Intelligent Analysis of Students’ Performance in Nigerian Schools: A Multi-Layer Perceptron-Based Prediction

Education is the bedrock of any society, it provides a foundation for development; therefore, this foundation must be properly built so that it can provide the desired development. The multilayer perceptron is characterized by a series of layers of interconnected neurons and feedforward neural networks that use standard backpropagation algorithms in their training. As a class of supervised learning model, they always require a desired response to be trained. This paper used student records collected from schools across Nasarawa State, to derive insights from the dataset. Multilayer perceptron architectures were used to predict students’ performance. The model consists of 8 layers; an input layer, six hidden layers, and an output layer. Accuracy, precision, recall, and confusion metrics were used to measure the performance of the model. The student dataset was analyzed to address the problem of mass failure in senior secondary schools in Nigeria. The result of the analysis showed that only 11% of students passed with marks greater than 50 in their core courses. Most students that passed with good facilities were from private schools. These results show that there was a massive failure, and it is mostly from government-owned schools that lack portable drinking water, stable electricity, and other infrastructures. The model presented in this paper predicted student performance 97% correctly. This paper made recommendations on what government and the society need to do to improve the quality of education in Nigeria.