Investigation of the Variables Affecting Primary School Teachers' State of Anxiety and Motivation in Mathematics Teaching through Data Mining Methods

The goal of the present study is to determine the factors affecting primary school teachers' anxiety and motivation in teaching mathematics by using the Random Forest technique, which is one of the data mining techniques, and to determine their profiles by using the K-Means clustering algorithm, which is also one of the data mining techniques. For this purpose, the study used the cross-sectional survey method. The participants are 485 primary school educators working in state and private schools in various cities of Turkey and volunteering to complete the measurement tools. In order to collect the data, "Demographic Information Form", "Mathematics Teaching Anxiety Scale" and "Primary School Teacher Motivation Scale" were used. In the first stage of the study, two different models were constructed by using the Random Forest model. In the first of these models, six independent variables in the Demographic Information Form filled by the participants and two-level anxiety variable (low-high) obtained through the transformation of the anxiety scale scores were taken as the dependent variables. In the second model, six independent variables in the Demographic Information Form and a two-level motivation variable (low-high) obtained through the transformation of the motivation scale scores were taken as the dependent variables. While the variable with the highest predictor importance in the prediction for both anxiety and motivation levels is "grade level taught", the second most important variable is "length of service".