A National Snapshot of Introductory Chemistry Instructors and Their Instructional Practices
Bibliographic record
Abstract
The effectiveness of active learning on promoting students' academic outcomes and persistence has been established in the literature. However, despite the effort of purposeful change agents, the uptake of active learning in science, technology, engineering, and mathematics (STEM) is slow. While previous research from the chemistry education community has provided insights into the implementation of specific active learning strategies across the United States, the extent to which chemistry instructors leverage these strategies in general remains unknown. This article presents the results of a national survey aimed at exploring introductory chemistry instructors' knowledge and implementation of active learning, variations on this knowledge, and use across tenure statuses and institution types. This paper also aims to address the gap in the literature in our understanding of the characteristics of instructors of these courses. We thus provide a description of instructors' demographics, training, teaching experience, and teaching responsibilities. Our findings reveal that instructors in these courses are prominently males of European descent. Additionally, instructors come into their teaching position with minimal pedagogical training and participate mainly in short training once in their position. While the majority of instructors have knowledge of specific active learning strategies, their consistent implementation remains limited, with lecturing still being the instructional practice of choice. Variations were found between institution types and across tenure statuses within institutions in terms of pedagogical training, use of specific active learning strategies, and proportion of class time spent lecturing. The findings provide a baseline for future studies that aim to assess the effectiveness of interventions fostering the implementation of active learning in introductory chemistry courses and highlight the critical need for improved communication about teaching practices across institutions and tenure statuses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".