DEVELOPMENT AND VALIDATION OF DESCRIPTORS FOR UNIVERSAL PROBLEM-ANALYSIS RUBRIC
Bibliographic record
Abstract
Abstract –This paper describes the process for creating and validating descriptors for a universal problem-analysis rubric. Our objective is to create descriptors that provide effective feedback to students on assessments that have been designed to elicit the demonstration of metacognitive problem-analysis skills. Building on previously tested and validated indicators as well as benchmarking descriptors from credible and cited rubrics (e.g. the VALUE rubrics), the descriptors were developed through decomposition of global outcome statements and expansion into separate dimensions. The descriptors were then iteratively revised through consultation with faculty experts who teach in fields where assessment of problem-analysis is common. This involved individual faculty and focus group sessions held with engineering faculty members. The universal problem-analysis rubric created could serve as a resource for engineering faculty to accompany their problem-analysis learning activities (e.g. problem sets) and to elicit student work that is aligned with learning outcomes students need to demonstrate to fulfill CEAB assessment needs. They could also use them as an evaluation tool to increase consistency and reliability of evaluation especially in large classes with multiple assessors.
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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 | 0.095 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".