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

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.