Machine Learning Approach to Determining the Influence of Family Background Factors on Students’ A
Authors:
KUYORO Afolashade
Publication Type: Journal article
Journal: International Journal Of Information Sciences And Application
ISSN Number:
0
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Abstract
Machine learning has been successfully applied to many domains such as fraud detection, medicine, banking, bioinformatics, and so on. The application of this paradigm to enhancing and evaluating the higher education tasks is a new research area. There have been various works bordering on students’ performance, and related problems but the focus of this work is on applying machine learning algorithms to students’ data for predictive purposes in an educational environment. We propose to develop novel approach based on machine learning algorithms to be delivered to educational institutions for guiding their planning of educational activities within the scope of increasing the academic performance of the students by taking into account their family background factors. One thousand five hundred (1500) records of students in three Nigerian tertiary institutions will be used. The students’ academic performance will be measured by Cumulative Grade Point Average (CGPA) at the end of first year. Waikato Environment for Knowledge Analysis (WEKA) and See5 will be used to generate three decision tree models, Artificial Neural Networks and two rulesets. These algorithms will be compared based on their accuracy level and confusion matrices to determine the optimal model. The rules generated from the optimal model will be disseminated to educational administrators to guide in their planning activities.