Candidate Teacher Performance Prediction Using Classification Techniques: A Case Study of High Schools in Gaza-Strip

摘要

This paper aims to build data mining model to predict the performance of candidate teachers who apply for employment in education of high schools of Gaza Strip. We apply three classification algorithms on our dataset which are Decision Tree, Naive Bays and KNN. Our dataset contains 8000 teacher records collected from ministry of education in Gaza Strip. Although there are a lot of researchers proposed many approaches in education field to predict student's performance, yet, their efforts didn't extend to predict candidate teacher's performance. So, we present an experimental study to assist stakeholders of education in Gaza Strip in selecting the most suitable candidate teachers for employment. On other hand, our study provides feedback to universities about their educational system and student quality, and helps them to take decisions to enhance the quality of education. According to our results the decision tree algorithm was better than others in which it achieves accuracy almost 90%.