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Article

An Early Warning System to Detect At-Risk Students in Online Higher Education

1
eLearn Center, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
2
Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
3
Open Education Faculty, Yunus Emre Campus, Anadolu University, 26470 Eskisehir, Turkey
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4427; https://doi.org/10.3390/app10134427
Received: 30 May 2020 / Revised: 25 June 2020 / Accepted: 25 June 2020 / Published: 27 June 2020
Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students. View Full-Text
Keywords: early warning system; artificial intelligence; predictive models; personalized feedback; online learning early warning system; artificial intelligence; predictive models; personalized feedback; online learning
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MDPI and ACS Style

Bañeres, D.; Rodríguez, M.E.; Guerrero-Roldán, A.E.; Karadeniz, A. An Early Warning System to Detect At-Risk Students in Online Higher Education. Appl. Sci. 2020, 10, 4427. https://doi.org/10.3390/app10134427

AMA Style

Bañeres D, Rodríguez ME, Guerrero-Roldán AE, Karadeniz A. An Early Warning System to Detect At-Risk Students in Online Higher Education. Applied Sciences. 2020; 10(13):4427. https://doi.org/10.3390/app10134427

Chicago/Turabian Style

Bañeres, David, M. E. Rodríguez, Ana E. Guerrero-Roldán, and Abdulkadir Karadeniz. 2020. "An Early Warning System to Detect At-Risk Students in Online Higher Education" Applied Sciences 10, no. 13: 4427. https://doi.org/10.3390/app10134427

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