## 2 Related Work
### Classification Methods  
Classification techniques such as; Decision Trees (DT), Support Vector Machines (SVM), Naive Bayes (NB), and Artificial Neural Networks (ANN) are widely used in the field of education to predict students' performance. For example, as stated in [45], the DT classifier was applied to predict the final grades of students in a university course under study. [PERSON] et al. [46] used eight-year data from 2006 to 2014 of undergraduate students to predict their academic performance in computer science courses. The applied dataset contains information such as gender, hometown, family income, and GPA. In addition, three classification algorithms comprising DT, Rule-Based (RB), and NB were utilized for building SPP models. The experimental results revealed that the RB classifier is the best one compared to the other classifiers by recording the highest accuracy rate of 71.3%. [PERSON] et al. [47] proposed an academic performance prediction model using two approaches, including fuzzy genetic algorithm (FGA) and DT. Internal and sessional makes along with admission scores were selected as features. The resultant prediction model can be used to determine the students' performance for each module. Hence, instructors can identify low-performing students and take early steps to improve their performance. SVM has been applied in SPP fields. For instance, [PERSON] et al. [48] tried to accurately predict students' performance with the aim of place them into suitable faculty courses where a multi-class SVM (MSVM) classifier was used to build the prediction model. In addition, the educational students' dataset from the University of Lagos, Nigeria, was applied to examine the proposed model. Findings of the experiments revealed that MSVM based SPP model with 7-fold cross-validation could correctly predict students' performances and provide the university management with the required information for placing students in various academic programs. In addition, [PERSON] and [PERSON] [49] utilized an SVM classifier for predicting the placement of students. The proposed model was evaluated on an educational dataset of students containing six features: attendance, GPA, reasoning, quantitative, communication skills, and technical skills. The authors stated that prediction results could provide educational institutions a better understanding of how students should be placed. Furthermore, based on the psychological information (features) of students, [PERSON] and [PERSON] [50] proposed a classification model using SVM to categorize students into three classes, including high, average, and low, depending on their academic performance. Experimental results showed that SVM with Radial Basis kernel function could provide better accuracy than using Linear Kernel function, which is nearly 90%. Another example of using classification approaches in the field of education for predicting the performance of students can be found in [51]. In this study, two classifiers, NB and SVM, were applied over students' data such as residence, GPA, and profile data to predict whether their college student will finish their studies in four years or less. Experimental results showed that SVM surpassed NB with 69.15% accuracy. Using the NB classifier in the field of SPP, [PERSON] et al. [52] introduced a model for predicting students' performance in semester exams. This model is based on NB classifier and is used to predict the end-of-semester results of students. The outcome of the proposed model can help students in improving their academic performance. [PERSON] et al. [53] estimated student's performance using NB classifier. The proposed model is utilized to discover the hidden patterns between subjects that influence the performance of students. In addition, the Best-First approach was applied for feature selection. Results have shown the superiority of the NB algorithm in predicting the performance of students compared to several classifiers such as Random TreeMulti-Classes Classifier, Conjunctive Rule, NearestNeighbour, and Lazy IB1.