## 1 Introduction  
Student retention is a major concern for STEM fields. It is particularly problematic in computing fields, where enrollment has not kept pace with industry demands. Therefore, finding patterns in historical educational data can help the education community reveal the potential reasons for students' withdrawal from the computing field. This information provides guidelines to better understand the relative success of computing students and enable strategic solutions to achieve higher retention rates [1]. The application of ML in education is currently very interesting to researchers and education communities. Building predictive models involves learning from data. Machine Learning (ML) is a technology that enables computers to learn without being explicitly programmed. One of the most important recent discussions in this field is related to the enhancement of ML algorithms' performance in different applications, such as education. In the context of ML, choosing the best model has always been a concern. Moreover, it is crucial to transfer the most important information to both save time and improve performance. In general, based on the principle of Occam's razor, \"a model should be simple enough for efficient computation and complex enough to be able to capture data specifics\" [2]. The extraction of useful information and the presentation of scientific, educational decision-making is necessary, providing professionals with an additional source of knowledge. The educational datasets can be massive. The high-dimensional nature of many modeling tasks has given rise to a wealth of feature selection techniques. Feature selection is a way of removing noise and random errors in the underlying data. In feature selection, we use techniques to select features that are more relevant to the problem and withdraw redundant or irrelevant data without incurring much loss of information [3]. Feature selection facilitates the best performance for the ML model [4]. The main idea of feature selection is to select a subset of features for the model to either improve the model's performance or reduce the structure's size and computational cost [5]. In [6], [PERSON] and [PERSON] presented a filter feature selection method to reduce the overall computation cost. In [PERSON] et al. [7], authors concluded that information entropy can determine the importance of features and can be effective for the reduction of attributes. Choosing K out of N features helps the training time to be distributed by N/K [8]. In feature selection problems, the size of the search space for a dataset with \(N\) features would be \(2^{N}\)[9]. Therefore, researchers have provided different search methods to reduce models' computational costs. [PERSON] and [PERSON] believed these methods typically have four basic steps: (1) a generation procedure to generate the next candidate, (2) an evaluation function, (3) stopping criteria, and (4) a validation procedure [10]. Cleaning and featurization of the data are considered the most time-consuming steps of AutoML, and researchers have investigated different methods to improve the performance of the AutoML process [11]. There are various heuristic search strategies to address combinatorial optimization challenges. These algorithms include, but are not limited to, nature-inspired algorithms such as Genetic Algorithms (GA) and Swarm Intelligence (SI), which include Particle Swarm Optimization (PSO) [12] and Artificial Bee Colony (ABC) [13]. This paper proposes a new approach (A2 BCF) for feature selection using an ABC algorithm to improve the performance of the classifier in an education application and predict students' success. The goal of A2 BCF is to choose a subset of features from the whole feature space, reducing the total number of used features to improve the total computation time. It is specifically helpful for ML algorithms, such as support vector machines, as their training time is highly dependent on the dimensions of the data.