Empowering faculty to initiate STEM education transformation: Efficacy of a systems thinking approach
Bibliographic record
Abstract
Just a decade ago Vision and Change in Undergraduate Biology Education: A Call to Action was released, catalyzing several initiatives to transform undergraduate life sciences education. Among these was the Partnership for Undergraduate Life Sciences Education (PULSE), a national organization commissioned to increase the adoption of Vision and Change recommendations within academic life sciences departments. PULSE activities have been designed based on the recognition that life sciences departments and faculty are embedded within institutions of higher education which, similar to other large organizations, are complex systems composed of multiple, interconnected subsystems. The organizational change research suggests that effecting large-scale changes (e.g., undergraduate STEM education transformation) may be facilitated by applying systems thinking to change efforts. In this paper we introduce the approach of systems thinking as a professional development tool to empower individual STEM faculty to effect department-level transformation. We briefly describe a professional development experience designed to increase life sciences faculty members' understanding of systems thinking, present evidence that faculty applied a systems thinking approach to initiate department-level change, and discuss the degree to which transformation efforts were perceived to be successful. Though focused on faculty in the life sciences, our findings are broadly transferable to other efforts seeking to effect change in undergraduate STEM education.
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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 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".