An intelligent nudging system to guide online learners

Abstract

This work discusses a nudging intervention mechanism combined with an artificial intelligence (AI) system for early detection of learners risk of failing or dropping out. Different types of personalized nudges were designed according to educational principles and the learners risk classification. The impact on learners performance, dropout reduction, and satisfaction was evaluated through a study with 252 learners in a first-year course at a fully online university. Different learners groups were designed, with each receiving a different set of nudges. Results showed that nudges positively impacted the learners performance and satisfaction, and reduced dropout rates. The impact significantly increased when different types of nudges were provided. Our research reinforced the role of AI as useful in online, distance, and open learning for providing timely learner support, improved learning experiences, and enhanced learner-teacher communication.