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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".