Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques

Abstract

Thanks to the advancement of technology, vast amounts of data are being generated in various fields on a daily basis. The research on identifying hidden patterns and extracting useful information from big data has become increasingly important. In the field of education, the availability of large datasets has allowed for the emergence of data mining techniques as an alternative to traditional statistical methods. Unlike traditional statistical methods, data mining can uncover hidden relationships between variables, thus avoiding the loss of valuable information and enabling the utilization of essential data in education. By unlocking valuable insights and predicting important relationships, educational data mining (EDM) has the potential to enhance and improve the quality of education. This study aims to demonstrate the predictive power of EDM through a sample application and draw attention to its implications. The dataset used in this study consists of survey responses collected from university students. The variables in the dataset include academic self-efficacy, sense of community, academic achievement averages, and various demographic variables of distance education students. Descriptive modeling was employed to identify latent patterns between variables, while a predictive model was utilized to estimate variables. In order to achieve this, both association rule mining and classification algorithms were employed. The findings of this study indicate that EDM can effectively identify relationships between variables and make accurate predictions.