Chemistry as if students matter: from student to student’s learning outcomes
Bibliographic record
Abstract
As a tribute to the legacy of Dr. Margaret-Ann Armour, we report on an initiative that involves university undergraduate students directly and meaningfully in the articulation and implementation of student learning outcomes for their chemistry programs. Student learning outcomes describe what a student should know, do, and value at the end of a learning experience. The initiative was carried out over several years at the King’s University in Edmonton, a small undergraduate liberal arts and science institution with a Chemical Institute of Canada accredited B.Sc. chemistry program. Senior students were involved in articulating their own learning outcomes for their chemistry program and mapping them onto the courses in the program. The resultant heat map provided an interesting visual tool to help the learning community assess strengths and gaps in coverage, as perceived by students. The authors then led a workshop at the Chemistry Education program of a Canadian Society for Chemistry national chemistry meeting to share experiences among Canadian chemistry programs on the diverse ways faculty and programs articulate, implement, and assess student learning outcomes. We conclude with suggestions for steps that departments and programs can take to meaningfully implement student learning outcomes in the design, review, and modification of chemistry programs, including benchmarking those learning outcomes with international outcomes published as a result of an IUPAC project.
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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 | 0.034 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".