Using Psychology in the Physics Classroom: Five Steps to Improving Classroom Effectiveness
Bibliographic record
Abstract
Psychology has been an avocation of mine for almost 20 years, and over the past decade I have begun integrating this knowledge into my classroom. My first introduction to psychology was through the Myers-Briggs Type Indicator®. MBTI looks at preferences in how people interact with the world around them. By recognizing how different students receive, process, and communicate information, and how my communication style likely interacts with theirs, I have been able to improve my competence in the classroom. For example, my student evaluations have gone from the low- to mid-5’s 13 years ago to mid-6’s (out of 7) consistently over the last five years, during which time I have also won four teaching awards and have been nominated for one more at my institution.
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 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 | 0.052 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".