## 2 Literature Review  
Several studies have been conducted to investigate student field specialization (student major) using ML. Past research has used different ML techniques and input features to study the relationship between student data and student majors. [PERSON] et al. [3] built a recommendation system to suggest suitable colleges for KAU students based on the students' grades, college specializations, and enrollment requirements. They applied this system to a dataset of 960 KAU preparatory students in 2017. Two methods were used to evaluate the accuracy of the k-nearest-neighbor algorithm. In the first method, the dataset was split into two datasets, 20% of the dataset for testing and the remaining 80% for training, which generated 70.83% accuracy. The second approach applied k-fold cross-validation, where the dataset was split into K smaller sets and the test was applied K times. [PERSON] et al. [50] have proposed an accurate institutional recommender system (RS) that was developed by combining decision tree and association rule methodologies. The RS is intended to assist students in selecting acceptable colleges based on their context and educational institution information using a mobile device. [PERSON] and [PERSON] [9] presented an adaptive recommendation system for predicting a suitable engineering department for students enrolled in an engineering preparatory year college using classification methods such as SVM, k-nearest neighbor (KNN), linear regression (LR), quadratic discriminant analysis (QDA), and RF. The system recommends a suitable engineering department among seven engineering departments for each student based on his academic performance and the proposed system has an average accuracy of 82.57%. In the study of [PERSON] et al. [51], 3 ML algorithms, namely, naive Bayes (NB), RF, and sequential minimal optimization (SMO), were trained on a dataset collected from three different educational colleges in Bangladesh to identify and select the best groups of educational majors to streamline the selection of a suitable direction for new students. The results showed that RF had the best performance, with 84.9% accuracy, 84.9% precision, 84.6% sensitivity, and an F-measure of 84.3%. In a study conducted by [PERSON] et al. [52], an academic decision support system was built in the IS department to classify and recommend IS sub-majors for students using a C4.5 decision tree classifier and a rule-based approach. [PERSON] et al. [10] adopted 8 methods (namely, the J48 tree classifier, logistic function classifier, naive Bayes (NB), nominal regression, decision tree CHAID, neural network multilayer perceptron, neural network radial basis function, and nearest neighbor) to recommend a suitable specialization for engineering students. The first three methods were tested in Weka and achieved accuracies of 80.5%, 64.30%, and 60.11%, respectively. The last 5 experiments conducted in SPSS, yielded accuracies of 64.00%, 68.20%, 71.30%, 61.00%, and 71.20%. Moreover, the J48 tree classifier performed the best, with the highest accuracy of 80.5%. A study conducted by [PERSON] and [PERSON] [53] aimed to develop a decision support system for student major selection using two ML methods, J48 and Bayesian network algorithms (BNs). Their results showed that BNs performed the best, with 92.13%, 0.93, and 0.91 accuracy, precision, and F-measure, respectively. [PERSON] and [PERSON] [35] applied 8 classification methods, namely, SVM, decision tree (DT), naive Bayes (GNB), RF, gradient boosting decision tree (GBDT), convolutional neural network (CNN), collaborative filtering (CF), and recurrent neural network (RNN), to recommend appropriate college majors. RF performed best, with an accuracy of 97.87% and an f-score of 96.60%. [PERSON] et al. [54] proposed an improved SVM-based prediction system model for predicting second major selection.