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Area of Science:

  • Educational Psychology
  • Data Science in Education
  • Academic Screening

Background:

  • Accurate prediction of student academic performance is crucial for timely intervention.
  • Previous research explored naive Bayesian approaches combining academic and social-emotional-behavioral (SEB) data.
  • Replication studies are vital to validate predictive models in educational settings.

Purpose of the Study:

  • To conceptually replicate Pendergast et al.'s (2018) study on the diagnostic accuracy of a naive Bayesian approach.
  • To evaluate the combined predictive power of academic and SEB screening data against state achievement tests.
  • To assess the utility of a naive Bayesian model in differentiating student risk levels for intervention.

Main Methods:

  • A naive Bayesian approach integrated academic (aimswebPlus) and SEB (SEB Risk Screener) data.
  • Data from 5753 students in Grades 3-5 across 19 elementary schools were analyzed.
  • Predictive performance was compared against state achievement test scores (Missouri Assessment Program).

Main Results:

  • The naive Bayesian approach showed similar diagnostic accuracy to individual aimswebPlus measures.
  • A high percentage of students (65%-87%) remained undifferentiated, indicating unclear risk status.
  • The study failed to replicate the findings of the original Pendergast et al. (2018) study.

Conclusions:

  • The naive Bayesian approach, while integrating multiple data sources, did not significantly improve differentiation of student risk compared to individual screeners.
  • The high proportion of undifferentiated students suggests limitations in the model's practical application for intervention decisions.
  • Further research is needed to refine predictive models and understand factors contributing to undifferentiated outcomes in student assessment.