## 1 Introduction
Under semi-controlled conditions, the classroom is an ideal location for systematically investigating group interactions--such as those between students and their teacher while measuring cognitive and behavioral outcomes (e.g., student engagement and academic performance). It has been demonstrated that the dynamic interaction between a teacher and a group of students influences both student engagement and academic achievement, which are both fundamental to classroom learning [17; 18]. Instruction and knowledge acquisition can be perceived as a collaborative effort involving both the instructor and the learners, in which aspects of the interactive companion and the activity are evaluated as stimuli in a two-way exchange. Exploring the neural activity that lies beneath student-teacher relationship exchanges in the classroom could help in comprehending and predicting educational outcomes from both the student's and the teacher's perspectives, according to research. In a recent study [19], researchers recorded nine students concurrently using portable EEG equipment in the classroom while they watched a natural movie. The results of these experiments were replicated from laboratory-based designs using commercial-grade equipment, showcasing the feasibility of applying such equipment to measure students' attentional engagement in real-world scenarios. However, most of the existing research is limited by several drawbacks, as studies frequently neglect to examine social behavior in naturalistic settings, are routinely restricted to dyads, and fail to investigate social dynamics over time. To identify neural markers of group engagement during dynamic real-world group interactions, this research study [20] presents experiments that significantly transcend pairs and laboratory boundaries. They assessed the brain activity of 12 high school pupils throughout an entire semester (11 classes) during routine classroom activities using a portable electroencephalogram (EEG). Student class engagement and social dynamics can be predicted by the degree to which brain activity is synchronized across students [20], according to a novel analysis technique for evaluating group-based neural coherence. Machine learning and deep learning have indeed gained significant popularity in signal processing domains such as PPG (photoplethysmography) [21; 22], electrocardiogram [23], and EEG [24] analysis. Utilizing the multiple signal classification (MUSIC) model to extract features from multichannel EEG signals in an effective way, ref. [25] addresses the computational challenges associated with emotion recognition based on EEG. The research aims to enhance the efficiency of emotional state classification through the adjustment of MUSIC parameters to identify discriminative features. Understanding a student's learning style is crucial for effective teaching, as it enables educators to tailor instructional methods to individual needs. Learning styles refer to the preferred ways in which students absorb, process, and retain information. By recognizing these preferences, teachers can design lesson plans that align with students' natural tendencies, thereby enhancing engagement, comprehension, and retention. This study aims to explore EEG data collected from real classrooms to assess the discriminative power of EEG in identifying different learning styles using machine learning. The key contributions of this work are as follows:  
* First comprehensive ML framework: This study introduces the first comprehensive machine learning (ML) framework that utilizes EEG data from real classroom environments to examine the discriminative power of EEG in distinguishing different learning styles. * Effective use of real-world data: The proposed ML framework demonstrates that EEG data from real-world classroom settings can be effectively used for the classification of different learning styles, highlighting the practical applicability of this approach.