Marking group projects in a large classroom: a case study in the Introduction to Project Management course
Bibliographic record
Abstract
The "Introduction to Project Management" is a core online graduate course in the Faculty of Engineering at the University of Calgary. During the Fall 2022 semester, 373 students enrolled in this course, and the course grade mainly depended on a group project report. The marking task was split between two Teaching Assistants (TAs), which could affect the marking consistency and fairness of grades. This study presents an approach for grade consistency in such large classrooms with multiple markers. For this, TAs and an instructor marked the same 10% of the submissions using a pre-defined rubric and discussed any differences in their marks. If there was a significant mark discrepancy, markers marked an additional few submissions and reconvened. Afterwards, the TAs marked the remaining submissions, and the final marks were validated for any remaining bias. Discussion on the implementation issues of this marking scheme can provide lessons learned for other instructors.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".