## 2 Methods  
### Systematic Review of the Literature  
In the literature, not a single attempt was made to apply rule-based, statistical, and machine-learning methods for automatic student learning-style prediction. The authors of [20] mention existing Bayesian networks, the hidden Markov model, decision trees, NB tree classification, reinforcement learning, and other algorithms, and describe their experimentation with artificial neural networks, genetic algorithms, the ant colony system, and particle swarm optimization algorithms, using the Felder-Silverman learning style model to describe learning-style dimensions. Based on the results of these experiments, the authors concluded that different approaches perform best in different learning-style dimensions, but all the tested models outperformed the existing approaches.  
Experiments with deep neural networks for learning-style prediction are presented in one study [2]. The authors selected an optimal neural network of two hidden layers for student learning-style prediction. For training the model, 100 samples were used. The Felder-Silverman learning-style dimensions were predicted as well. The authors also applied principal components analysis (PCA) to investigate \"whether targets for each dimension might be explained by some descriptor (time and count)\" [21]. The authors also presented NN performance metrics, including a detection rate that was between 0.2 and 0.5, depending on the dimension.  
A comprehensive comparative study of multi-label classification (MLC) methods is presented by the authors of [16]--they evaluate 26 methods with 42 benchmark data sets, using 20 evaluation measures. The study includes both problem transformation and algorithm adaptation methods. The former group of methods decomposes the MLC problem into simpler problems that are addressed with standard machine-learning methods; the latter group of methods addresses the MLC problem in a holistic manner--it trains a model for predicting all labels simultaneously [16]. The authors selected the 8 best-performing methods: RFDTBR, AdaBoost.MH, ECCJ48, TREMLC, PSt, and EBRJ48 in terms of problem transformation, with RFPT and BPNN as the algorithm adaptation methods [16]. The authors state that MLC methods have weaknesses as well as strengths, depending on what metrics are used for the evaluation. RFPT, RFDTBR, EBRJ48, AdaBoost.MH and ECCJ48 were distinguished as the best-performing methods, considering all 18 evaluation measures.  
The authors of [1] developed a learning agent for classifying students' learning styles using an artificial neural network. Due to the lack of homogenous data, the data set was generated by stimulating students' learning behavior, based on the five inputs.  
Despite the fact that a plethora of papers exists about multiclass and multilabel classification (for example, [22; 23; 24; 25]), not many experimental works applying problem transformation approaches to multi-label classification for student learning-style prediction have been published. For example, the authors of [26] present a summary of works in the automatic detection of learning styles and do not mention problem transformation approaches for multi-label classification at all. According to their research, decision tree, random forest, k-nearest neighbor and other classifiers achieve 74-90% accuracy for various learning style dimensions.  
Many other student learning-style classification applications target mainly problem adaptation rather than problem transformation approaches.