Demystifying the scaffolding required for first-year physics student retention: contextualizing content and nurturing physics identity<sup>,</sup>
Bibliographic record
Abstract
Graduates of physics degree programs are a critical element in the development of a scientifically literate, economically competitive society as discussed by the National Research Council in 2013 (Adapting to a changing world: Challenges and opportunities in undergraduate physics education. National Academies Press, Washington, D.C. 2013). This qualitative case study invited students at a university in Atlantic Canada to participate in a post-course survey to investigate what influenced them to major or to not major in physics. Sixty students participated in the survey portion of the study, of a possible 121, and the survey data was cross-referenced with data from two student interviews, one professor interview, and one laboratory technician interview. Results indicated that student participants were more likely to choose a physics degree program if they felt that they had enough interest in the subject matter, they had experienced good teaching, and they could see how to apply the degree to a career. In addition, a newly implemented, research-based laboratory curriculum proved beneficial, especially for female students. Fifteen percent of participating students continued into a physics major, which is more than the average of under 10% reported by Nicholson and Mulvey in 2016 (Roster of physics departments with enrollment and degree data, 2013. Focus on: American Institute of Physics. 2016. Available from https://www.aip.org/sites/default/files/statistics/rosters/physrost15.1.pdf ). This study provides a Canadian perspective on the choice to major in physics or not, which had been missing from the literature. Findings from this case could be infused into other first-year physics courses to boost student retention rates.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".