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EVER framework for judging EdTech evidence

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Description

EVER organizes evidence quality along four aspects. Methodological quality asks whether methods are appropriately executed, described and justified. Outcome strength concerns sizable impact or predictive value, quantified via effect size or sensitivity/specificity. Generalizability is the extension of findings from one sample to the target population, requiring representativeness. Ethics and transparency ensure ethical design, users' well-being, culturally-responsive approaches and transparent data use.

Accounts

  • Four quality assurance aspects for judging EdTech evidence: methodological quality, outcome strength, generalizability, ethics and transparency: EVER organizes evidence quality along four aspects. Methodological quality asks whether methods are appropriately executed, described and justified. Outcome strength concerns sizable impact or predictive value, quantified via effect size or sensitivity/specificity. Generalizability is the extension of findings from one sample to the target population, requiring representativeness. Ethics and transparency ensure ethical design, users' well-being, culturally-responsive approaches and transparent data use. (Natalia Kucirkova et al. (2023))

Claims

Key Sources

  • Natalia Kucirkova, Garvin Brod and Nadine Gaab. (2023). Applying the science of learning to EdTech evidence evaluations using the EdTech Evidence Evaluation Routine (EVER). npj Science of Learning. https://doi.org/10.1038/s41539-023-00186-7
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Field Value
type research-method
id ever-framework-for-judging-edtech-evidence
title EVER framework for judging EdTech evidence
description EVER organizes evidence quality along four aspects.
status review
generated by process:settle-candidates on 2026-10-27
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