Class integration of ChatGPT and learning analytics for higher education

Abstract

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.