## 4 Results and Discussion
(2021) developed and validated an experimental method that effectively stimulates differences in the information-processing dimension, achieving an accuracy of 71.2% [6]. Additionally, other studies have recorded learners' behavior during interactions with learning objects in online courses and used mapping rules to infer learning styles according to the Felder-Silverman model, with reported classification accuracies ranging from 62.5% to 72.7% [50; 51; 52; 53]. In comparison, our proposed framework demonstrates superior performance, achieving higher accuracy than previously reported studies for learning-style classification. This highlights the effectiveness of our approach and the potential of EEG features in educational research, warranting further exploration into effective learning-style recognition. Figure 4: F-values bar plot for each significant feature from ANOVA. Green color bar denotes the lowest F-value. Another finding of the present study is that the RF classifier performed better for discriminating learning-style patterns compared to KNN and MLP. As illustrated in Figure 5, the RF outperformed other classifiers irrespective of the feature choice made. In contrast, KNN and MLP exhibited results that are comparable in nature. Using a statistical feature set, we achieved the highest average performance accuracy of 78.45%, precision of 78.90%, recall of 78.45%, F1-score 78.49%, and AUC of 0.8563. This is in line with previous research supporting the utility of RF for EEG signal classification, showcasing its adaptability. Given that the statistical feature set was the most successful, the subsequent results primarily focused on outcomes for the statistical features and RF classifier model. Notably, entropy achieved the lowest accuracy in learning-style classification because it primarily measures the overall unpredictability or randomness of the signal, which can be highly influenced by the noise and artifacts inherent in EEG data [54]. This measure may lack the specificity needed to capture the distinct patterns or structures crucial for distinguishing between different cognitive states or tasks. Additionally, entropy does not account for the temporal dynamics of EEG signals, which are non-stationary and can change over time. Topographical maps of statistical features (i.e., MAD1, MAD2, and NMAD1) associated with reading, video, lecture, and discussion learning-style patterns are plotted in Figure 7. The color coding (orange to red) indicates the degree of brain activity, with orange suggesting low activity and red indicating high activity. From Figure 7, we observe that brain activity becomes more pronounced as certain activities, such as lectures or discussions, progress. During lectures, this increase in brain activity suggests that students are actively processing and integrating the information being presented over time. In contrast, reading sessions consistently show lower brain activity, which may indicate a more relaxed state or a different type of cognitive focus that requires less overall brain activation. Similarly, brain activity tends to rise during discussions, which likely reflects the active engagement and critical thinking required to analyze and respond to interactive content. When watching videos, the increase in brain activity over time indicates the ongoing processing of complex visual and auditory information. It is important to recognize that these general trends can vary widely among individuals and