The Feasibility of Utilizing ChatGPT in Learning Analytics for the Identification of At-Risk Students

The value-added applications of ChatGPT occur in many fields. Cooperation with ChatGPT has gradually become inevitable. This study aims to explore the potential of ChatGPT in the field of learning analytics, with a specific focus on predicting risk students while tackling prevalent challenges in learning analytics. Traditionally, learning analytics classification tasks have relied on machine learning models, leading to issues related to model interpretability and tailor learning suggestion generation. By utilizing the LBLS467 learning behavior dataset, experimental findings with ChatGPT-4 reveal its potential as a fundamental and accessible tool. While occasional performance variations are noted, ChatGPT holds promise as an alternative approach for basic at-risk student prediction within learning analytics. This study paves the way for further exploration of ChatGPT's potential in enhancing student support mechanisms and improving educational outcomes.