Translations and Adaptations of Assessment Tools in Early Childhood Education: A Scoping Review
Bibliographic record
Abstract
Objective: This scoping review sought to provide an insight into the key processes used in the translation and adaptation of assessment tools in peer-reviewed literature on assessment tools in early childhood education. Methods: Peer-reviewed articles published between 2012 and 2022 were identified via independent systematic searches using the databases Academic Search Complete, ERIC, Education Source, and APA PsycInfo. The articles included in this scoping were coded using a data extraction form developed for specifically for this study. Results: In the 56 articles selected, 33 reported forward translation; the analyses and findings of this scoping review were based on these 33. 30% of the articles did not report any methods of quality control methods. The most used quality control methods were back-translation only, and back-translation and expert review. 42% specified at what point the target population was included in the adaption process, and the preference was during pilot testing and focus groups. Regarding translators, 7 articles indicated the tools were translated by the researchers, 10 by independent bilinguals, and 3 utilized a team in the translation process. Conclusion: While cultural relevance and appropriateness were emphasized in these articles, there is limited information reported on what this process entailed. There were no specific or general guidelines reported. More focus should be placed on developing a culturally relevant protocol and related guidelines for the translation and adaptation process of assessment tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".