## 1 Introduction  
The study of the performance of the proposed EWOA is a challenging problem that managers face at any institution. The goal of this paper is to develop a new approach to the development of the EWOA. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handle the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions. ## 1 Introduction  
Students' performance prediction (SPP) problem is a common challenge for institutions' lecturers and decision-makers to develop the best educational strategies for students. To perform such a prediction, several educational parameters can be employed to evaluate the performance of students, such as exams grades, Grade Point Average (GPA), lecture absenteeism, number of attempts to pass a course or an exam. Moreover, other demographic features such as gender, family relationship, parent profession, marital status, and personal habits [1; 2]. Predicting students' performance for educational organizations has been conducted by many scientific communities. Examining a vast amount of educational data and extract their impacts on students' performances is closely related to educational data mining (EDM) and machine learning (ML) algorithms. Generally speaking, EDM is a set of data mining methods that tries to extract hidden and valuable information from educational data to expand our understanding of students' performance and enhance the learning process [3; 4]. EDM applications require two types of data: (i) educational data collected from educational systems such as exams centers, virtual courses, registration offices, and e-learning systems, and (ii) demographic data that presents information about students. Demographic data is usually collected by surveys or personal meetings. Both types of data can be used to build a robust EDM application, which is able to manipulate seemingly meaningless educational data into valuable knowledge that can improve the learning process and avoid negative performance [5]. In EDM, generally speaking, different kinds of data mining methods are needed, including but not limited to classifications [6], clustering [7], association rule mining [8], and web mining [9]. Moreover, due to modern learning technologies such as online classrooms, exams, and seminars, EDM applications can manipulate educational data accurately for a better understanding of the students' performance, and learning process [10]. Such EDM applications can assist both futors and decision-makers in executing suitable learning strategies that fit their students. In reality, there are many advantages of EDM applications, such as revealing the weaknesses of the learning process between the teachers and students, predicting dropout potential, and negative student behaviors [11]. Moreover, it can determine the lapses and weaknesses of teaching strategies. EDM applications assist with reviewing the current learning models and evaluate their effectiveness. It can be used to evaluate the feedback information obtained from students and determine the limitations of the learning processes. EDM can cluster students based on their levels based on different criteria such as personal skills, learning behaviors, social attitudes, and interests [12].