A Machine Learning Approach to Dropout Early Warning System Modeling. International
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Date
2019-10-18
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Advanced Studies In Computer Science And Engineering
Abstract
Dropout 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.
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Citation
Isiaka R.M., Babatunde R. S., Ajao F.J. and Abdulsalam S.O. (2019)