# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 1 Introduction
In [33], a neuro-fuzzy model was proposed to predict the students' perceptions of problem solving skills. In this paper, the authors used the neuro fuzzy approach (ANFIS) in modeling students' perceptions. Then, they used correlation and regression to examine the relationship between students' skills and CPS characteristics. In [34], a fuzzy preference programming approach was introduced to a university department evaluation. In this paper, the authors proposed a fuzzy preference programming approach for data envelopment analysis. The authors applied their approach in one of the higher education institutions in the Philippines. In [35], the authors provided a system for monitoring the students' activity and performance. They used a fuzzy Linguistic Summarization (LS) technique to extract linguistically interpretable rules from students' data describing prominent relationships between activity/engagement characteristics and achieving performance. They applied their system in analyzing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master's-level module. In [36], the authors proposed a conceptual framework to examine testing as a communication process between assessment systems and the testing of English language learners (ELLs). They used probabilistic approaches based on generalizability theory for examining testing as a communication process between assessment systems and ELLs examination. In [37], the authors investigated different types of distance and similarity measures for nested probabilistic-numerical linguistic term sets. They proposed a family of distance and similarity measures between two NPNLTSs with their properties and then established a variety of weighted distance and similarity measures between two collections of NPNLTSs. They applied their approach to evaluating medical treatment. Generally nested probabilistic linguistic term sets have received attention form researchers, especially in the area of dealing with multidimensional and uncertain characteristics; for example, in [38], the authors proposed the interactive multi-attribute decision-making model. They constructed their model by combining Dempster-Shafer theory (DST) with the concept of probabilistic linguistic term set. The authors used evidential Best-Worst method and maximum entropy principle to capture the weight and interaction of attributes, and they applied a Choquet integral for aggregating the different ELTSs. The authors implemented their model to select the supplier for medical devices. In [39], the authors provided a method to make a decision in the fields of multidimensional, complex, and uncertain characteristics. They applied a VIKOR method to extend the concept of nested probabilistic-numerical linguistic term sets incorporating the investment decision. They provided their solution based on the process of the VIKOR method with the NPLTS, using six steps including alternatives and the attribute vectors for determining, calculation, and sorting. In [40], a nested probabilistic-numerical linguistic term set (NPNLTS) was introduced, in which the quantitative and qualitative information were considered to enhance the fuzzy linguistic approach in handling multi-attribute group decision making. They tested this approach by providing an application for strategy initiatives of the HBIS Group on Supply-side Structural Reform. Although most of the above research works have used part of propositional logic in their work, there are still some research works concerned with providing ideas, concepts, or theoretical background in the area of propositional logic and its implication with fuzzy concepts. For example, in [41], three kinds of linguistic truth-valued fuzzy negation were proposed. The paper introduced linguistic truth-valued fuzzy negation operators and proposed the operation method based on linguistic truth-value logic systems. In [42], the authors presented an alternative approach for describing fuzzy propositional logic.