An Examination of Preservice Teachers' Simulated Classroom Assessment Practices
Bibliographic record
Abstract
The comments made by 127 preservice teachers (PTs) in the Faculty of Education, University of Victoria as they compiled portfolios on three hypothetical grade 5 children are examined. The PTs were asked to record their comments in the form of a journal throughout the term. At the end of the term the PTs' comments were collected, transcribed, and the resultant data analyzed using Atlas/ti. The data were analyzed to examine the types of decisions the PTs made about the hypothetical children. Two main patterns of decisions were taken by the PTs. Most seemed to follow a fairly logical set of procedures, formulating criteria to evaluate the assignments and then applying them. A few appeared to make judgments that may have been unsound, however, commenting on the children's quality of life or designating them as having special educational needs when the evidence presented did not support such conclusions.
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How this classification was reachedexpand
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 | 0.006 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".