Beyond ‘driving’: The relationship between assessment, performance and learning
Bibliographic record
Abstract
OBJECTIVE: Is the statement 'assessment drives learning' a myth? BACKGROUND: Instructors create assessments and students respond to these assessments. Although such responses are often labelled indications of learning, the responses educators observe can also be considered a performance. When responses are aligned with generating stable changes, then assessment drives learning. When responses are not aligned with stable changes, we must consider them to be something else: a performance put on partially or fully for the sake of implying capability rather than actual learning. The alignment between the assessments educators create and the way students respond to these assessments is determined by the actions students take in our curriculum, in preparation for our assessments and after engaging with our assessments. CONCLUSIONS: Not all assessments need to or should support learning, but when we assume all assessments 'drive learning', we endorse the myth that assessment is necessarily a formative aspect of our curricula. When we create assessments that encourage performance activities such as cramming, competing for tutorial airtime and impression management in the clinical setting we drive students to a performance. By thinking about how our students, institutions, curricula and assessments support learning and how well they support performance, we can modify and more fully align our curricular and assessment efforts to support learners in achieving their (and our) desired outcome. So, is the phrase 'assessment drives learning' a myth? This paper will conclude that it often is but we as educators must, through our leadership, move this myth towards a reality.
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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 | 0.022 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".