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 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".