## 1 Introduction
These methods must be combined into a coherent and logical methodology, providing the solution to learning-style identification. Later learning style(s) may be used for personalization of the VLE. Of the many papers on learning personalization, not a single author has addressed the lack of experimental work in the area of automatic student learning-style prediction and the personalization of learning environments. Trying to fill this gap, we conducted experiments for determining students' learning styles, using supervised machine learning methods. Students' learning-style identification is related to multi-learning problems--multiclass, multi-label, or multi-output classification methods may be applied to solve them, depending on the specific need [18]. Multi-label classification is most likely to be used in practical applications since a student is very rarely characterized by a single learning style--usually, several dominant learning styles interact for each learner. Therefore, this paper describes our experiments with multi-label classification solutions. Another learning-style modeling aspect that requires consideration is interpretability. Interpretability is about the extent to which a particular cause and effect can be observed within a system. It is not enough to have the probability values of a student's learning style--teachers, students, and other stakeholders may want or need to understand the reasoning behind the predictions and decisions made by the model. In this way, for example, teachers could prepare learning objects of certain types (video, text, or audio) or develop learning content that is appropriate to the group of students, who may be characterized by a particular learning style. In this context, it is also important to understand the difference between interpretability and explainability. Explainability is the extent to which the internal mechanics of a deep learning system can be explained in human terms. In turn, interpretability is the capacity to consistently explain (interpret) model's result without trying to know the reasons behind the scenes. The model agnostic SHAP (Shapley additive explanations) method is proposed to explain NN's individual predictions for those cases when the input data features are not correlated or weakly correlated. When proposing multi-label classification methods for students' learning-style detection, more sophisticated practical cases were also taken into account: classifier chains [19] and other methods for correlating labels, asymmetric Shapley values for incorporating causal knowledge into model explainability, etc. Besides the above-mentioned points, the experimental work described in this paper deals with the input data preprocessing techniques (data scaling, under/oversampling in the case of imbalanced data sets, imputation strategies in the case of missing input data values, etc.) and the methods used to prevent the model from overfitting (for example, the dropout approach or regularization techniques). This research and the pilot experimental results led to our creating a set of procedures and methods for identifying a student's learning style and personalizing the virtual learning environment accordingly. The rest of the paper is organized as follows: firstly, the existing categories of multi-label approaches that may be applied in students' learning-style classification are listed and briefly outlined; secondly, the model agnostic SHAP method that we propose for model interpretation is introduced. Then, the results of our experiments with multi-label approaches are described in Section 3. After an exploratory analysis of the training data set, we describe the experiments performed with the _OneVsRest_ multi-label classifiers that use various base estimators (the problem transformation approach); based on the results of these experiments, the generalization quality of the classifiers is then compared, using such performance metrics as the Hamming loss, precision, recall, and the F1 score.