Pre-admission data (2018–2022) contain sufficient information to identify early patterns of dropout or course failure in first-year engineering mathematics subjects¶
Claim · All claims
Evidence · 1 study · associationalr2·q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
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q2 i? Pre-admission data from 2018–2022 contain sufficient information to identify early patterns of dropout or course failure, providing institutions a practical tool for retention interventions. → Fabian Santiago-Muñoz 2025
Evidence¶
Fabian Santiago-Muñoz 2025¶
Fabian Santiago-Muñoz, Mikel Larrañaga, Jon A. Elorriaga, Ana Arruarte. (2025). Identification of Patterns to Prevent the Failure of Engineering Students. Int J Artif Intell Educ 35:3356–3405. https://doi.org/10.1007/s40593-025-00502-9
q2 · i? · associational · r2
Summary of the study's clustering analyses of DEMRE pre-admission data from 2,292 engineering students at the University of Tarapacá (2018–2022), reported in the Introduction. The article states the data "contain sufficient information to identify early patterns of dropout or course failure."
"The analyses show that pre-admission data (2018–2022) contain sufficient information to identify early patterns of dropout or course failure, providing institutions with a practical tool for designing evidence-based retention interventions."
Discussion¶
Related Claims¶
- Academic failure in Introduction to Algebra and Introduction to Calculus ranged between 31% and 66% (2018–2022), and existing identification of struggling cases took 4–6 weeks — related
- Attendance, behavior, and course performance (the ABC indicators) reliably predict which secondary students are at risk of dropping out — a broader claim this one bears on
- AI-powered predictive analytics improves academic performance by enabling early identification of at-risk students and timely intervention — a broader claim this one bears on
- Ninth-grade early warning systems around course grades and attendance led to substantial improvements in grades, graduation rates, and test-measured learning gains — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
pre-admission-data-identify-early-dropout-failure-patterns |
title |
Pre-admission data (2018–2022) contain sufficient information to identify early patterns of dropout or course failure in first-year engineering mathematics subjects |
description |
Pre-admission data (2018–2022) contain sufficient information to identify early patterns of dropout or course failure in first-year engineering mathematics subjects |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2025 |
generated |
by process:wiki-ingest on 2026-10-27 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — fabian-santiago-muñoz-2025 |
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