Civit M. , Escalona M.J. , CUADRADO MÉNDEZ, FRANCISCO JOSÉ, REYES DE CÓZAR, SALVADOR
No
Expert Syst.
Article
Científica
21/08/2024
001295572700001
2-s2.0-85201723482
Background: Active Learning with AI-tutoring in Higher Education tackles dropout rates. Objectives: To investigate teaching-learning methodologies preferred by students. AHP is used to evaluate a ChatGPT-based studented learning methodology which is compared to another active learning methodology and a traditional methodology. Study with Learning Analytics to evaluate alternatives, and help students elect the best strategies according to their preferences. Methods: Comparative study of three learning methodologies in a counterbalanced Single-Group with 33 university students. It follows a pre-test/post-test approach using AHP and SAM. HRV and GSR used for the estimation of emotional states. Findings: Criteria related to in-class experiences valued higher than test-related criteria. Chat-GPT integration was well regarded compared to well-established methodologies. Student emotion self-assessment correlated with physiological measures, validating used Learning Analytics. Conclusions: Proposed model AI-Tutoring classroom integration functions effectively at increasing engagement and avoiding false information. AHP with the physiological measuring allows students to determine preferred learning methodologies, avoiding biases, and acknowledging minority groups. © 2024 The Author(s). Expert Systems published by John Wiley & Sons Ltd.
Active learning; Adversarial machine learning; Collaborative learning; Expert systems; Federated learning; Active Learning; Application in education; Comparatives studies; Cooperative/ collaborative learning; Data science application in education; High educations; Postsecondary education; Science applications; Teaching-learning; Teaching/learning strategy; Contrastive Learning