Earning Engagement as a Graduate Student Instructor
Bibliographic record
Abstract
One of the key benefits of a Cal Poly education is access to experienced instructors in a small classroom setting. In my final quarter at Cal Poly, I had the opportunity to glance behind the curtain and fill the significant shoes of an instructor of an introductory structural statics class. What initially was a chance to lend a hand to the department by sharing my limited experience turned into a thorough exploration of what it means to be productively engaging and of how to utilize the underrealized power of asking questions in the classroom setting. This report documents my experiences co-teaching a section of Structures II to a small class of Construction Management students in the Spring of 2024. It will provide the path I took to developing a teaching philosophy of my own by examining the four parts of the class—lecture, activity, quiz, and office hours—and discussing the successes and struggles I encountered in learning to earn the engagement of my students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".