ADAPTATION OF A CLASSROOM OBSERVATION PROTOCOL FOR ACTIVE LEARNING
Bibliographic record
Abstract
In our work, we are focusing on the use of classroom observation to provide feedback on instructional space design. An initiative to redesign teaching space began a decade ago at a large, research-intensive institution. In September 2018, a large-scale (477 seat) active learning classroom became operational. The affordances of this space are intended to enable active teaching and learning in large classes. However, it is difficult to assess how successful this space is for active learning. A multi-year study has been undertaken to observe teaching practice in situ, with the goalof developing design principles for instructional space and technology that support the development, design, and implementation of teaching activities. Existing teaching observation protocols do not fully capture the interaction between the instructor and the space because such protocols were generally intended for other purposes. The goal is to develop a protocol that captures activities that are both intrinsic and extrinsic to teaching. This paper describes the development and use of an observation protocol. The core of the protocol is the wellknownTeaching Dimensions Observation Tool (TDOP). The scope of the TDOP is extended to active learning activities drawing from the Active Learning Classroom Observation Tool (ALCOT). The resulting extended protocol, TDOP+, was used for coding both live and recorded classroomobservations in the Winter 2020 term. This extended protocol allows the researchers to capture information that characterizes the intersection of pedagogy, space, and technology through Activity Theory. In future work, the data gathered through observations will be analyzed using theDifferentiated Overt Learning Activities (DOLA) framework, to provide insight into what types of teaching activity happens in a large-scale active learning classroom across STEM education and how active learning in large classrooms compares to pedagogy in other spaces.
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 | 0.111 | 0.175 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".