Taking the sting out of assessment: is there a role for progress testing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it