Designing and Implementing a Societal-Context Course for Physics Undergraduates
Bibliographic record
Abstract
Through a self-study research-practice partnership, we discuss the design, implementation, observations and lessons learned from active learning integration in an upperlevel university course.This one-semester course on physical science in contemporary society aims to empower physics undergraduates to develop transferable skills and 21st century competencies through design elements including a student-driven curriculum, student-led classes, an open-ended final project, and ungrading.The class was judged to be a success based on student outputs of final projects, student-designed classroom activities, and student reflective writing. Pedagogical objectivesWe live in a global, interconnected world in which science plays a vital role, as illustrated recently through the COVID-19 pandemic.Yet physics is often taught as an objective discipline, impervious to societal concerns.How do we prepare physics students to grapple with vital questions regarding complex relationships between physics and society?In this self-study, we examine the design and implementation of a university course on physical science in contemporary society.Each year, roughly forty third-through-fifth-year physics undergraduates take this one-semester course, which fulfills the ethics requirement for physics specialists at a large research university.This study examines the depth of learning and the efficacy of our student-driven course design.In particular, we aim to engage students equitably, mitigate any resistance to a nontraditional course format, and empower students to critique and ameliorate the culture(s) in which they live (including physics, academia, and beyond).Learning goals include: (1) to explore relationships between individual people and physics; (2) to explore relationships between society and physics; (3) to communicate effectively.This self-study is a research-practice partnership that focuses on a second iteration of the course taught by the practitioner (Sealfon) with observations and analysis provided by a trusted observer (Burron).The practitioner's first iteration was half online and half in-person due to the COVID-19 pandemic.Previous iterations of the course, not taught by the practitioner, were less student-centered and involved less active learning.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".