## 1 Introduction
WOA is a modern meta-heuristic algorithm, introduced by [PERSON] and [PERSON] [34]. It simulates the humpback whales' intelligent foraging behavior. WOA possesses a simple structure that makes it easy to implement. It also has only two primary parameters that need to be adjusted. In addition, the WOA algorithm depends on just one parameter for smooth shifting from exploration to exploitation. WOA has shown high exploration  
Figure 1: EMD lifecycle. ability. Unlike other meta-heuristic algorithms, WOA updates the position vector of a whale (solution) in the exploration stage with respect to the position vector of a randomly chosen search agent rather than the optimal search agent discovered so far [17; 34; 35; 36]. Like other meta-heuristic algorithms, WOA has drawbacks like early convergence and the ease of falling into the local optimum. Hence, scholars have made several improvements to the basic version of WOA to overcome its limitation and employed it to solve various optimization problems. For instance, [35] proposed an improved version of WOA based on Natural Selection Operators and applied it as a wrapper feature selection method for software fault prediction. [PERSON] and [PERSON] [17] combined WOA with simulated annealing (SA) algorithm to enhance its exploitation ability and applied their enhanced WOA-based approach for feature selection. Also, [PERSON] and [PERSON] [36] proposed an improved variant of WOA and applied it for solving complex constrained optimization problems. A Mixed-Strategy-based WOA was proposed by [PERSON] et al. [37] for optimizing the parameter of a hydraulic turbine governing system (HTGS). [PERSON] et al. [38] proposed Levy flight and logical chaos mapping based WOA approach and employed it to tackle virtual machine (VM) placement problem. As presented in [39], WOA has the same problem as many other optimization algorithms and tends to be stuck into local optima. To overcome this problem, two enhancements for the WOA algorithm were proposed. The first improvement involves applying Elite Opposition-Based Learning (EOBL) the initialization stage of WOA, whereas the second one includes the integration of evolutionary operators comprising mutation, crossover, and selection from the Differential Evolution algorithm at the end of every WOA iteration. Since the WOA-based algorithms have been widely and effectively used in various applications, this is the foundation and motivation of this research as well. This paper proposes an evolutionary-based SPP model that integrates an enhanced variant of WOA (EWOA) with an ML algorithm. The new variant EWOA is used to enhance the FS process and the prediction of students' performance. The efficiency of the proposed model developed in this research is evaluated on two real, imbalanced, and public educational datasets adopted from the literature. To sum up, the main contributions of this research are as follows:  
1. The ADASYN sampling technique is applied to handle the problem of imbalanced data. 2. Various types of well-known ML algorithms are assessed to select the best-performing one to handle the SPP problem. 3. Eight fuzzy transfer functions from S-shaped and V-shaped families are examined to prepare WOA to match the binary search space of the FS problem. 4. An improved form of the WOA algorithm is introduced by combining it with the Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) mechanism. The main objectives are overcoming the main weak point of WOA (i.e., weakness exploitation process) and keeping an appropriate scale between exploration and exploitation processes. 5. The performance of the proposed EWOA is evaluated against the state-of-the-art metaheuristic algorithms and shows promising results. The rest of the paper is organized as follows: Section 2 presents the related works of SPP and related EDM applications. Section 3 explores the proposed methods. Section 4 explores the educational datasets used in this work. Section 5 presents the performance evaluation criteria for the proposed method.