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Student achievement prediction and auxiliary improvement method based on fuzzy decision support system

Gardado en:
Publicado en:Discover Artificial Intelligence vol. 5, no. 1 (Dec 2025), p. 63
Publicado:
Springer Nature B.V.
Materias:
Machine learning
Behavior
Accuracy
Students
Data mining
Academic achievement
Personalized learning
Decision making
Neural networks
Support vector machines
Questionnaires
Cognitive models
Data processing
Variables
Feature selection
Data analysis
Decision support systems
Engineering
Humanities
Mental health
Decision trees
Distance learning
Teachers
Education
Fuzzy logic
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Resumo:This paper proposes a student achievement prediction model based on fuzzy decision support system (FDSS), which uses multi-dimensional data to accurately predict student achievement. A prediction model including students’ historical achievement, learning behavior and class participation is constructed, and fuzzy reasoning mechanism is used to deal with the uncertainty and fuzziness of data. Experimental results show that the FDSS model has high prediction accuracy on multiple data sets, and the error range is controlled at a reasonable level. Compared with traditional machine learning methods, FDSS model has advantages in prediction accuracy and generalization ability. This paper also discusses the application effect of the model in different subjects, grades and regions of the student data, and puts forward the model optimization and improvement strategies. Fuzzy decision support system provides an effective solution for student achievement prediction in the field of education, and provides scientific decision support and personalized guidance for education decision-makers and teachers.
ISSN:2731-0809
DOI:10.1007/s44163-025-00308-7
Fonte:Research Library