The impact of teacher attitudes and beliefs about large-scale assessment on the use of provincial data for instructional change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the quest to improve measured educational outcomes national governments across the OECD and beyond have instituted large-scale assessment (LSA) policies in their public schools. Controversy almost universally follows the implementation of such testing, related to such topics as: a) the uncertain quality of the tests themselves as psychometrics measures; b) the uses to which the data can and should be put; c) the unintended consequences of test-preparation activities and resulting score inflation; and d) the effects of high-stakes tests on students. Debates of this nature naturally involve and impact the attitudes and opinions of teachers related to their collection and use of these data. This paper examines the impact of these attitudes using both the qualitative and quantitative data from a large-scale research study on Canadian provincial assessment. Data were collected from nation-wide teacher surveys as well as interviews with teachers, administrators and district-level staff. Results show that teacher attitudes about these assessments are strongly correlated to classroom-level instructional change. Three attitudinal factors have significant effects on teaching (to) the provincial curricula, yet none significantly affects the use of less constructive instructional strategies also known as ‘teaching to the test.’ Specifically, the belief that large-scale assessment data have more appropriate uses and the belief that these data could lead to school improvement were significant factors in facilitating change. The implications of these findings are profound in that large-scale assessment policy cannot succeed even by its own standards without more buy in from teaching professionals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it