Observing Instructor Behaviour in an Active Learning Classroom: A Case Study of an Undergraduate Calculus Course
Bibliographic record
Abstract
Active learning classrooms (ALCs) are spaces explicitly designed to encourage collaborative learning, often through the use of technology. To learn more about teaching activity in ALCs, a study was designed to observe an engineering calculus course during the winter 2020 term. A large-scale active learning classroom was selected for classroom observation using the extended Teaching Dimensions Observation Tool (TDOP+). The TDOP+ is a descriptive classroom observation protocol based on the Teaching Dimensions Observation Tool, enhanced with elements from the Active Learning Classroom Observation Tool (ALCOT). This case study compares the class orchestration of different instructors teaching two different sections of the same course at a large, public university. Twenty class sessions were coded for this study: 10 for each section (5 for each instructor). The coded instructor behaviour was analyzed using a conceptual framework described by Nocera (i.e., a version of Activity Theory), focusing on mediating artifacts and instructor goals. While we observed differences in the frequency and duration of active learning activities and in the type and number of tools used in each class session, the results from this case study suggest that flexible space design enables instructors with the same lesson plan (and content) to create different technological frames to achieve their varied pedagogical goals, while encouraging increased adoption of new tools.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".