The challenges of changing teaching assistants’ grading practices: Requiring students to show evidence of understanding
Bibliographic record
Abstract
Teaching assistants (TAs) are often responsible for grading in introductory physics courses at large research universities. Their grading practices can shape students’ approaches to problem solving and learning. Physics education research recommends grading practices that encourage students to provide evidence of understanding via explication of the problem-solving process. However, TAs may not necessarily grade in a manner that encourages students to provide evidence of understanding in their solutions. Within the context of a semester-long TA professional development course, we investigated whether encouraging TAs to use a grading rubric that appropriately weights the problem-solving process and having them reflect upon the benefits of using such a rubric prompts TAs to require evidence of understanding in student solutions. We examined how the TAs graded realistic student solutions to introductory physics problems before they were provided a rubric, whether TAs used the rubric as intended, whether they were consistent in grading similar solutions, and how TAs’ grading criteria changed after discussing the benefits of a well-designed rubric. We find that many TAs typically applied the rubric consistently when grading similar student solutions, but did not require students to provide evidence of understanding. TAs’ written responses, class discussions, and individual interviews suggest that the instructional activities involving the grading rubrics in this study were not sufficient to change their grading practices. Interviews and class discussions suggest that helping TAs value a rubric that appropriately weights the problem-solving process may be challenging partly due to the TAs’ past educational experiences and the departmental context.
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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 | 0.139 | 0.413 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".