Practice Using home-laboratory kits to teach general chemistry
Bibliographic record
Abstract
Abstract: University-level chemistry courses that contain a substantial laboratory component have always been a challenge to deliver effectively through distance education. One potential solution is to enable students to carry out real experiments in the home environment. This not only raises issues of logistics and safety, but also the fundamental question of whether an equivalent learning experience could be achieved with home laboratories. Athabasca University, Canada’s Open University, has been successfully running chemistry courses for almost three decades. The migration from traditional supervised laboratories to home-study experiments over a fifteen year period in a general chemistry course is described. The study examines both student experience using the home-study laboratory kits, and their actual performance. Student grades in the course essentially remain the same as supervised laboratories are replaced by home-study laboratories, while at the same time offering the student increased access and flexibility. Furthermore, bringing experiments into a home environment contextualizes learning for the student and raises the possibility of incorporating the home-study laboratory experience, in whole or in part, into traditional general chemistry course offered on campus. [Chem. Educ. Res. Pract., 2007, 8 (3), 337-
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".