What Makes a Good Assessment? Assessments for Learning
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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