Class integration of ChatGPT and learning analytics for higher education

Abstract

AbstractBackgroundActive Learning with AI‐tutoring in Higher Education tackles dropout rates.ObjectivesTo 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.MethodsComparative 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.FindingsCriteria 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.ConclusionsProposed 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.