Practice to Theory: Making Connections Between Assessment and Evaluation Through a Reflective Practice Assignment in the Bachelor of Education Program
Bibliographic record
Abstract
In self-study, researchers explicitly identify how their practices add to the body of knowledge in teacher education (Vanassche & Kelchtemans, 2015). This self-study aims to document and analyze some of my learning as an early career teacher educator in Ontario, Canada. Being cognizant of the well-researched theory-to-practice gap in teacher education and the potential for someone with recent field experience to readily share “tips and tricks” without deeply connecting to theoretical perspectives (Hibbert et al., 2022), I document and analyze my experiences shifting the structure of a Bachelor of Education course assignment. The purpose of the shift is to help support teacher candidates in drawing connections between their assessment practices on placement and assessment theory and policy. Through this reflective essay, I share my instructional decisions, how I enacted them, and reflect forward. Because teacher candidates demonstrated clear connections about how their assessment decisions aligned with theory, policy, and curriculum documents, I believe this assignment was a successful addition to the course.
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.
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 | 0.116 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".