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Education and Information Technologies

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05-03-2019

Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia)

Authors: Ali Yağci, Mustafa Çevik

Published in: Education and Information Technologies | Issue 5/2019

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Abstract

This study aims to predict the academic achievements of Turkish and Malaysian vocational and technical high school (VTS) students in science courses (physics, chemistry and biology) through artificial neural networks (ANN) and to put forth the measures to be taken against their failure. The study population consisted of 10th and 11th grade 922 VTS students in Turkey and 1050 VTS students in Malaysia. The study was conducted with the screening model, and a 34-item demographic questionnaire was developed for the collection of data Using the SPSS 24.0, the KR20 reliability coefficient of the questionnaire was found to be .90. The items in the questionnaire that were believed to affect academic achievement were accepted as independent variable/input, and the academic achievement averages of students in the previous year’s physics, chemistry and biology courses were considered as dependent variables/output. Using these parameters, a model was created and the academic achievements of the students were predicted with ANN using the Matlab R2016a program. At the end of the study, a successful academic achievement prediction system was developed with an average 98.0% sensitivity over 922 samples for Turkey and with a 95.7% sensitivity over 1050 samples for Malaysia, and the measures to be taken were determined in order the prevent failure of students.

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Title: Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia)
Authors: Ali Yağci
Mustafa Çevik
Publication date: 05-03-2019
Publisher: Springer US
Published in: Education and Information Technologies / Issue 5/2019
Print ISSN: 1360-2357
Electronic ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-019-09885-4

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