## 4 Results and Discussion  
The statistical significance and variability of various EEG features during distinct cognitive tasks (reading, discussion, lecture, and video) are comprehensively examined in Figure 4. A one-way ANOVA test was conducted to assess the effectiveness of features in differentiating between teaching methods. Features with \(p\)-values < 0.05 are highlighted, and their corresponding F-values indicate the strength of their discriminative power. There are statistically significant differences (\(p\) < 0.001) between MAD1, MAD2, and NMAD1, suggesting that the EEG signals exhibit differing levels of dispersion throughout the various activities. Significant differences are highlighted by the high F-values associated with these characteristics (836.89, 744.59, and 719.29, respectively). The KFD and PFD features demonstrate substantial differences (\(p\) < 0.001) in their respective F-values of 1577.9 and 1079.3, which emphasize the non-linear complexity of the underlying brain activity captured by FD. Significant variations in mAmp, H1, H2, and H3 are observed (\(p\) < 0.0001, F-value: 81.71, 1395.26, 1353.21, 1507.48, respectively), indicating that cognitive tasks possess unique spectral amplitude properties. Entropy measures, namely, AE, and SE, exhibit substantial F-values (671.66 and 544.83) that indicate significant differences (\(p\) < 0.001). These differences underscore the varied patterns of regularity and complexity observed in EEG signals throughout cognitive activities. In brief, the ANOVA results underscore the intricate character of neural reactions, illuminating the distinct EEG characteristics that distinguish cognitive initiatives, and furnishing significant data for comprehending cerebral intricacies through participation in reading, discussion, lecture, and video. Table 2 presents the average classification performance for reading, discussion, lecture, and video learning styles. Performance scores are shown for each feature classification technique, including the combination of all the feature sets. The highest scores across the feature sets are highlighted in bold. The mean class accuracies for the four teaching methods are reading (79.29%), video (82.79%), lecture (80.31%), and discussion (72.28%); the video class exhibits the highest performance, with a mean class accuracy of 82.79% across the three models. The majority of the feature sets perform reasonably well with average classification accuracy (\(\geq\)65.44%), precision (\(\geq\)65.52%), recall (\(\geq\)65.45%), F1-score (\(\geq\)65.37%), and AUC (\(\geq\)0.76). This is interesting, as it suggests a complex relationship between learning styles and many properties of EEG signals. As illustrated in Table 2, the performance of EEG statistical features is higher for classifying learning styles relative to other features when using RF, KNN, or MLP classifiers. One of the possible reasons that statistical features outperform could be that they utilize more time domain information. These results are also broadly consistent with previous research highlighting the significance of statistical features for recognizing the hidden internal discriminative information needed for learning-style identification [39]. Furthermore, the statistical feature set delivers classification results with the lowest SD of accuracy, precision, recall, F1-score, and AUC, showing that they perform more consistently than other features set in this study. This suggests greater stability or reliability of statistical feature sets for unveiling learning-style patterns. Furthermore, we found that no prior research employed a machine learning approach specifically for classifying learning-style EEG data in real classroom settings. However, several studies have utilized EEG features to construct recognition models for learning-style classification based on the Felder-Silverman model, which divides learning styles into four dimensions: information processing, perception, input, and understanding. For example, [PERSON] et al.