Intelligent Model for Suitable University Specialization Selection in Palestine

Choosing suitable track is a key success in the academic and professional life. Whenever the specialization is appropriate for the student; an increase in students' performance is the natural result. Many studies investigated the influential factors affecting specialization selection by using statistical methods, but none of the researches studied these factors and employed machine learning methods to a develop classification model which can help to choose a suitable specialization. In this research, we extracted the local influential factors in our area (Palestine) by using filter approach Correlation-based Feature Selection (CFS) and factor analysis approach Principle Component Analysis (PCA). According to the results, we identified five basic influential factors affecting specialization selection at the universities in Palestine. Then we developed a classification model which might consider the first proposed model studying the influential factors affecting the specialization selection and has the ability to predict the specialization selection for high school students by identifying the suitable specialization based on rules. A special questionnaire was developed which covers various questions relating the influential factors. Hence, our proposed model depends on extracting the previous knowledge and student experiments. The collected data used as inputs to build our classification model using PART. According to the results, the accuracy of the proposed model is 77.4% for the training group, and 73.7% for the testing group. The accuracy of the proposed model is 73.7%. The model adopted final 49 rules, which are considered as a map to lead high school students steps toward choosing the suitable specialization.