Review of In-class Active Learning Observation Protocols
Bibliographic record
Abstract
This review paper examines the literature on classroom observation methods with an emphasis on observation protocols that are appropriate for active learning classrooms in engineering.Classroom observations have been used for professional development (e.g., formative feedback, evaluation of teaching) and administrative assessment (e.g., program evaluation).The focus of this work is to identify a protocol for collecting observation data to provide insight into active learning activity in STEM education and to inform design decisions for future active learning classroom space and technology design.With the emergence of purpose-built active learning classrooms, observations can capture active learning pedagogies and characterize the fit between teaching strategies and space affordances.This paper provides an overview of classroom observation protocols, and particularly those that were designed for active learning pedagogies.The review of these protocols identifies the advantages of each, and the aspects of the protocols that are suited to providing information on space design.Active classrooms typically include a physical layout that supports collaborative learning, and technology that supports interaction.To produce feedback on space design, a protocol should provide insight on the way STEM instructors makes use of both the physical layout and the technology to realize their teaching goals.We found that the existing protocols meet many, but not all of the requirements.We propose a hybrid protocol, that combines two existing frameworks, specifically aimed at providing information for active classroom design.
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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 | 0.036 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".