Evaluation of academic self-efficiency, community feeling, and academic achievement of students in the process of the Covid-19 pandemic by data mining techniques

This study aimed to perform a sample educational data mining (EDM) application to draw attention to its EDM predictive power. The data set consisted of the opinions collected from university students. These data set variables were formed by distance education students' academic self-efficacy, sense of community, academic achievement averages, and some demographic variables. The descriptive model revealed latent patterns between variables in the study, and a predictive model was used to estimate variables. For this, the association rule method and classification algorithm were also used. At the end of the study, it was concluded that EDM could effectively find relationships between variables and predict variables.