Student performance prediction with BPSO feature selection and CNN classifier

Educational Data Mining (EDM) is gaining great importance as a new interdisciplinary research field related to some other areas. It is directly related to data mining (DM), the latter being a fundamental part of knowledge discovery in databases (KDD). This data is growing more and more and contains hidden knowledge that could be very useful for users (both teachers and students). It is convenient to identify such knowledge in the form of models, patterns, or any other representation scheme that allows better exploitation of the system. Data mining is revealed as the tool to achieve such discovery, giving rise to EDM. In this complex context, different techniques and learning algorithms are usually used to obtain the best results. Recently educational systems are adopting artificial intelligent systems, especially in the educational context, specific areas for extracting relevant information, such as EDM, which integrates numerous techniques that support the capture, processing, and analysis of these sets of records. The main technique associated with EDM is Machine Learning, which has been used for decades in data processing in different contexts, but with the advent of Big Data, there was an intensification in the application of this technique to extract relevant information from a huge amount of data. This paper proposes the student performance prediction using CNN (Convolution Neural Network) and BPSO (Binary Particle Swarm Optimization) based feature selection method. In this study, classifiers are made for 2-class and 5-class predictions. The proposed system claims an outperforming accuracy of 96.6% with various previous research works as well as found that the majority of attributes related to school activities as compared to data on demographic and socioeconomic characteristics.