Creative teaching assistant organization to maintain an Integrative Physiology course with 440 students
Bibliographic record
Abstract
We were recently challenged with trying to maintain the integrity and learning experience of our Physiology course, which included the use of long-answer, essay-style test questions, with a class size that increased over 2 yr by approximately 200 students. We reorganized the teaching assistant (TA) support structure in an attempt to keep the testing style and mark (or grade) the exams accurately, in a timely fashion, and provide feedback to the students that want it. Each of four TAs became experts in two sections of the course. To assess our success, TA time allocation for specific duties was recorded. Marking (or grading) accuracy was assessed by recording test data including the number of tests returned for remarking and how much marks changed by when a grade was reassessed. Student feedback was solicited to determine whether this structure provided adequate feedback and support to the students. TAs spent an average of 115 h and 35 min +/- 7 h 21 min of a total of 140 h contracted. On average, 13.2 +/- 0.5% of the tests were identified as being inaccurately graded by 4.2 +/- 0.7%. When asked to score whether the statement of assessment of students was fair, it scored 4.5 out of 5, where 5 equals strongly agree. When asked whether the course provided a worthwhile learning experience, the question scored 4.84 out of 5. Thus, we were successful at marking the exams accurately, in a timely fashion, and providing the necessary feedback, and we were successful at maintaining the objectives of the Physiology course with a class size of 440 students.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.033 |
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".