A Neutrosophic d-Algebraic Framework for Modeling Psychological Education Modes of College Students in the Big Data Era - Xia Yan (Neutrosophic Sets and Systems)
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.