Predicting key educational outcomes in academic trajectories: a machine-learning approach
- PDF / 932,363 Bytes
- 20 Pages / 439.37 x 666.142 pts Page_size
- 20 Downloads / 164 Views
Predicting key educational outcomes in academic trajectories: a machine-learning approach Mariel F. Musso 1,2
3
& Carlos Felipe Rodríguez Hernández & Eduardo C. Cascallar
3
# The Author(s) 2020
Abstract
Predicting and understanding different key outcomes in a student’s academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches. However, these models assume linear relationships between variables and do not always yield accurate predictive classifications. On the other hand, the use of machine-learning approaches such as artificial neural networks has been very effective in the classification of various educational outcomes, overcoming the limitations of traditional methodological approaches. In this study, multilayer perceptron artificial neural network models, with a backpropagation algorithm, were developed to classify levels of grade point average, academic retention, and degree completion outcomes in a sample of 655 students from a private university. Findings showed a high level of accuracy for all the classifications. Among the predictors, learning strategies had the greatest contribution for the prediction of grade point average. Coping strategies were the best predictors for degree completion, and background information had the largest predictive weight for the identification of students who will drop out or not from the university programs. Keywords Machine learning . Higher education . Prediction . Educational achievement
Introduction Modern societies require a college and/or university degree as a pillar for economic progress and responsible citizenship (Kuh et al. 2008). Nevertheless, students face certain problems during their university studies, so they drop out or take more time in obtaining their degrees (Berkner et al. 2002). Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10734-02000520-7) contains supplementary material, which is available to authorized users.
* Mariel F. Musso [email protected] Extended author information available on the last page of the article
Higher Education
The first year in higher education is probably one of the most important changes in a student’s academic trajectory (Chemers et al. 2001). First-year students are at the greatest risk of dropping out or not achieving acceptable grades (Horstmanshof and Zimitat 2007; Kovacic 2010; Strayhorn 2009). This transition demands high levels of self-regulation, adequate coping strategies to new academic problems and situations, efficient use of the student’s cognitive skills, and the presence of other favorable circumstances in the student’s life in order to make academic success possible (Bryde and Milburn 1990; Kuh, Kinzie, Schuh, Whitt 2005; Strayhorn 2009). A considerable number of studies have reported the predictive role of GPA (as a proxy for overa
Data Loading...
