USING WEIGHTED SCORING RUBRICS IN ENGINEERING ASSESSMENT
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
When evaluating student work by deducting marks for errors,it is possible to underestimate the importance ofdominant concepts and assign grades at a level that mightnot be in agreement with academic policies. Rubrics facilitateand expedite the marking process but it is importantto examine in detail the parameters and limitations of thisstructured approach to assessment. The structure of thescoring rubric considered in this study promotes the consistencyand validation of the assessment process and discriminatesbetween evaluation components by assigningdifferent weights to dominant and secondary criteria. Theshape of the weight distribution function plays an importantrole in this process. In the proposed rubric structure,an array of performance levels is multiplied by anarray of task components to arrive at a mark and a grade.A uniform weight distribution is easy to develop and utilize,especially for large classes, but it fails to recognizethe importance of dominant components. The proposedapproach allows the incorporation of single and multipledominant criteria modeled by using step or linear distributionfunctions and adjusting the relative value of dominantand secondary components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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