## 1 Introduction
EDM and ML allow us to design a learning model(s) to predict students' performance as a classification or recognition model(s). However, selecting a robust ML model is a challenging task due to several factors such as data nature, imbalanced data, noisy data, incomplete data, and the number of collected samples. Imbalanced data plays a vital role that affects the overall performance of ML models. For example, the number of passed students is much higher than the number of failed students, and the performance of learning model(s) will be influenced toward passed students. So, the learning process will suffer from overfitting problem. As a result, it is essential to analyze the educational data before building the EDM application. Moreover, the educational data should not have missing data to prevent the unstable behavior of the ML model. Several research papers addressed the imbalanced educational datasets while building ML models [13; 14; 15]. In general, imbalanced data is manipulated based on data level (e.g., resampling methods) or algorithm level (e.g., cost-sensitive learning). Figure 1 depicts the life cycle of EDM process. In data mining techniques (e.g., classification), data preprocessing has a major impact on both the quality of chosen features and the performance of learning algorithms [16; 17]. Feature selection (FS) is a fundamental preprocessing stage that aims to uncover and keep informative patterns (features) and remove noisy, uninformative, and irrelevant ones from the feature space. Detecting high-quality subset of features will boost the accuracy of learning classifiers and lessen the computational cost [18; 19]. According to assessmentcriteria of the selected subset of features, FS techniques follow one of two branches: filters or wrappers [19; 20]. Filter FS methods utilize scoring matrices for estimating the excellence of the selected subset of features. In other words, in filter type, features are weighted using a filter technique (e.g., information gain or chi-square), and then the features that possess weights less than a pre-set threshold are excluded from the features set. In the case of wrapper FS, a learning classifier (e.g., Linear Discriminant Analysis or K-Nearest Neighbour) is hired to decide the excellence of subsets of features produced by a search approach [21; 22]. In general, in comparison with filter methods, wrapper FS can deliver better performance because it can implicitly discover and employ dependencies between features of a subset, whereas filter FS may miss such an advantage. However, the computational cost of using filter FS is cheaper than wrapper FS [23]. Feature subset generation is identified as a search operation for finding a high-quality subset from a given set of patterns where a search mechanism such as complete/exact, random, or a heuristic is employed [24; 25; 26]. In a complete search, all potentially obtainable feature subsets in the search space are formed and assessed. In other words, if a dataset includes \(M\) features, then \(2^{M}\) subsets will be obtained and examined to identify the most valuable one. Complete search is impractical when dealing with massive datasets because of its high computational cost. Random search is another mechanism for generating subsets of features. In this mechanism, looking for the following feature subset in the feature space is done randomly [27]. In some cases, the random search may lead to generate all potential subsets of features as in the complete search mechanism [18; 28]. Compared to complete and random search, heuristic search is a different search mechanism for generating subsets of features. It is defined by [PERSON] [28] as upper layer general methods that can be employed as guiding mechanisms to design underlying heuristics for resolving particular optimization problems. In contrast to complete/exact methods [29; 30], meta-heuristics algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) have demonstrated outstanding ability in solving many FS problems [19; 31; 32; 33].