An Ensemble Predictive Model Based Prototype for Student Drop-out in Secondary Schools
Neema Mduma 1 * , Khamisi Kalegele 2, Dina Machuve 1
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1 The Nelson Mandela African Institution of Science and Technology, TANZANIA
2 Tanzania Commission for Science and Technology, TANZANIA
* Corresponding Author

Abstract

When a student is absent from school for a continuous number of days as defined by the relevant authority, that student is considered to have dropped out of school. In Tanzania, for instance, drop-out is when a student is absent continuously for a period of 90 days. Despite the fact that several efforts have been made to improve the overall status of education at secondary level, the student drop-out problem still persists. Taking advantage of advancement in technology, several studies have used machine learning to address the student drop-out problem. However, most of the conducted studies have used datasets from developed countries, while developing countries are facing challenges on generating public datasets to be used to address this problem. Using a dataset from Tanzania which reflect a local scenario; this study presents an ensemble predictive model based prototype for student drop-out in secondary schools. The deployed model was developed by soft combining a tuned Logistic Regression and Multi-Layer Perceptron models. A feature engineering experiment was conducted to obtain the most important features for predicting student drop-out. Furthermore, a visualization module was integrated to assist interpretation of the machine learning results and we used flask framework in the development of the prototype.

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.29333/jisem/5893

J INFORM SYSTEMS ENG, 2019 - Volume 4 Issue 3, Article No: em0094

Publication date: 22 Aug 2019

Article Views: 877

Article Downloads: 931

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