A Neutrosophic d-Algebraic Framework for Modeling Psychological Education Modes of College Students in the Big Data Era

Abstract

 In the era of big data, college students face complex psychological challenges 
influenced by high-volume, high-velocity, and often conflicting information. Traditional 
educational psychology models often fail to capture the uncertainty, inconsistency, and 
vagueness in students' emotional and cognitive responses. In this paper, we propose a 
novel mathematical framework using neutrosophic d-algebra, MBJ-neutrosophic ordered 
subalgebras, and NeutroGeometry to model the psychological dynamics of college 
students. The model incorporates the Law of Included Infinitely-Many-Middles to 
represent continuous psychological fluctuation and uncertainty. Our approach introduces 
multi-layered logical structures with precise mathematical definitions, supported by 
equations and fully calculated examples. This framework allows for more realistic 
modeling of student behavior, incorporating degrees of truth, indeterminacy, and 
falsehood. The proposed system is designed to support adaptive, data-driven 
psychological education modes that can better serve students in dynamic digital 
environments.

DOI: 10.5281/zenodo.15843614