## 4 Discussion and the Methodology Proposed  
Mechanisms to Select the Appropriate Machine Learning Methods for Student Learning-Style Prediction and Interpretation  
Based on the findings from our literature-based research, and using the results of our experiments, we see that the different preprocessing, classification, or clustering and interpretation methods have to be applied depending on the peculiarities of the data set (imbalanced or not, sparse or not, having missing values or not, having correlated features or not, having correlated labels or not, etc.) and the specifics of the multi-label classification and model interpretation tasks that need to be performed. We identified from our experiments that the _MultinomialNB_ and _GradientBoostingClassifier_ performed best when applying the _One-vs-all_ strategy. The authors of [46] state that \"the binary relevance method, specifically _One-vs-all_, yields the best result in the real data analysis, but has the drawback  
Figure 11: Stacked SHAP values and cluster data.  
Figure 12: The SHAP force plot for the “sensing” learning style.  
of neglecting correlation among labels. The label powerset considers correlations between labels indirectly but has the drawback of not including all possible label combinations in the model-fitting process, which leads to overfitting of the training set\". Based on the literature review, we know that the ensemble of classifier chains (ECC) algorithm is very effective for multi-labeled learning objects; therefore, it is proposed as the best classification algorithm for data sets with correlated labels, followed by RAKEL, ML-kNN, and finally, EPS [47]. ML-kNN performs the worst, but it has the advantage of producing a ranking of the labels. Based on the work in [48], \"the ECC has the best performance in all measures, followed by EPS and RAKEL. However, with respect to the Hamming loss, the RAKEL and ML-KNN are the best-performing methods, followed by ECC. As for the label-based evaluation measures, the best-performing methods are ECC and BR\". Empirical evidence published by [49] shows that ML-kNN performs the best, followed by RAKEL, then followed by the classifier chain and binary relevance. The authors of [50] emphasize that BR methods are quite appropriate for a not very large number of labels and that they have strong limitations regarding the use of label relationship information. They propose the BR+ approach, to improve the multi-label classification performance in datasets that do not have a very high label space dimensionality, trying to discover label dependency. The authors of [51] experimentally prove that, for data sets with correlated labels, the LC and PS methods give better prediction results than the BR method as the LC and PS consider label correlation during the transformation from a multi-label to a single-label dataset. A comprehensive comparison between various problem transformation methods is presented in [52]. It states the merits and/or disadvantages of the classification methods as the ability to utilize the available unlabeled data for classification, the ability to take label correlations into account, and the speed and computational complexity of each problem transformation method. Complexity comments on the multi-label classification algorithms are also presented by the authors of [24].  
Various model interpretation approaches may be chosen for explaining the predicted students' learning styles as well. Except for those cases when we have inherently interpretable models (such as BCM) with imposed interpretability constraints, model-agnostic explanation methods may be used for interpreting the model. These methods are considered post hoc since they are decoupled from the black box [35].  
Systematizing the research results, we present the procedure that might be followed for the selection of methods for multi-label students' learning-style detection and interpretation (Figure 13).