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.
Highlights
Software systems to assist learners and support teachers’ tasks in higher education (HE) have evolved in recent years
Receiving additional nudges enlightened learners with information about competencies to acquire in the continuous assessment activities (CAA), skills needed from the previous CAA, and reminders about the submission
We present a nudging intervention mechanism combined with an early warning system (EWS) based on artificial intelligence (AI) techniques
Summary
Software systems to assist learners and support teachers’ tasks in higher education (HE) have evolved in recent years. AI-based systems improve learner success and retention by enabling early detection and support of online learners at risk of failing or dropping out; these are key concerns in online learning (GrauValldosera et al, 2019). To this end, we developed an adaptive intelligent system (called LIS system) with predictive analytics, a progression dashboard, automated nudges, and recommendations based on AI classification algorithms. Our work aimed to develop a nudging intervention mechanism in conjunction with an AI-based system to detect at-risk learners early, and to evaluate the system’s overall impact on learner performance, dropout rates, and student satisfaction
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