BEING A TEACHING ASSISTANT (TA) IN FIRST-YEAR ENGINEERING COURSE: PERSPECTIVES, CHALLENGES, REWARDS AND RECOMMENDATIONS
Bibliographic record
Abstract
The CEN100 Introduction to Engineeringteaching assistantship experience is examined from aTA ’s perspective. In addition to generic duties, CEN100TAs are primary deliverers of the engineering design component of the course and assessors of 55% of the course materials. Challenges faced by CEN100 TAs include changing students ’ perception of the course and effectively managing expectations from students and the course instructors. Also, maintaining fair and consistent grading is a big challenge to TAs. However, being CEN100 TAs has many rewards, such as improving organizational skills and public speaking skills, and obtaining real life teaching experience. Considering the degree of importance TAs play for the course, it is recommended that proper TA training is offered using available resources from the Learning and Teaching Office. Also, feedback from TAs should be rigorously collected and incorporated in CEN100 in addition to having proper TA evaluations from both students and the course instructor.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".