Active Learning Curriculum Design: Insights through Teacher Educators’ Lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research has demonstrated the value of active learning, such as flipped classrooms and inquiry-based learning. In higher education, there is a growing investment in Active Learning Classrooms (ALCs) designed for supporting these approaches and maximizing their benefits. However, designing curriculum for these spaces poses challenges to instructors, beginning with their own understanding active learning, how to effectively use physical classroom space, and how to design and implement new teaching strategies. This study seeks to inform our understanding of how teacher educators design active learning curriculum for their own courses, with the ultimate goal of informing the development of a curriculum authoring environment to support instructors in their design. The research is centered around the interview of two experienced teacher educators at a Canadian University, who provide insight into their active learning course design processes. The findings highlight how educators integrate active learning into their pedagogy, revealing strong interest in spatial and technological elements, yet facing implementation barriers. This study aims to deepen our understanding of teacher educators’ curriculum design processes and provide a foundation for future support that empowers them to effectively utilize active learning classrooms.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| 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 it