RT Journal Article
T1 Systematic literature review of predictive analysis tools in higher education
A1 Liz Domínguez, Martín
A1 Caeiro Rodríguez, Manuel
A1 Llamas Nistal, Martín
A1 Mikic Fonte, Fernando Ariel
K1 5801.07 Métodos Pedagógicos
K1 1203.04 Inteligencia Artificial
K1 3307 Tecnología Electrónica
AB 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.
PB Applied Sciences
SN 20763417
YR 2019
FD 2019-12-17
LK http://hdl.handle.net/11093/1579
UL http://hdl.handle.net/11093/1579
LA eng
NO Applied Sciences, 9(24): 5569 (2019)
NO Departamento de Educación da Xunta de Galicia | Ref. (TIN2016-80515-R AEI / EFRD, UE)
DS Investigo
RD 04-dic-2023