Predictive modelling and analysis of academic performance of secondary school students: Artificial Neural Network approach

Abstract

The need for educational managers and policy makers to design and implement pedagogical and instructional interventions necessitates the development and validation of a predictive model for analyzing the academic performance of students before gaining admission into university. This study adopts feed-forward neural network to establish and analyze the complex nonlinear relationship that exist between cognitive and psychological variables that influence the academic performance of secondary school students. The sample space comprises of 120 students selected from four randomly selected secondary schools in Ibadan-North Local Government Area of Oyo State. Students' performances in five science subjects at 2015 West African Examination Council (cognitive factor) and psychological factors (age, gender, status of school and parent occupation) were used as inputs to the proposed model while the performance at Post-Unified Tertiary Matriculation Examination (Post-UTME) served as the target output. Simulated results from the study showed that artificial neural network (ANN) is efficient at clustering students into different categories according to their predicted level of performance. Study of this type will enable educational planners and curriculum developers provide better educational services as well as customize assistance to students when they gain admission into universities.