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.

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

“…Additionally, the minimum marks necessary for the following assessment to receive a pass grade in the course are also shown 77,79,80 . Interventions are automatically provided through email when at‐risk students have been identified 81 . The EWS and interventions are evaluated 77,79–81 in a variety of courses at UOC.…”

“…77,79,80 Interventions are automatically provided through email when at-risk students have been identified. 81 The EWS and interventions are evaluated 77,[79][80][81] in a variety of courses at UOC. The results are encouraging, showing higher pass rates, lower dropout rates, and positive student and instructor acceptance.…”

You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education

“…In this scenario, automated student performance monitoring has been indispensable in qualifying and quantifying individual learning [115]. Based on profile and prediction analyses, educators and universities, in general, may deploy nudging intervention systems, control drop-out and retention rates, monitor student trajectories, and guide them to academic success [117]. Moreover, using machine learning algorithms, it is possible to predict the use of learning resources, adoption behavior, and integration in the educational process [62,104], and systematize the academic evaluation process [89,146].…”

“…Notably, Raza et al [115] designed a time-series predictive model through a long short-term memory network based on information obtained from previous online course interactions and outcomes to predict student progress in the future. The other two papers coded in Computer sciences proposed implementing early warning systems [59] and creating an intelligent nudging system [117]. Two publications were based on monitoring at risks-students in the Health sciences and Behavioral sciences groups [78,118].…”