A Bayesian State-Space Approach to Dynamic Hierarchical Logistic Regression for Evolving Student Risk in Educational Analytics
Early detection of academically at-risk students is crucial for designing timely interventions that improve educational outcomes. However, many existing approaches either ignore the temporal evolution of student performance or rely on “black box” models that sacrifice interpretability. In this study...
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| 格式: | Article |
| 語言: | English |
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MDPI AG
2025-02-01
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| 叢編: | Data |
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| 在線閱讀: | https://www.mdpi.com/2306-5729/10/2/23 |