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

Thanks to the technologies that have become a part of daily life, huge data piles are formed in almost every field. Research on detecting hidden patterns in big data and discovering useful information has gained importance. The amount of data accumulated in the field of education has enabled data mining techniques to come to the fore in this field as an alternative to traditional statistical methods. In traditional statistical methods, hidden relationships between some variables can be ignored. This can cause some information to be lost or not to use essential data in essential areas such as education. However, educational data mining (EDM) can unlock valuable data and predict important relationships to improve and improve the quality of education. For this reason, this study aimed to perform a sample EDM application to draw attention to its EDM predictive power. The data set consisted of the opinions collected from university students. This 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.