Optimizing Student Performance Prediction: A Data Mining Approach with MLPC Model and Metaheuristic Algorithm

Abstract

Given the information stored in educational databases, automated achievement of the learner's prediction is essential. The field of educational data mining (EDM) is handling this task. EDM creates techniques for locating data gathered from educational settings. These techniques are applied to comprehend students and the environment in which they learn. Institutions of higher learning are frequently interested in finding how many students will pass or fail required courses. Prior research has shown that many researchers focus only on selecting the right algorithm for classification, ignoring issues that arise throughout the data mining stage, such as classification error, class imbalance, and high dimensionality data, among other issues. These kinds of issues decreased the model's accuracy. This study emphasizes the application of the Multilayer Perceptron Classification (MLPC) for supervised learning to predict student performance, with various popular classification methods being employed in this field. Furthermore, an ensemble technique is utilized to enhance the accuracy of the classifier. The goal of the collaborative approach is to address forecasting and categorization issues. This study demonstrates how crucial it is to do algorithm fine-tuning activities and data pretreatment to address the quality of data concerns. The exploratory dataset utilized in this study comes from the Pelican Optimization Algorithm (POA) and Crystal Structure Algorithm (CSA). In this research, a hybrid approach is embraced, integrating the mentioned optimizers to facilitate the development of MLPO and MLCS. Based on the findings, MLPO2 demonstrated superior efficiency compared to the other methods, achieving an impressive 95.78% success rate.