# Comparative Analysis of Exemplar-Based Approaches for Students' Learning Style Diagnosis Purposes
## 2 Methods
### Proposed Approach for Students' Learning Style Prediction
#### 2.6.4 Interpretability  
Machine learning algorithms build mathematical or/and statistical models based on sample data (\"training data\"). Relying on patterns and inference, these models make predictions, diagnoses, or decisions on specific tasks without using explicit instructions. Machine learning conditions improve products, processes, and research. However, computers usually do not explain their predictions, which is a barrier to the adoption of machine learning [53]. Interpretable models and methods for interpretation enabling humans to  
Figure 3: Bayesian case model [51] for students’ learning style prediction. comprehend why certain decisions or predictions have been made were developed to cope with interpretability problems. Authors in [53] define interpretability as the degree to which a human can understand the cause of a decision. Authors in [54] treat interpretability as the degree to which a human can consistently predict the model's result. One can describe a model as interpretable if he/she can comprehend the entire model at once. Authors in [55] point out that in explaining the predictions of a machine learning model, we rely on some explanation method, which is an algorithm that generates explanations. An explanation usually relates the feature values of an instance to its model prediction in a humanly understandable way [56]. The main questions are \"What\", \"How\", and \"Why\"; authors in [53] emphasize answers to \"Why\". In spite of that, in some cases non-causal explanations exist (for example, answering the question 'what happened'), and causality is the most important explanation; that is, an explanation should refer to causes. Probabilistic theories that BCM are based on state that event X is a cause of event Y if and only if the occurrence of X increases the probability of Y occurring. BCM, which is proposed for students' learning style diagnosis, uses exemplar-based explanations. It generates a prototype (example (actual data point) that best represents the cluster) and subspace (set of important features) for each cluster. In subspaces, the binary variable has value 1 for important features. Authors in [53] define a prototype as a data instance that is representative of all the data. In general, the importance of a feature is defined as the increase in the prediction error of the model after we permuted the feature's values, which breaks the relationship between the feature and the true outcome [53]. There are permutation feature importance algorithms to train the feature importance. Depending on the purpose, they may use a training data set or test data set for training feature importance. Here it is necessary to emphasize the fundamental difference between generative and discriminative models, including the difference in the way it defines what important features are. As [PERSON] explains, in BCM important features are whatever the particular cluster has in common (and also the rest has high likelihood in the entire generative process), and unimportant features are the ones where if they are changed arbitrarily, their cluster membership does not change. Thus, prototype and subspace form an explanation of a cluster. It is noteworthy again that in BCM, which uses an exemplar-based approach, an explanation consists of real data examples. For students' learning style modelling using BCM, explanations for each cluster will consist of the following:  
* prototype presented as log of behavioral activities of the student; this log best represents the cluster; in turn, a cluster represents learning style, and its features (for learning style modeling-behavioral activities) have cluster labels;
* subspace of important features, i.e., behavioral activities that have been performed most frequently in the virtual hypermedia learning environment by students whose learning style corresponds to the style represented by the cluster (Figure 4). Besides prototypes, influential instances, criticisms, adversarial examples, etc., also may be used for explanations.