An Early Warning System to Identify and Intervene Online Dropout Learners

Autor/inn/en	Bañeres, David; Rodríguez-González, M. Elena; Guerrero-Roldán, Ana-Elena; Cortadas, Pau
Titel	An Early Warning System to Identify and Intervene Online Dropout Learners
Quelle	In: International Journal of Educational Technology in Higher Education, 20 (2023), Artikel 3 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bañeres, David)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41239-022-00371-5
Schlagwörter	Dropout Characteristics; Dropout Prevention; Identification; Intervention; Electronic Learning; Online Courses; Artificial Intelligence; Foreign Countries; Goal Orientation; Spain + Suchen Sie Ihr Suchwort? Identifikation; Identifizierung; Online course; Online-Kurs; Künstliche Intelligenz; Ausland; Zielorientierung; Zielvorstellung; Spanien
Abstract	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. (As Provided).
Anmerkungen	BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www.springer.com/gp/biomedical-sciences
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01