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Systematic Literature Review of Predictive Analysis Tools in Higher Education

Telematic Systems Engineering Group, atlanTTic Research Center, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
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This paper is an extended version of our paper published in Learning Analytics Summer Institute (LASI) Spain 2019.
Appl. Sci. 2019, 9(24), 5569; https://doi.org/10.3390/app9245569
Received: 21 November 2019 / Revised: 12 December 2019 / Accepted: 13 December 2019 / Published: 17 December 2019
(This article belongs to the Special Issue Smart Learning)
The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task. View Full-Text
Keywords: predictive analytics; early warning systems; learning analytics; learning technologies predictive analytics; early warning systems; learning analytics; learning technologies
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MDPI and ACS Style

Liz-Domínguez, M.; Caeiro-Rodríguez, M.; Llamas-Nistal, M.; Mikic-Fonte, F.A. Systematic Literature Review of Predictive Analysis Tools in Higher Education. Appl. Sci. 2019, 9, 5569. https://doi.org/10.3390/app9245569

AMA Style

Liz-Domínguez M, Caeiro-Rodríguez M, Llamas-Nistal M, Mikic-Fonte FA. Systematic Literature Review of Predictive Analysis Tools in Higher Education. Applied Sciences. 2019; 9(24):5569. https://doi.org/10.3390/app9245569

Chicago/Turabian Style

Liz-Domínguez, Martín; Caeiro-Rodríguez, Manuel; Llamas-Nistal, Martín; Mikic-Fonte, Fernando A. 2019. "Systematic Literature Review of Predictive Analysis Tools in Higher Education" Appl. Sci. 9, no. 24: 5569. https://doi.org/10.3390/app9245569

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