The PAMSSEM Approach for Multi-Attribute Group Decision-Making Using Single Valued Neutrosophic Sets: Applications to Evaluating the Effectiveness of College Students' Psychological Education

Abstract

The evaluation of the effectiveness of college students' psychological education involves using 
scientific approaches and tools to assess the actual impact of psychological education on improving students' 
mental health, fostering personality development, and enhancing psychological adjustment abilities. The 
evaluation covers aspects such as students' mental health status, the achievement of educational goals, the 
effectiveness of interventions, and student satisfaction. Through such evaluations, psychological education 
programs can be optimized to improve quality, help students better cope with psychological challenges in 
academics, life, and relationships, and promote their holistic development and healthy growth. The results also 
provide a scientific basis for continuous improvement of psychological education. The effectiveness 
evaluation of college students' psychological education is MADM. In this paper, the single-valued 
neutrosophic sets (SVNSs) and the average are employed to determine the attribute weights within decision
making processes. To address multi-attribute group decision-making (MADM) problems under SVNSs, the 
single-valued neutrosophic numbers PAMSSEM (SVNN-PAMSSEM) approach is proposed and 
systematically structured. This approach integrates the advantages of SVNSs, which effectively handle 
uncertainty, imprecision, and inconsistency in decision-making scenarios, with the PAMSSEM approach, 
known for its robustness in evaluating alternatives based on multiple attributes. To demonstrate the practicality 
and effectiveness of the proposed SVNN- PAMSSEM approach, a case study on the effectiveness evaluation 
of college students’ psychological education is presented. This example illustrates how the approach can be 
applied to assess the outcomes of psychological education programs, highlighting its applicability in real
world scenarios. Additionally, comparative decision analyses with other existing approaches are conducted to 
validate the SVNN- PAMSSEM approach further. The results confirm its superior performance in handling 
complex decision-making problems involving linguistic and fuzzy information, proving its value as a robust 
tool for MADM under SVNSs. 

DOI: 10.5281/zenodo.14537954