Pre-service Teachers’ Thinking about Student Assessment Issues
Bibliographic record
Abstract
Pre-service teachers are typically concerned with student assessment and view related issues through varied experiences and backgrounds. Understanding how they think about assessment issues within the current educational context helps to better prepare them. In this paper we describe pre-service teachers’ thinking about assessment issues, the theories that underlie their thinking, and how it evolves as a result of using an introspective critical approach called the objective knowledge growth framework. The framework combines the diary and the think aloud protocol and brings pre-service teachers to identify initial assessment problems, propose tentative solutions, and challenge their solutions. Thirty-one pre-service teachers took part in this study and received a one hour workshop on the use of the introspective approach to solve their self-identified assessment issues. Brookhart’s ‘Tensions in Classroom Assessment Theory and Practice’ framework was then used to explore the theories at play when pre-service teachers go through their problem solving processes. The participants identified group work, test failure, accommodation, fairness, multiple assessment opportunities, and academic enablers as key areas of concern. Particularly notable in the study, was the greater importance attached by the pre-service teachers to assessment for classroom management, student motivation, and social justice purposes, than to support learning. The analysis of these concerns using Brookhart’s framework and of the reasoning about them suggests that the intersection of measurement, psychological, and social theories continues to impact the decision making process regarding assessment.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".