Mapping Measurement Scales for the Assessment of Learning Environments
Bibliographic record
Abstract
This article aims to map the scales validated in the international literature for the assessment of learning environments. A systematic literature review was carried out in articles from the web of Science database in the period from 1970 to 2020. After completing the three stages proposed by Tranfield, Denyer, and Smart (2003), 94 articles were selected to compose the final sample. Most of the articles analyzed were published from 2011 to 2020 (54.2%). Barry J. Fraser is the author who published most of the articles from the analyzed sample (10), which confirms his representativeness in studies involving the subject. Most articles involve the area of Education and Educational Research (78.7%). Twenty scales used to assess the learning environment were identified. The What Is Happening In This Class? Instrument is the most cited and most used in the articles analyzed. We also identified that the six dimensions of this instrument appear in four or five different scales, which reveals its contribution to the construction of assessment scales. The study results can assist in the development of a multidimensional scale of learning environments.
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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 | 0.036 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".