## 2 Related Work
### Classification Methods
The authors concluded that the NB classifier could be utilized for the classification of students' performance in the early phase of the second semester with 74% accuracy. Neural network (NN) classifier is also utilized to develop automated SPP models. As presented in [54], for instance, the authors used Back Propagation Neural Network (BP-NN) based on the classification to predict future student performance based on their previous knowledge along with other new students with similar characteristics. Academic data of six subjects for 60 high school students were used for model evaluation. Results show that the model is able to produce precise results. [PERSON] and [PERSON] [55] also applied two machine learning classifiers, including NN and NB, using WEKA machine learning software to predict the performance of students. The authors evaluated the proposed models on a small dataset that includes information of 58 students. The recorded results confirmed that NB is better than the NN classifier. As stated earlier, FS is a core pre-processing procedure that aims to find and eliminate noisy, uninformative, and irrelevant features from datasets to reduce data dimensionality and boost the efficiency of machine learning classifiers. Wrapper and filter-based FS approaches have been applied for some works in the area of SPP. For example, in [56], a filter FS approach based on information gain (IG) was employed to filter the highly informative students' behavioral features for building prediction models. A set of ML classifiers including DT, ANN, and NB boosted with ensemble methods such as bagging and boosting were utilized for classification. Results showed that using students' behavioral features can remarkably enhance the performance of students' prediction model. In [14], a feed-forward Multi-Layer Perceptron (MLP) technique integrated with stochastic training algorithms was applied as an SPP model. In addition, IG was exploited as an FS approach, and the SMOTE oversampling technique was applied to deal with the problem of imbalanced data. Experimental results confirmed that the proposed MLP based approach efficiently resolves SPP problems compared with several ML classifiers such as DT, KNN, Logistic Regression (LR), Linear Discriminant Analysis (LDA), SVM, and Random Forest (RF), plus a set of state-of-the-art methods. Wrapper FS approaches that combine optimization algorithms with ML classifiers have also been applied to improve the performance of SPP models. For instance, a wrapper-based FS technique was proposed by [PERSON] et al. [44] for resolving the problem of SPP. In this technique, an improved form of the recent Harris Hawks Optimization algorithm (HHO) was applied to explore the search space for discovering the most informative features. In addition, the KNN classifier was used for evaluating the goodness of the produced subsets of features by the HHO algorithm. Several ML classifiers, including KNN, Layered recurrent neural network (LRNN), NB, and ANN, were applied over a real student performance prediction dataset to assess the overall performance of the SPP system. Most Promising accuracy value was achieved when HHO is applied in conjunction with the LRNN classifier, which is equal to 92%. Another wrapper FS approach based on Binary Teaching-Learning Based Optimization (TLBO) was introduced by [PERSON] et al. [15] for improving the performance of student performance prediction. TLBO algorithm was applied as a search strategy while various ML classifiers (i.e., SVM, LDA, LR, RF, and DT) were used for evaluating the quality of subsets of features generated by the TLBO algorithm. Moreover, two real student performance prediction datasets were adopted for evaluation purposes. It was observed that the utilized datasets are highly imbalanced. For this reason, oversampling techniques (i.e., SMOTE) were applied over the datasets to handle the problem of imbalanced data. The experimental results proved the power of the proposed wrapper FS in improving the classification performance of LR and LDA classifiers.