The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

• Main Authors: Almotairi, S. (Author), Farooq, K. (Author), Granelli, F. (Author), Hassan, M.A (Author), Muhammad, Y. (Author), Strážovská, Ľ (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: classification algorithms; education; feature selection; machine learning; socioeconomic status
• Online Access: View Fulltext in Publisher; View in Scopus

 Nowadays, we are living in the modern era of technological revolution and globalization, where people are giving more priority to proper education to compete among the top countries and to achieve something in their lives. Education improves a person’s abilities and creativity, which in turn have a positive effect on the development of a nation’s or an individual’s economy and also play a productive role in it. The traditional approaches are based only on statistical measures and are not capable of figuring out the most significant socioeconomic factors affecting the performance of a student. Keeping in mind the significance of socioeconomic status (SES) in improving the performance of a student, this study analyzes the important socioeconomic factors that affect the performance of a student in Khyber Pakhtunkhwa, Pakistan. We developed our own dataset by collecting data from 100 different schools (both government and private) in Khyber Pakhtunkhwa, Pakistan, consisting of more than 5550 students who were given a proper questionnaire survey. The created dataset consists of a total of 18 features and a target class. In this research, we used different statistical and machine learning (ML) methodologies to identify the most crucial elements that significantly affect the academic achievements of a student and have a strong correlation with the target class. To select the most prominent features from the dataset, we used two different feature selectors (FCBF and relief) and measured their performances along with ML models. To measure the significance rate of each ML algorithm using the full and selected feature space, we used different performance measures such as accuracy, precision, recall, sensitivity, specificity, etc. The experimental outcomes show that the feature selection algorithms significantly improve the performance of the classification models by providing more relevant features that have a strong association with the target class. This study also offered some advice for decision-makers, particularly in the respective education sector and other authorities, to develop specific solution strategies, plans, and initiatives to address the issue. It is envisioned that the suggested scheme will help the residents of Khyber Pakhtunkhwa province, in particular, obtain a high-quality education that can help pave the way for an educated and developed Pakistan. © 2023 by the authors.