Enhanced Model for Predicting Student Dropouts in Developing Countries Using Automated Machine Learning Approach: A Case of Tanzanian's Secondary Schools

The Sub-Saharan countries are leading in dropout rates in secondary schools by 37.5% followed by South Asia 15.5% and Middle East 11% in 2018. In Tanzania, student dropouts in secondary schools increased from 3.8% in 2018 to 4.2% in 2019. Different initiatives such as parent-workshops, parent-teache...

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Bibliographic Details
Published in:Applied artificial intelligence 2022-12, Vol.36 (1)
Main Authors: Mnyawami, Yuda N., Maziku, Hellen H., Mushi, Joseph C.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Developing countries
Formal method
LDCs
Machine learning
Mathematical models
Parameter identification
Root cause analysis
School dropouts
Secondary schools
Students
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Summary:The Sub-Saharan countries are leading in dropout rates in secondary schools by 37.5% followed by South Asia 15.5% and Middle East 11% in 2018. In Tanzania, student dropouts in secondary schools increased from 3.8% in 2018 to 4.2% in 2019. Different initiatives such as parent-workshops, parent-teacher meetings, community empowerment programs, school feed programs, and secondary education development program (SEDP) have been used to address student dropout but unfortunately, the dropout problem still persists. The persisting dropout problem especially in secondary schools is attributed to a lack of proper identification of root causes and unavailability of formal methods that can be used to project the severity of the problem. In addressing this problem, machine learning (ML) techniques have done a great job in predicting secondary school dropouts. However, most of the ML models suffer from processing features, and hyper-parameters tuning leads to poor prediction accuracy in identifying the root causes of the student dropout. In this study, the AutoML model has been used to improve prediction accuracy by selecting the corresponding hyper-parameters, features, and ML algorithm for the acquired dataset. The proposed model achieved a better prediction accuracy of DT = 99.8%, KNN = 99.6%, MLP = 99% and NB = 97%. The improved prediction score indicates an accurate selection of features that cause student dropout that can be looked in a close eye in the learning process for early intervention.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2022.2071406