# Comparative Analysis of Exemplar-Based Approaches for Students' Learning Style Diagnosis Purposes
## 2 Methods
### Related Works  
General knowledge may be modeled by statistical distributions [22]. The type of uncertainty that deals with assigning a probability of a particular state given a known distribution is referred to as aleatory uncertainty, which can perfectly be modelled using Bayesian networks. In Bayesian reasoning, uncertainty about the unknown parameter is represented by a probability distribution. This probability distribution is subjective and reflects personal judgment of uncertainty. Another type of uncertainty, epistemic uncertainty, is related to cognitive mechanisms of knowledge processing; therefore, some lack of knowledge exists in a sense that it is limited by knowledge processing mechanisms. Epistemic uncertainty is also known as systematic uncertainty, which is due to things one could in principle know but does not know in practice. The case-based reasoning method is applied for modelling epistemic uncertainty. It is based on situation-specific experiences and episodic knowledge [23]. For better decision making that includes both types of uncertainty, it may be appropriate to apply a combination of the CBR method and Bayesian network model. Therefore, literature on combining CBR with BN has been reviewed. Authors in [22] state that CBR and BN can be combined in parallel, in sequence BN -> CBR, and in sequence CBR -> BN. According to [23], in Artificial Intelligence, integration or a combination of various methods is a popular approach. Authors of [23] introduce the basic types of case-based reasoning integrations and indicate that case-based reasoning is usually combined with rule-based reasoning [24], model-based reasoning, and soft computing methods (i.e., fuzzy methods, neural networks, genetic algorithms). They distinguish five combinations of models depending on the degree of coupling between integrated components: standalone, transformational, loose coupling, tight coupling, and fully integrated. Authors of [23] also systematize intelligent methods for CBR integration. According to their research results, most of the efforts to integrate CBR with other methods or models have been input into CBR integration with rule-based reasoning systems. Authors in [25] present different architectures for CBR and BN combinations. In a parallel way, both methods use all of the input variables and then produce a classification independently. Authors in [25] introduce metareasoner, which decides during runtime which strategy to use (based on Bayesian network or on CBR) according to collected performance data. It is assumed that a method or model that performed well on a previous task will continue to do so in the future as learning is gradual. Authors in [26] describe a technique that integrates CBR and BN to build user profiles incrementally. Regardless of the diversity of various architectures, the main way of integrating CBR and BN is through a common space between CBR and BN parameters (a generalizing schema is presented by [27]), comparing new observations with all retrieved cases and trying to find the most similar ones. Authors in [28] conducted trials to combine CBR and semantic networks. The goal was to generate more detailed and meaningful explanations. Authors mentioned that, in this case, a challenge is the lack of a formal basis for the semantic network. Qualitative comparison between CBR and BN is made by [29]. It is stated that difficulties in BN are computational complexity, in the case of big data sets, and difficulty to obtain the initial parameters or add a new node. CBR is treated as an easier approach that has the main difficulty of describing cases by experts. Authors in [30] describe an optimized hybrid approach for fault diagnosis using a combination of BN and CBR. The Chi-square test is applied instead of heuristic algorithms for construction of BN and modification of its structure in the case of newly added nodes or removed ones.