Systems Thinking: An approach for departmental transformation in the life sciences
Bibliographic record
Abstract
The Partnership for Undergraduate Life Sciences Education (PULSE) is a national organization that supports department‐level efforts to transform life sciences programs to align with the recommendations of Vision and Change (V&C). With support from the NSF, a team of PULSE fellows from the Pacific Northwest region created a program consisting of (1) a three‐day, team‐based workshop on how to apply systems‐thinking approaches to organizational change and (2) a mentored execution of a department‐level transformation plan. Over the past five years, faculty teams have participated from over 40% (63/148) of institutions in the NW region, including 27 community colleges, 15 liberal arts, and 11 masters granting, 9 doctoral granting and one professional‐degree granting institutions. Reported here are the short and long term outcomes of the first 45 institutional teams from years 1–3 of the project. A mixed‐methods approach was applied to (a) understand the ways in which NW PULSE supported departmental transformation, (b) identify strategies used by department and the relative efficacy of those strategies, and (c) determine emergent practices to inform departmental transformation efforts nationwide. Pre‐ and post‐surveys were administered prior to and immediately following the workshop and 6–7 months later. Long‐term effects of program participation were measured by a longitudinal survey. Fifty‐seven percent of the 138 individual participants responded to the survey, representing 87% (39 of 45) of institutions. An outlier sampling approach was subsequently used to interview faculty and administrators at 12 institutions to identify factors that contribute to departmental/institutional transformation. Results from the evaluation provide insight into potential “better practices” for supporting biology education reform. First, our data support the recommendation for systematic and inclusive engagement of faculty, especially faculty that represent the composition of the department and those that have decision‐making power or influence in the department. Further, while a critical mass of faculty is needed for transformation, our data suggest that effective transformation need not involve all department faculty members. Second, our data underscore the importance of providing a range of resources and support. Not surprisingly, there is no “one‐size‐fits‐all” approach because each institution has its own unique context. Finally, our data demonstrate the efficacy of applying systems thinking to department transformation efforts. These data are consistent with prior literature encouraging multiple levers across levels within the organization to catalyze change.
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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 | 0.032 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".