Self-explanation quality correlated with prior knowledge and post-test knowledge but not with learning gains¶
Claim · All claims
Evidence · 1 study · causalr2·q3·i3large
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person
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q3 i3 Self-explanation quality correlated significantly with prior knowledge (r = .24) and post-test knowledge (r = .37), but not with gain scores (r = .16, p = .188). → Bichler 2022
Evidence¶
Bichler 2022¶
Bichler, S., Stadler, M., Bühner, M., Greiff, S., & Fischer, F. (2022). Learning to solve ill-defined statistics problems: does self-explanation quality mediate the worked example effect? Instructional Science. https://doi.org/10.1007/s11251-022-09579-4
q3 · i3 · causal · r2
Correlational analysis of the new self-explanation data (N = 74). Self-explanation quality and concept application strongly correlated (r = .72, p < .001); the article continues with self-explanation quality as the mediator of interest. None of the correlations with covariates and moderators except prior knowledge were significant.
"Self-explanation quality correlated significantly with prior knowledge r = .24, p = .042 and post-test knowledge r = .37, p = .001, but not with gain scores r = .16, p = .188."
Discussion¶
Related Claims¶
- Self-explanation quality does not mediate the worked example effect on learning gains in ill-defined statistics problems — related
- Prior knowledge and working memory capacity did not moderate the worked example effect — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
self-explanation-quality-correlations-null-gain |
title |
Self-explanation quality correlated with prior knowledge and post-test knowledge but not with learning gains |
description |
Self-explanation quality correlated with prior knowledge and post-test knowledge but not with learning gains |
status |
review |
evidence_strength |
weak |
newest_evidence |
2022 |
generated |
by process:wiki-ingest on 2026-10-18 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — bichler-2022 |
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