Notícies i recerca sobre aprenentatge avançat en tecnologies de la informació

Bloc del grup de recerca GRETEL. Espai de divulgació sobre la recerca en tecnologies i metodologies per millorar l'aprenentatge.

07 November 2024 | Posted by Editorial Team GRETEL

SPADATAS at TEEM 2024: Advanced Technologies to Improve Education

The Technological Ecosystems for Enhancing Multiculturality (TEEM 2024) conference brought together researchers from around the world at the University of Alicante from October 23 to 25, 2024. This event has become an international benchmark for exploring emerging technologies applied to education and cultural diversity. Within this framework, the SPADATAS project presented three innovative studies aimed at improving education through the use of language models, educational quizzes, and tools to reduce university dropout rates.

Track 2: Expressing Educational Content with Extensive Language Models - A Question of Context

Authors: Daniel Amo-Filva, Amaia Pikatza Huerga, Susana Romero-Yesa, Álvaro Sicilia Gómez, Belén Donate-Beby, Eduard Fernández, and David Fonseca
Presenter: Belén Donate-Beby

In this presentation, the SPADATAS team analyzed the use of extensive language models (such as text-generating AI systems) for creating educational content. These models can produce teaching materials that assist educators, but the study focuses on how to personalize these models so that the generated content is relevant and adapted to each educational context. Thus, the opportunities and challenges of using artificial intelligence in the classroom are highlighted, underscoring the importance of adjusting AI to the needs and realities of students.

Track 13: Undergraduates Perceptions on Post-Lecture Quizzes in Microeconomics and Macroeconomics

Authors: Dubravka Novkovic, Josep Petchamé, Ignasi Iriondo, Daniel Amo-Filva, Eulàlia Ribó, Belén Donate-Beby, and Francesc Solanellas
Presenter: Belén Donate-Beby

This study investigates students perceptions in Microeconomics and Macroeconomics regarding quizzes conducted after each class. The research team found that these quizzes help students reinforce what they have learned by allowing them to quickly assess their understanding and identify areas for improvement. Students valued this immediate feedback tool, which also promotes self-management and active learning, becoming a practical method for consolidating knowledge in these subjects.

Track 13: Human vs. Machine Learning - The Best Approach to Early Detect University Dropout Rates

Authors: Sofía Aguayo-Mauri, Belén Donate-Beby, Daniel Amo-Filva, Alba Llauró, David Simón, María Alsina, David Fonseca, Silvia Necchi, Susana Romero-Yesa, Marian Aláez, Jorge Torres Lucas, and María Martínez-Felipe
Presenter: Sofía Aguayo-Mauri

The final presentation by SPADATAS at TEEM 2024 addresses a critical issue in higher education: university dropout. This study compares the use of machine learning algorithms with human experience to predict students' risk of dropping out. While AI models can identify complex risk patterns, human intervention remains essential to understand the emotional and contextual factors behind dropout decisions. This mixed analysis of data and human context allows for designing more effective and personalized intervention strategies to retain students.

The research presented by SPADATAS at TEEM 2024 reflects a continuous commitment to innovation in education, exploring new ways to leverage technology to enrich the learning experience and improve academic outcomes.

The SPADATAS project (Ref.: 2022-1-ES01-KA220-SCH-000086363) is co-financed by the Erasmus+ program of the European Union. The content of this publicacion is the sole responsibility of the consortium and neither the European Commission, nor the Spanish Service for the Internationalization of Education (SEPIE) are responsible for the use that may be made of the information disclosed here.

Share