Bibliographic record
Abstract
Summary: This talk will discuss how guided-inquiry experiments bridge the gap between the classroom and research laboratory through active learning and problem solving. Abstract: In most undergraduate laboratory courses, students perform thoroughly tested experiments with proven results. These exercises do not necessarily represent a research laboratory experience where reaction outcomes are unknown and procedures are routinely optimized for higher yield and purity. This talk will focus on the role and impact of guided-inquiry learning in the undergraduate laboratory by highlighting two new experiments in the second year organic chemistry curriculum at the University of Toronto, which effectively bridges the gap between the classroom and research laboratory. These types of experiments are useful teaching tools across all Science disciplines as they give students the opportunity to experience the challenges of conducting scientific research while encouraging active learning through creative problem solving. The process of developing these new laboratory activities and select student experimental results will be briefly discussed. The impact of guided-inquiry experiments towards student learning will also be presented by sharing the data collected from student evaluations.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".