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Record W4412002574 · doi:10.59236/td2022vol14iss31597

What Lies Beneath? A Systems Thinking Approach to Catalyzing Department-Level Curricular and Pedagogical Reform Through the Northwest PULSE Workshops

2022· article· en· W4412002574 on OpenAlexaff
Alyce DeMarais, Gita Bangera, Claire Bronson, Steven S. Byers, William B. Davis, Nalani Linder, Jenny McFarland, Erika G. Offerdahl, Joann Otto, Pamela Pape-Lindstrom, Carol A. Pollock, C. Gary Reiness, Stasinos Stavrianeas, Mary Pat Wenderoth

Bibliographic record

VenueTransformative Dialogues Teaching and Learning Journal · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsPulse (music)Mathematics educationPedagogyMedical educationPsychologyMedicineEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

We have developed and tested a dynamic approach to assist positive, department-wide change at institutions of higher education. Here we describe a workshop strategy designed to empower faculty as agents of change. This strategy incorporates tools and concepts including systems thinking, visual facilitation, and action planning to drive transformation at the department/program level. Although our workshops were developed for life sciences faculty, the processes we adopted, and the lessons learned from the project, provide a framework for the faculty of any STEM discipline at any type of higher education institution to develop skills to effect changes in approach and pedagogy that will improve learning outcomes. While our workshops were carried out in person, we describe approaches that can be adapted for online use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0090.005
Open science0.0030.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.111
GPT teacher head0.299
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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