Assessment Vol.2-193 VALUES & EXAMS: MEASURING THE QUALITY OF TEACHER-GENERATED ASSESSMENT
Bibliographic record
Abstract
Values/beliefs in measurement are highly controversial and rarely explored. Yet is a vital part of classroom assessment. Based on a quantitative research study, this paper examines mathematics teacher’s values in relation to the exams they create. The framework is based on Messick’s four faceted model. The results help identify areas of strength and weakness in the quality of teacher assessment. Values and beliefs play an integral role in teachers ’ lives. Teachers determine on a daily basis what students should learn, in which way and how to assess learning. This is all accomplished while staying within the confines of teachers ’ professional ethos and ministry guidelines. This paper examines the cohesion of these relationships to analyse the quality of teacher-generated assessment. Perspective Teachers develop final assessments that measure student’s ability within a course. This is considered "assessment of learning. " The Western and Northern Canadian Protocol [WNCP], (2006) state: "The purpose of assessment of learning is to measure, certify, and report the level of students ’ learning, so that reasonable decisions can be made about students " (p.56). Thus exams,
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".