Modeling Active Engagement Pedagogy through Classroom Response Systems in a Physics Teacher Education Course
Bibliographic record
Abstract
One of the most commonly explored technologies in Science, Technology, Engineering, and Mathematics (STEM) education is Classroom Response Systems (clickers). Clickers help instructors generate in-class discussion by soliciting student responses to multiple-choice conceptual questions and sharing the distribution of these responses with the class. The potential benefits of clicker-enhanced pedagogy include: increased student engagement, reduced anxiety, continuous formative assessment, and enhanced conceptual understanding. Most studies, however, investigate the effects of clicker-enhanced instruction in large undergraduate STEM courses. The impact of this pedagogy on learning in small secondary or post-secondary classrooms is still relatively unexplored. The context of this study is a secondary physics methods course in a Teacher Education Program at a large Canadian university. One of the course assignments required future teachers to develop multiple-choice conceptual questions relevant to the secondary physics curriculum. This study investigates the impact of modeling clicker-enhanced active engagement pedagogy on future teachers’ Content Knowledge, Pedagogical Knowledge, and Pedagogical Content Knowledge, as revealed by this assignment. The results of the study indicate that: (1) modeling clicker-enhanced pedagogy in a physics methods course increases future teachers’ interest in active learning; (2) clicker-enhanced pedagogy is a powerful vehicle for developing Pedagogical Content Knowledge of future physics teachers; (3) clicker-enhanced pedagogy is a useful tool for teacher educators for identifying and addressing the gaps in the Content Knowledge of future teachers. This study sheds light on developing future teachers’ capacities to design and implement instruction that is driven by conceptual questions in the presence or absence of technology and the impact of this process on their Pedagogical Content Knowledge and attitudes about conceptual STEM 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".