Usability Analysis of Freeform Marking on Engineering Problem Solving
Bibliographic record
Abstract
Freeform comments as a means of providing formative feedback on engineering problems, from the perspective of feedback providers (i.e. assessors), is examined. The aim of this research is to collect and analyze assessors’ ratings on the usability of this type of task. Two course topics with different error loads in the sample solutions were used as the basis for the work. Assessors were divided into two groups: Group 1 received an evaluation package containing first year mechanics students’ test solutions with a high error load (Error 1 =32), while Group 2 received first year circuits students’ test solutions with low error load (Error 2 =11). Assessment time was held constant (t tot =20min). A standard instrument for usability was utilized. Analysis of the survey data from the assessors (n 1 =11, n 2 =19) revealed some significant differences between the two groups. In particular, Group 1 reported a lower degree of perceived consistency in marking relative to Group 2.
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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 | 0.017 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".