A Machine Learning Approach to Dropout Early Warning System Modeling. International

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dc.contributor.authorIsiaka R.M., Babatunde R. S., Ajao F.J. and Abdulsalam S.O. (2019)
dc.date.accessioned2024-10-21T14:28:55Z
dc.date.available2024-10-21T14:28:55Z
dc.date.issued2019-10-18
dc.description.abstractDropout has been identified as a social problem with an immediate and long term consequences on the socioeconomic development of the society. A major element of a conceptual framework for dropout monitoring and control system is the prevention component. The component specified the need for automatic dropout early warning system. This paper presents the procedure for building the adaptive model, that is the core element for the realization of the prevention component of the framework. The model development is guided by the knowledge of the domain experts. An experimental approach was used to identify the K-Nearest Neighbors (KNN) as the best of the six algorithms considered for the adaptive models. Other algorithms explored are Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). Historical dataset from a State University in the North Central region of Nigeria was used for building and testing the adaptive model. The average performance of the model on full attributes and the first year academic attributes compared favourably with 0.98 Precision, Recall and F1-Scores. Conversely, the Precision, Recall and F1-Scores on the entry attributes are 0.90, 0.95 and 0.92 respectively. The implication of this is that the academic performance is a very significant factor for dropping out of the educational system. The developed model can provide an early signal on the students with the propensity to dropout, thereby serving as an advisory system for the students, parents and school management towards curtailing the menace.
dc.identifier.citationIsiaka R.M., Babatunde R. S., Ajao F.J. and Abdulsalam S.O. (2019)
dc.identifier.issnhttps://journals.indexcopernicus.com/search/article?articleId=2495806 ISSN : 2278 7917
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/2489
dc.language.isoen
dc.publisherJournal of Advanced Studies In Computer Science And Engineering
dc.titleA Machine Learning Approach to Dropout Early Warning System Modeling. International
dc.typeArticle
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