Recording Tutorials To Increase Student Use and Incorporating Demonstrations To Engage Live Participants
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Over the course of three semesters, the tutorials for introductory organic chemistry at McGill University evolved significantly with the input from student surveys. The tutorials changed from “chalk talks” in the first semester to a lecture capture format in the second in which PowerPoint slides, ink annotations, and associated audio were recorded, and uploaded online to be viewable by any student at any time. As expected, the later format reached more students, though fewer came in person to the live tutorial. In an effort to continue to reach as many students as possible, while at the same time providing a more engaging environment for students at the live event, the format changed once more. Demonstrations, discussions, and other personalized interactions not accessible online were incorporated in the third semester to provide a more meaningful experience for students physically present, without compromising the online content. This third tutorial format in the final semester did indeed encourage more students to come in person. Herein, we follow the evolution of these tutorials, discuss the impetus for changing formats, document student use (both online and in person) and conclude that lecture capture technology is an effective means of delivering optional course content and it can be effectively supplemented by demonstrations and other personalized interactions to reach students with different learning styles.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".