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Effectiveness of Artificial Intelligence Models for Predicting School Dropout: A Meta-Analysis

Truvace record
TRV-2026-2854raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
sha256
61d002804c84…check it

School dropout is a major concern in the educational systems of all countries. In recent years, artificial intelligence is playing an important role in predicting school dropout in the different educational stages of formal education. In this context, it is crucial to know that these predictions are accurate and understandable. This meta-analytic study aims to investigate the effectiveness of dropout prediction models conducted until May 2022. The databases used are Web of Science, Scopus, PubMed, ERIC, PsyInfo,…

The gain

A meta-analysis of 15 studies covering 199,015 students found AI models predicted school dropout with a pooled 91% accuracy (95% CI 89-93%), with decision trees reaching 95.3%.

What this doesn’t fix

The authors say the findings need confirmation, calling for more models to be applied to dropout prediction with larger numbers of participants.

Sources

1 on file
  1. Multidisciplinary Journal of Educational Researchdoi:10.17583/remie.13342
    Peer-reviewed2024-06-05

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Accuracy of artificial neural network models in predicting school dropout

    Proportion 91.8% (95% CI 90% to 93%)

    3 studies · search to May 2022

    The review’s own words
    The ANN model has a mean proportion effect size of 91.8% (95% CI = 90-93%) with significant variability

  • Accuracy of decision tree models in predicting school dropout

    Proportion 95.3% (95% CI 93% to 98%)

    5 studies · search to May 2022

    The review’s own words
    The DT model has a mean proportion effect size of 95.3% (95% CI = 93-98%) with significant variability

  • Accuracy of artificial intelligence models in predicting school dropout

    Proportion 91% (95% CI 89% to 93%)

    15 studies · 199,015 people · search to May 2022

    The review’s own words
    The accuracy shows a mean proportion effect size of 91% (95% CI = 89-93%).

  • Accuracy of random forest models in predicting school dropout

    Proportion 90.6% (95% CI 88% to 93%)

    3 studies · search to May 2022

    The review’s own words
    The RF model has a mean proportion effect size of 90.6% (95% CI = 88-93%) with significant variability

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

Cite this record

v1

Truvace Impact Record TRV-2026-2854, v1: “Effectiveness of Artificial Intelligence Models for Predicting School Dropout: A Meta-Analysis.” Truvace, 2026-11-12. /record/TRV-2026-2854 (accessed on the day you copy it). sha256 61d002804c847715…

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