An early warning system to identify and intervene online dropout learners
- Autores: David Bañeres, M. Elena Rodríguez González, Ana Elena Guerrero Roldán, Pau Cortadas Guasch
- Localización: International Journal of Educational Technology in Higher Education, ISSN 2365-9440, Nº. 20, 2023
- Idioma: inglés
- Enlaces
- Dialnet Métricas: 2 Citas
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Resumen
Dropout is one of the major problems online higher education faces. Early identifca‑ tion 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 con‑ tributes 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 artifcial intelligence techniques to help teachers in this challenging task. The system has been tested on a fully online frst-year course with 581 partici‑ pants from 957 enrolled learners of diferent degrees from the Faculty of Economics and Business at the Universitat Oberta de Catalunya. Results confrm that interventions aimed at goal setting on the ongoing assessable activity signifcantly reduce dropout issues and increase engagement within the course. Additionally, the work explores the diferences between identifcation mechanisms for course dropout and failure aiming to distinguish them as diferent problems that learners may face.
