Formative assessment, growth mindset, and achievement: examining their relations in the East and the West
Bibliographic record
Abstract
Both formative assessment and growth mindset scholars aim to understand how to enhance achievement. While research on formative assessment focuses on external teaching practices, work on growth mindset emphasises internal psychological processes. This study examined the interplay between three formative assessment strategies (i.e. sharing learning progressions, providing feedback, and instructional adjustments) and growth mindset in predicting reading achievement using the PISA2018 data. We focused specifically on samples from the West (the United States, the United Kingdom, Ireland, Canada, Australia, and New Zealand) and the East (Mainland China, Hong Kong SAR, Macau SAR, Chinese Taipei, Japan and Korea) which comprised of 109,204 15-year old students. The results showed that formative assessment strategies were positively, albeit weakly, related to a growth mindset in the East, but not in the West. In contrast, growth mindset was positively related to reading achievement only in the West, but not in the East. The impacts of different formative assessment strategies on reading achievement demonstrated cross-cultural variability, but the strongest positive predictor was instructional adjustments. These findings highlight the potential synergy between formative assessment and growth mindset in enhancing academic achievement as well as the importance of cultural contexts in understanding their roles in student learning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".