S3PSO: Students’ Performance Prediction Based on Particle Swarm Optimization

Nowadays new methods are required to take advantage of the rich and extensive gold mine of data, given the vast content of data particularly created by educational systems. Data mining algorithms have been used in educational systems, especially e-learning systems, due to the broad usage of these systems. Providing a model to predict the final student results in an educational course is a reason for using data mining in educational systems. In this paper, we propose a novel rule-based classification method called S3PSO (Students’ Performance Prediction based on Particle Swarm Optimization) to extract the hidden rules that could be used to predict the students’ final outcome. The proposed S3PSO method is based upon the Particle Swarm Optimization (PSO) algorithm in a discrete space. The S3PSO particle encoding inducts more interpretable even for normal users like instructors. In S3PSO, the Support, Confidence, and Comprehensibility criteria are used to calculate the fitness of each rule. Comparing the results obtained from S3PSO with other rule-based classification methods such as CART, C4.5, and ID3 reveals that S3PSO improves 31% of the value of fitness measurement for the Moodle dataset. Additionally, comparing the results obtained from S3PSO with other classification methods such as SVM, KNN, Naïve Bayes, Neural Network, and APSO reveals that S3PSO improves 9% of the accuracy value for the Moodle dataset and yields promising results for predicting the students’ final outcome.