Walking Our Talk About Assessment With Preservice Teachers
Bibliographic record
Abstract
In this reflective study, six members of a Faculty of Education implemented or adapted research-informed assessment practices in their university Bachelor of Education teaching. These practices included aligning university course outcomes to assessment, separating achievement on university course outcomes from achievement of non-academic outcomes, collaboratively creating achievement indicators for provincial curriculum outcomes, co-constructing criteria with university students for assignments, setting up opportunities with university students for peer feedback before an assignment is submitted for marking, and administering and marking a test according to research-suggested practices. This article describes the implementation of these practices and analyzes the challenges and successes experienced as these teacher educators strove to model assessment practices that are expected of preservice teachers when they enter the profession. The primary goal of implementation was to increase congruence between teaching and practice in terms of assessment. Keywords: assessment; preservice teacher learning; self-study
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".