Taking the sting out of assessment: is there a role for progress testing?
Bibliographic record
Abstract
CONTEXT: It has long been understood that assessment is an important driver for learning. However, recently, there has been growing recognition that this powerful driving force of assessment has the potential to undermine curricular efforts. When the focus of assessment is to categorise learners into competent or not (i.e. assessment of learning), rather than being a tool to promote continuous learning (i.e. assessment for learning), there may be unintended consequences that ultimately hinder learning. In response, there has been a movement toward constructing assessment not only as a measurement problem, but also as an instructional design problem, and exploring more programmatic models of assessment across the curriculum. Progress testing is one form of assessment that has been introduced, in part, to attempt to address these concerns. However, in order for any assessment tool to be successful in promoting learning, careful consideration must be given to its implementation. OBJECTIVE: The purpose of this paper is to consider the implications of implementing progress testing within practice, and how this might promote or impede learning in the three phases of assessment (pre-test, pure-test and post-test). METHODS: We will examine the literature on how assessment drives learning and how this might apply to progress testing. We will also explore the distinction between assessment of learning and assessment for learning, including ways in which they overlap and differ. We end by discussing how the properties of an assessment tool can be harnessed to optimise learning. CONCLUSIONS: Progress tests are one potential solution to the problem of removing (or at least lessening) the sting associated with assessment. If implemented with careful thought and consideration, progress tests can be used to support the type of deep, meaningful and continuous learning that we are trying to instill in our learners.
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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 | 0.230 | 0.526 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.013 | 0.036 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".