The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2854
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:35:15.582731Z
status: published
lens: g_space
sector: education
headline: Effectiveness of Artificial Intelligence Models for Predicting School Dropout: A Meta-Analysis
dek: 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,…
gain_title: 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%.
problem_title: (none)
trace_subject: (none)
gain_reading: 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%.
gain_evidence: artificial intelligence models performed well (91%; 95% CI = 89-93%) in predicting dropout | the Decision Tree model significantly (95.3%; 95% CI = 93-98%) predicts dropout better than other models
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis of 15 studies with 199,015 participants found that AI models predicted school dropout with a pooled accuracy of 91% (95% CI 89-93%). The studies were published up to May 2022 and drawn from seven databases.

Decision tree models did best, at 95.3% (95% CI 93-98%), ahead of random forests, artificial neural networks, support vector machines, logistic regression and stacking ensembles. The authors say dropout is a major concern in education systems in every country, which is why the accuracy of such predictions matters.

The authors say more models should be tested on larger groups of students to confirm the findings.
limitation: The authors say the findings need confirmation, calling for more models to be applied to dropout prediction with larger numbers of participants.
tag: Evidence-backed gain
key_points: The review pooled 15 studies with 199,015 participants on AI dropout prediction. | Searches covered Web of Science, Scopus, PubMed, ERIC, PsyInfo, Dialnet and Scielo, with studies included up to May 2022. | Pooled accuracy across all AI models was 91% (95% CI 89-93%). | Decision trees scored 95.3% (95% CI 93-98%), better than the other model types compared. | The comparison models were random forest, artificial neural network, support vector machine, logistic regression and stacking ensemble. | The analysis used a random-effects proportions model with 95% confidence intervals.
rundown: The researchers searched seven academic databases for studies of dropout prediction models published up to May 2022. They analysed 15 studies with a combined 199,015 participants, using a random-effects proportions model with 95% confidence intervals.

Across all models the pooled accuracy was 91% (95% CI 89-93%). Decision trees scored 95.3% (95% CI 93-98%) and significantly outperformed random forests, artificial neural networks, support vector machines, logistic regression and stacking ensembles.

The authors conclude that more models should be applied in dropout research with larger numbers of participants to confirm these findings and improve the quality of education.
sources:
- peer_reviewed | Multidisciplinary Journal of Educational Research | https://doi.org/10.17583/remie.13342 | 2024-06-05
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

61d002804c847715ce82ac7e597573dbdcc8f209d12ec58465199b64ce749b72

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000