What Makes a Good Assessment? Assessments for Learning
Bibliographic record
Abstract
We present in this paper a ‘how-to’ frameworkfor designing motivating assessments, based upon thecognitive theories of expectancy-value and of aligned andauthentic objectives. The framework recasts thesecognitive theories into more practical steps of determiningobjectives, setting expectations, and framing theassessment to be well scoped, authentic, and relatable. Inthe Fall 2017 offering of our Introduction to MechanicalEngineering course, two new short design challenges andone long design challenge were piloted after beingdesigned according to the objectives-expectations-framingframework. In each case, the assessments were designed tobe (to varying extents) engaging/authentic (something thatstudents would want to do), and doable/relatable(something the students could do). The term long project(of largest scope, authenticity, and relatability) was foundby student survey to be the most motivating. Of the twosmaller projects, the second, while seemingly moreauthentic and relatable, was found to be less motivating.We understand this to be due to the context of thisassessment coming during a time in the term when studentwere busy with the term design project and other courses.
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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 | 0.040 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".