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Using the Bayesian Network Method to Evaluate the Effectiveness of College Students' Mental Health Intervention Strategies and Their Impact on Academic Performance

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Title: Using the Bayesian Network Method to Evaluate the Effectiveness of College Students' Mental Health Intervention Strategies and Their Impact on Academic Performance
Language: English
Authors: Wang Xiaohui, Cai Lianghui (ORCID 0009-0003-2166-8480)
Source: Journal of Psychoeducational Assessment. 2026 44(4):434-459.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 26
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Undergraduate Students, Academic Achievement, Mental Health, Bayesian Statistics, Risk, Intervention, Predictive Validity
DOI: 10.1177/07342829251393575
ISSN: 0734-2829
1557-5144
Abstract: Mental health and academic success are increasingly interdependent challenges for university students worldwide. This study developed and validated dynamic Bayesian models to predict academic performance and psychological risk across semesters using probabilistic approaches. We analyzed a cohort of 3,276 undergraduates and externally validated findings against an independent cohort of 5,112 students. Dynamic Bayesian Networks (DBN) and Bayesian Networks (BN) were trained using psychological scores (PHQ-9, GAD-7, PSS-10, CD-RISC) to model psychological risk and academic records to model academic outcomes. Ten-fold temporal cross-validation was conducted internally, and comparative analyses involved Random Forests, XGBoost, Deep Neural Networks, and TabTransformer models. DeLong's tests compared AUCs and permutation tests assessed Brier scores. Internally, BN achieved 91.0% accuracy, an AUC of 0.84 (95% CI 0.81-0.87), and a Brier score of 0.128, while DBN achieved 94.2% accuracy, an AUC of 0.86 (95% CI 0.84-0.89), and a Brier score of 0.124. In external validation, BN achieved 90.0% accuracy and an AUC of 0.88 (95% CI 0.85-0.90), and DBN achieved 92.0% accuracy and an AUC of 0.91 (95% CI 0.88-0.93). Top predictors included GPA, stress scores, depression scores, and intervention engagement. Posterior predictive p-values exceeded 0.44 across GPA and both outcome domains, indicating adequate calibration. Dynamic Bayesian modeling enables accurate, uncertainty-resilient prediction of both psychological risk and academic outcomes among university students.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1506066
Database: ERIC
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  Data: Using the Bayesian Network Method to Evaluate the Effectiveness of College Students' Mental Health Intervention Strategies and Their Impact on Academic Performance
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  Data: <searchLink fieldCode="AR" term="%22Wang+Xiaohui%22">Wang Xiaohui</searchLink><br /><searchLink fieldCode="AR" term="%22Cai+Lianghui%22">Cai Lianghui</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0003-2166-8480">0009-0003-2166-8480</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Psychoeducational+Assessment%22"><i>Journal of Psychoeducational Assessment</i></searchLink>. 2026 44(4):434-459.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Risk%22">Risk</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+Validity%22">Predictive Validity</searchLink>
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  Data: 10.1177/07342829251393575
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  Data: 0734-2829<br />1557-5144
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  Data: Mental health and academic success are increasingly interdependent challenges for university students worldwide. This study developed and validated dynamic Bayesian models to predict academic performance and psychological risk across semesters using probabilistic approaches. We analyzed a cohort of 3,276 undergraduates and externally validated findings against an independent cohort of 5,112 students. Dynamic Bayesian Networks (DBN) and Bayesian Networks (BN) were trained using psychological scores (PHQ-9, GAD-7, PSS-10, CD-RISC) to model psychological risk and academic records to model academic outcomes. Ten-fold temporal cross-validation was conducted internally, and comparative analyses involved Random Forests, XGBoost, Deep Neural Networks, and TabTransformer models. DeLong's tests compared AUCs and permutation tests assessed Brier scores. Internally, BN achieved 91.0% accuracy, an AUC of 0.84 (95% CI 0.81-0.87), and a Brier score of 0.128, while DBN achieved 94.2% accuracy, an AUC of 0.86 (95% CI 0.84-0.89), and a Brier score of 0.124. In external validation, BN achieved 90.0% accuracy and an AUC of 0.88 (95% CI 0.85-0.90), and DBN achieved 92.0% accuracy and an AUC of 0.91 (95% CI 0.88-0.93). Top predictors included GPA, stress scores, depression scores, and intervention engagement. Posterior predictive p-values exceeded 0.44 across GPA and both outcome domains, indicating adequate calibration. Dynamic Bayesian modeling enables accurate, uncertainty-resilient prediction of both psychological risk and academic outcomes among university students.
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    Subjects:
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        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Mental Health
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      – SubjectFull: Risk
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      – SubjectFull: Intervention
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      – SubjectFull: Predictive Validity
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