A Review of Multi-Source Behavioral Data-Driven Dynamic Assessment and Intervention for Vocational College Students’ Mental Health

DOI: https://doi.org/10.62517/jhve.202616410

Author(s)

Liuliu Sheng

Affiliation(s)

Department of Student Affairs, Taizhou Vocational and Technical College, Taizhou, Zhejiang, China

Abstract

Mental health problems among vocational college students are becoming increasingly serious and increasingly hard to detect, and one-off assessments are no longer able to keep up with the need for ongoing monitoring. This review looks at how multi-source behavioral data are integrated to support dynamic risk assessment and intervention decision-making for this population. Drawing on bibliometric and content analysis, we reviewed how data such as psychological scales, campus card spending, attendance records, and online behavioral trajectories are collected and integrated, along with the machine-learning risk models built on top of them, and how well these models' warning signals actually perform in practice. The evidence shows that fusing multiple data sources can bring to the surface hidden indicators that might otherwise be overlooked—irregular sleep patterns, sudden changes in spending, and social withdrawal—which are hard to spot when examined individually. This, in turn, improves the accuracy of risk identification and shifts intervention from “post-hoc remediation” toward evidence-based decision-making. The "university-college-class" link chain is jointly supported by families, schools, hospitals, and the community to drive this change. There are still some problems; namely, privacy protection, data heterogeneity and model interpretability have not been fully addressed. Nevertheless, the construction of a full-cycle, multi-dimensional monitoring system in conjunction with an intelligent intervention support platform remains the most promising path to enhancing the scientific basis of mental health services for vocational college students.

Keywords

Vocational College Students' Mental Health; Multi-source Behavioral Data; Dynamic Risk Assessment; Intervention Decision Support; Precision Intervention.

References

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