How the application of physics education research improved student success in first-year physics
Bibliographic record
Abstract
The proliferation of physics education research (PER) has presented an opportunity for university-level educators to adopt peer-reviewed teaching best-practices and to build upon them. At the University of Ontario Institute of Technology, we have made a systematic change in our approach to teaching first year physics courses using the principles of PER. The curriculum, instruction, and assessment in these large-enrollment (about 900 students) courses were altered to bring them in line with evidence-based pedagogy, and we evaluated the effectiveness of these changes.\nDuring the first phase of our project we designed and built a series of classroom demonstrations based on common student misconceptions in physics. These demonstrations were meant to both enhance student engagement as well as to improve conceptual understanding. We designed the actual demonstrations alongside “predict-observe-discuss” questions, which can promote student conceptual understanding. During the second phase of our project we altered the homework delivery system. Online homework systems have mostly replaced traditional hand-written assignments; however, the most common online systems are provided by textbook publishers, which can be expensive and inflexible. From recent PER, we identified a “mastery setting” as a good model for homework delivery, built an online system based on an open-source platform, and delivered it to our students.\nIn this presentation we will describe the above initiatives, as well as quantify changes in student performance and engagement. We will also provide hands-on and interactive examples of our demonstrations and online homework system.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".