Predicting Key Educational Outcomes in Academic Trajectories: A Machine-Learning Approach
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| Title: | Predicting Key Educational Outcomes in Academic Trajectories: A Machine-Learning Approach |
|---|---|
| Language: | English |
| Authors: | Musso, Mariel F. (ORCID |
| Source: | Higher Education: The International Journal of Higher Education Research. Nov 2020 80(5):875-894. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 20 |
| Publication Date: | 2020 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Classification, Prediction, Artificial Intelligence, College Students, Private Colleges, Accuracy, Outcomes of Education, Grade Point Average, School Holding Power, Graduation, Predictor Variables, Dropout Characteristics, Learning Strategies, Coping |
| DOI: | 10.1007/s10734-020-00520-7 |
| ISSN: | 0018-1560 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | EJ1269232 |
| Database: | ERIC |
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| 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. |
|---|---|
| ISSN: | 0018-1560 |
| DOI: | 10.1007/s10734-020-00520-7 |
