Class integration of ChatGPT and learning analytics for higher education

Abstract

Background: Active Learning with AI-tutoring in Higher Education tackles dropoutrates. 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.