Harnessing machine learning models and explainable AI to understand MOOC continuance intention

Purpose This study aims to investigate factors that influence individuals’ continuance intention to use massive open online courses (MOOCs) by using machine learning models. Design/methodology/approach Data was collected from 702 MOOC users from major metropolitan cities in India through a network-based sampling and recruitment via various social media outlets (e.g. LinkedIn and Facebook). Various machine learning algorithms along with explainable artificial intelligence (XAI) were employed using Python PyCaret. Findings Results confirm that pedagogy value, content value, interface value, ubiquity value, teacher presence and learning satisfaction have positive effects on the continuous intention of MOOCs. Furthermore, pedagogy value is a chief driving force of CI of MOOCs. XAI helps clarify intricate patterns in learner data, thus allowing more appropriate interventions. Practical implications The findings of the study would be useful for MOOC developers to formulate better value propositions for ensuring a sustainable business and higher growth rate in the market. Originality/value This study bridges the gap in the existing literature by providing a novel approach. To the best of the authors’ knowledge, this is the first study using machine learning and XAI earlier in identifying factors leading to continuous intentions of MOOCs, so this research adds value to the existing method of exploring factors enhancing retention rates among learners.