Implications of Standardized Testing in Teacher Education: A Look at Perceptions, Experiences and Impacts on Teacher Candidates
Bibliographic record
Abstract
In 2019, the provincial government in Ontario released an announcement indicating all teacher candidates, regardless of their specialization or teachable subject areas, would be required to take a standardized math test in order to become certified to teach. This decision has implications for teacher candidates and the way they think about and experience assessment and evaluation. Assessment and evaluation are essential constructs in a teacher education program, because strong classroom-based assessment practices could have an impact on student learning and performance (Volante & Fazio, 2007). While standardized assessments exist outside of teacher education (e.g. university admissions exams), they are known to produce anxiety for students (Volante & Fazio, 2007). However, the impact these tests have on preservice teachers enrolled in education programs is not known. There is an urgent need to understand the perceptions, experiences, and impact of standardized testing on teacher candidates currently enrolled in Ontario teacher education programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".