Assessing semester-long student team design reports in large classes to provide individual student grades.
Bibliographic record
Abstract
This paper presents a method and tool to achieve a trade-off between workload on assessors of semester-long team-based design projects in large classes, with the need for fair and comprehensive assessments of each student individually. Students “book time” throughout the semester, recording their level of input into each project element. They each provide totals for time spent on each element of their final reports. The instructor assesses each design report as if one person wrote it. These data are combined into a single rubric/spreadsheet. The rubric scales report assessments to accommodate differences in team size, and generates a unique grade for each student in a team. Examples are given in the paper, as are details from the implementation of the method in a Fall 2015 introductory design course. There is anecdotal evidence that the method works, but there is always room for improvement. Several ideas for future modifications to method are discussed. All spreadsheets, documentation, and examples are freely available via the Web. Links are provided. Keywords: engineering design, teamwork, project, assessment, individual grading.
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".