USING ACCOUNTABILITY LOGS TO ASSESS INDIVIDUAL STUDENT CONTRIBUTIONS TO CAPSTONE PROJECTS: WHAT HAPPENS WHEN ONE STUDENT ON A TEAM FAILS?
Bibliographic record
Abstract
Abstract – To discourage social loafing, the need for fair differential grading among team members led to the development and use of Accountability Logs(ALs) and associated evidence-based rubrics as tools to help Capstone Coordinators identify individual students failing to make meaningful and competent technical contributions. While traditional Engineering logbooks tend to be hard-covered notebooks documenting the design analysis, sketches, calculations, and results of a project, ALs can be in electronic format to allow for importing of multi-media examples of works in-progress or completed. ALs must contain a) Evidence - explicit accounting of the student’s independent technical contributions to the project on a minimum weekly basis; b) Learning - reflection on value added from personal contributions to the overall project goals; and c) Planning - articulation of logical next steps for moving forward with technical contributions to meet project goals. The AL components align with the Graduate Attribute of Life-Long Learning. Through experience with three cohorts of students (Fall 2014 – Winter 2017), we explain the evolving use of ALs; how ALs are currently situated in the overall assessment of the student team members of Capstone Engineering Projects; and our current recovery options for students who receive failing grades in the Capstone course.
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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 | 0.026 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".