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Predicting key educational outcomes in academic trajectories: a machine-learning approach
- Source :
- CONICET Digital (CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas, instacron:CONICET
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 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. Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi". Grupo Vinculado CIIPME - Entre Ríos - Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi"; Argentina Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; Bélgica Fil: Cascallar, Eduardo C.. Katholikie Universiteit Leuven; Bélgica
- Subjects :
- Coping (psychology)
Higher education
PREDICTION
Computer science
Sample (statistics)
Machine learning
computer.software_genre
050105 experimental psychology
Education
CIENCIAS SOCIALES
0501 psychology and cognitive sciences
purl.org/becyt/ford/5.1 [https]
HIGHER EDUCATION
purl.org/becyt/ford/5 [https]
Artificial neural network
business.industry
05 social sciences
050301 education
Psicología
Backpropagation
Identification (information)
Multilayer perceptron
Artificial intelligence
MACHINE LEARNING
EDUCATIONAL ACHIEVEMENT
business
0503 education
computer
Graduation
Subjects
Details
- ISSN :
- 1573174X and 00181560
- Volume :
- 80
- Database :
- OpenAIRE
- Journal :
- Higher Education
- Accession number :
- edsair.doi.dedup.....2fc49fda0a1412d4dfc973c42f3789d0