Big Data Analytics for Mental Health Education: A New Framework for University-Level Evaluation under Linguistic Confidence Interval Neutrosophic Numbers

In the contemporary educational ecosystem, mental health has emerged as a pivotal aspect of holistic student development. The integration of big data analytics offers a transformative path for evaluating the effectiveness of mental health education at universities. This study proposes a comprehensiv...

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Bibliographic Details
Published inNeutrosophic sets and systems Vol. 83; pp. 868 - 882
Main Author Zhu, Hong
Format Journal Article
LanguageEnglish
Published Neutrosophic Sets and Systems 15.08.2025
University of New Mexico
Subjects
Big data
big data analytics
Decision-making
Detectors
Educational evaluation
Health education
linguistic confidence interval neutrosophic numbers
Medical personnel
Mental health
mental health education
Occupational health and safety
Rankings
Training
Online AccessGet full text
ISSN2331-6055
2331-608X
DOI10.5281/zenodo.15207939

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Summary:In the contemporary educational ecosystem, mental health has emerged as a pivotal aspect of holistic student development. The integration of big data analytics offers a transformative path for evaluating the effectiveness of mental health education at universities. This study proposes a comprehensive framework that merges data-driven tools with pedagogical strategies to assess key indicators of mental health support efficacy. Ten criteria--including accessibility, awareness, analytics integration, and data ethics--are used to evaluate a diverse set of intervention alternatives ranging from Al-based detection systems to immersive VR training. By applying a structured multi-criteria decision-making (MCDM) approach, this research identifies optimal strategies that ensure privacy, responsiveness, and personalized support. The findings not only guide administrators in refining their mental health initiatives but also contribute to academic research by introducing a scalable evaluation model that can adapt across institutional contexts. We use the Linguistic Confidence Interval Neutrosophic Numbers (LCINN) to overcome uncertainty and vague information. We use the EDAS method to rank the alternatives and select the best strategies. Keywords: Linguistic Confidence Interval Neutrosophic Numbers; Big Data Analytics; Mental Health Education.
ISSN:2331-6055
2331-608X
DOI:10.5281/zenodo.15207939