Using Student Focus Groups to Support the Validation of Rubrics for Large Scale Undergraduate Independent Research Projects
Bibliographic record
Abstract
Finding methods of validating rubrics forsignificant “capstone” experiences, including fourth yeardesign projects and the research-oriented thesis, can bechallenging, given the large number of individualstypically involved in the assessment of studentdeliverables. This paper describes a methodology forusing student focus groups to support the validation of arubric for a fourth year thesis course in a largeEngineering Program, and the results from these focusgroups. Through focus group discussion and activitysheets used in the focus groups, a number of interestinginsights were raised about both the rubric, namely: a lackof consultation by the students with the rubric until thefinal stages of writing the final report; concerns andinconsistencies in the perception of how supervisors willuse the rubric; a perceived lack of focus on process andproject experience-related criteria and concerns with thelevel of expectation of the project experience-relatedcriteria that are present, and other concerns related toterminology and distance between rubric descriptors. Thefocus group provided a useful forum for discussion oncourse experience and assessment, effectively allowingstudents to both individually reflect, and build on eachother’s ideas and suggestions.
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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 | 0.227 | 0.345 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".