A multi-institutional investigation of first-year engineering tutorials: content, pedagogy, and effectiveness
Bibliographic record
Abstract
For many courses, tutorial classes are important part of students’ learning. They are mainly designed to offer students in large classes (usually over 60 students) the opportunity for a more focused discussion and direct engagement with other students and teaching assistants (TAs). Therefore, it is important to make sure tutorial classes address students’ needs and reach the effectiveness that is expected from tutorial classes. However, teaching assistants provide essential support roles in the coordination of large undergraduate tutorial classes, but are often overlooked in discussions of pedagogy, both as aspiring teachers and as continuing learners.In this research, we looked at the overall structure and effectiveness of first-year tutorial classes in design and non-design courses from TA’s points of view at two large Canadian universities; the University of Toronto and York University. The intended outcome of this work is to discuss teaching assistants’ perceptions on tutorial classes, content and pedagogy of distinctive styles of tutorials, as well as strengths and weaknesses of tutorial classes, and any opportunities for improvement.
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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 | 0.000 |
| 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".