Implementing Investigative Labs and Writing Intensive Reports in Large University Physics Courses
Bibliographic record
Abstract
Undergraduate physics programs are increasingly facing pressure from university and college administration, industry, and funding agencies to improve training of our undergraduates. Increasingly, tertiary institutions have redefined their graduate profiles and mission statements to encompass more than just content knowledge, including skills that will help students succeed in today’s fast-paced world. Many physics departments have started to incorporate the results of physics education research and cognitive science, by adopting more active pedagogies. Student Centered Active Learning Environment with Upside-down Pedagogies (SCALE-UP) is one such educational innovation that has spread widely around the United States and abroad. While initially developed for large-enrollment university physics courses, the approach is being used in a variety of disciplines and class sizes so the acronym has evolved to reflect this. SCALE-UP integrates the lab, “lecture,” and tutorial sections of the course in a reformed classroom to allow large-enrollment university courses to benefit from interactive instruction. This article explains how the University of Auckland developed more open-ended, resourceful lab activities to be completed by large classes that enhance understanding of physics while developing transferable writing-related and critical thinking skills.
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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 | 0.070 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".