A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts

Abstract

This paper presents a new approach called MultiNeutrosophic Offset Structures 
(MN-OS) to improve students’ Mental Health Literacy (MHL). The method combines 
ideas from psychology, education, sociology, and computer science to provide a more 
personalized and accurate way of understanding and improving students’ mental health 
knowledge. Unlike traditional models, MN-OS allows the levels of truth (T), uncertainty 
(I), and falsehood (F) to go beyond normal limits (above 1 or below 0), which helps capture 
extreme cases  like strong misconceptions or outstanding understanding. The model 
represents students’ knowledge as points within a Multiple space and uses new 
mathematical tools  such as custom operators, matrices, and clustering algorithms  to 
group students with similar MHL profiles. Based on these groups, targeted interventions 
can be designed to address specific needs. To test the model, a simulation was conducted 
with 300 students. The results showed 94% accuracy in clustering and an 87% 
improvement in MHL outcomes after the interventions. This demonstrates that the MN
OS framework is effective, flexible, and scalable for improving mental health education in 
diverse student populations. 

DOI: 10.5281/zenodo.15708537