Laboratory Skills Assignments as a Teaching Tool to Develop Undergraduate Chemistry Students’ Conceptual Understanding of Practical Laboratory Skills
Bibliographic record
Abstract
Laboratory skills assignments were developed as a novel approach to providing students with the opportunity to engage in hands-on laboratory skills development outside of the lab during the COVID-19 pandemic. Initially, the assignments were implemented within a second-year forensic chemistry course of 48 students and redesigned and modified to be implemented within a large in-person second-year analytical chemistry course of 208 students as a complement to the laboratory experiments. Five laboratory skills were chosen to coincide with those used within the laboratory experiments of the course: pipetting, quantitative transfer, serial dilutions, buret use within titrations, and weight-by-difference mass measurements. Each skills assignment consisted of two videos demonstrating the selected skill: one in which the skill was performed properly and one in which deliberate errors have been included. For each skills assignment, students were tasked with distinguishing between the two videos along with identifying the included errors and the consequences each error would have on either the accuracy and reproducibility of the collected data or the safety of the experimental procedure. Student feedback on the skills assignments is also reported.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".