An early warning system to identify and intervene online dropout learners

• Published in: International Journal of Educational Technology in Higher Education 2023-12, Vol.20 (1), p.3-25, Article 3
• Main Authors: Bañeres, David, Rodríguez-González, M. Elena, Guerrero-Roldán, Ana-Elena, Cortadas, Pau
• Format: Article
• Language: English
• Subjects: Academic Failure; Artificial Intelligence; CAI; Computer Appl. in Social and Behavioral Sciences; Computer assisted instruction; Computer Science; Computers and Education; Distance learning; Dropout; Dropout Characteristics; Dropout Prevention; Early warning system; Educational Technology; Electronic Learning; Foreign Countries; Goal Orientation; Higher Education; Humanities; Identification; Information Systems Applications (incl.Internet); Intervention; Law; Online Courses; Online learning; Prediction models; Research Article; Risk levels; School dropouts; Statistics for Social Sciences; Teachers
• ISSN: 2365-9440; 2365-9440
• DOI: 10.1186/s41239-022-00371-5

Dropout is one of the major problems online higher education faces. Early identification of the dropout risk level and an intervention mechanism to revert the potential risk have been proved as the key answers to solving the challenge. Predictive modeling has been extensively studied on course dropout. However, intervention practices are scarce, sometimes mixed with mechanisms focused on course failure, and commonly focused on limited interventions driven mainly by teachers' experience. This work contributes with a novel approach for identifying course dropout based on a dynamic time interval and intervening, focusing on avoiding dropout at the assessable activity level. Moreover, the system can recommend the best interval for a course and assessable activity based on artificial intelligence techniques to help teachers in this challenging task. The system has been tested on a fully online first-year course with 581 participants from 957 enrolled learners of different degrees from the Faculty of Economics and Business at the Universitat Oberta de Catalunya. Results confirm that interventions aimed at goal setting on the ongoing assessable activity significantly reduce dropout issues and increase engagement within the course. Additionally, the work explores the differences between identification mechanisms for course dropout and failure aiming to distinguish them as different problems that learners may face.