Data mining techniques for classifying and predicting Teachers’ performance based on their evaluation reports

Abstract

Background/Objectives: Teachers' performance is a key bridge to ensure successful pedagogical and educational objectives. However, the evaluation of teachers' performance has been used to be a manual and temperamental task for school principals. This traditional context limits the teachers' engagement to develop his/her performance as well as the principle to predict the strengths and weaknesses attached. Hence, schools' principals need to use initiative methods to evaluate the teachers' performance. In this study, a comparative approach was developed to evaluate the teachers' performance aiming at avoiding the potential biased and temperamental human behaves in the teacher's evaluation process. Methods: It involves different Data Mining (DM) techniques to identify the key patterns that are driving the teachers' performance evaluation process. Therefore, the proposed approach extracts several potential and influential indicators mined from a paper-based on teachers' performance reports at the Directorate of Education/ Southern Ghawrs, along with some demographics variables. Several DM algorithms are used to analyze teachers' performance reports and predict their performance, such as NB Tree, Naïve Bayes, and Conjunctive Rule methods. Findings: The experimental results show a significant prediction accuracy improvement by (33%) when applying NB Tree compared to Conjunctive rule, and (12%) when compared to Naïve Bayes techniques respectively.