A Process for Developing Introductory Science Laboratory Learning Goals To Enhance Student Learning and Instructional Alignment
Bibliographic record
Abstract
Learning goal (LG) identification can greatly inform curriculum, teaching, and evaluation practices. The complex laboratory course setting, however, presents unique obstacles in developing appropriate LGs. For example, in addition to the large quantity and variety of content supported in the general chemistry laboratory program, the interests of faculty members from various chemistry subdisciplines and the service goals of such a course should be addressed. To optimize the instructional impact of limited laboratory contact time, achieve learning gains in basic and transferable (i.e., valuable in other sciences) laboratory learning content, and establish departmental consensus, a model was created for LG and assessment development that was inspired by interdisciplinary science laboratory LGs implemented at Rice University. These newly developed processes and materials were used to enhance the introductory chemistry laboratory curriculum at the University of British Columbia, involving a large (>1700 student) laboratory program. This model has potential to guide alignment and build consensus within, and possibly across, science laboratory instructional programs.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".