Assessment software based on semantic and fuzzy logic

Designing the architecture of an intelligent decision support system for course selection based on ontologies

The increasing diversity of elective courses in curricula amid the digital transformation of universities exacerbates the problem of creating individual educational plans, as students face information overload and difficulty adhering to academic rules. Conventional statistically based recommender systems do not explain their decisions and ignore prerequisites and schedules. The goal of our paper is to design the architecture of an intelligent decision support system for course selection based on an ontological approach. In this paper, we presented a modular system structure in which modules for ontology preparation, student profile creation, rule checking, and meaningful course selection operate in a chain. We demonstrated that the ontological approach enables clear definition of disciplines, professional skills, and educational requirements. We found that the integration of logical rules and semantic analysis ensures rigorous verification of prerequisites and schedules while maintaining the flexibility of searching for suitable courses. We also demonstrated that the proposed architecture solves the problem of understanding by generating a clear explanation for each recommendation using educational language. The implementation of the proposed architecture will reduce the workload of teachers and improve the quality of independent course selection by students.

Probabilistic Linguistic Decision-Making Intelligent Model for College Digital Literacy Education Quality Evaluation

This research outlines the logic of dynamic evolution: digital competence, innovative teaching ability, and the symbiotic mutual uplift of university educators spiraling upwards, all towards the strategic goal of establishing an educational powerhouse. Indeed, this process involves developing qualitative assessments of higher education digital literacy, which can be framed as complex multi-attribute group decision-making problems. The present contribution in this respect is the modeling of multi-attribute group decision-making problems in a probabilistic linguistic environment through an integrated probabilistic linguistic exponential interactive multi-criteria decision making and multi-attributive border approximation area comparison method approach. Attribute weights are considered to be objective and expressed in terms of probabilistic linguistic term set data using the method based on the removal effects of criteria method. A case study on the assessment of the quality of digital literacy education confirms the feasibility and effectiveness of the proposed method.

LLM for the development of FCM

This article is about the development of a fuzzy cognitive map using a local large language model, and the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor and a fuzzy cognitive map is trained and evaluated.

A fuzzy logic model for diagnosis of university students' learning motivation

This article presents a fuzzy logic model for assessing university students' academic motivation. This model allows for the interpretation of motivational states as complex, ambiguous, and partially formalizable socio-pedagogical phenomena. The methodological framework is based on the measurement of students' academic motivation, including the approaches of T.O. Gordeeva, O.A. Sychev, and E.N. Osin, who developed the "Academic Motivation Scale" questionnaire using a sample of university students. A system of input linguistic variables, a database of expert rules, and an integrated motivation indicator are proposed. Fuzzy logic is shown to provide more flexible assessments compared to rigid scale procedures.

ONTOLOGICAL MODEL OF THE DOMAIN “INTELLIGENT ASSISTANT OF AN IT TEACHER”

An Interpretable and Hierarchical Positive-Prior-Driven Student Evaluation Enhancement Decision-Making Strategy

A scientific and reasonable student evaluation system plays a crucial guiding role in the development of higher education. However, for the current student evaluation theories and models, their operability is relatively weak. The constructed models have poor adaptability (weak robustness), lack cross-scenario research on guiding policies, and the evaluation results of the models are also relatively single. Therefore, this study proposes a hierarchical student evaluation enhanced decision-making model which is based on a zero-order fuzzy classifier as the construction unit. By introducing the administrative decision-making characteristics of the educational administration department (such as policy orientation indicators, teaching intervention suggestions, etc.), combined with a hierarchical fuzzy system and an improved ridge regression algorithm, it achieves the collaborative optimization of evaluation efficiency and interpretability. The experimental results show that the model demonstrates excellent classification performance and semantic interpretability on the degree student evaluation dataset, and can accurately predict students’ academic performance and support personalized educational decisions.