required for deep learning models is considerable. Full article