Bibliographic record
Abstract
This article investigates the pedagogical and instructional design process of a hybrid course in preservice teacher education. Guided by the Community of Inquiry (COI) framework, the course design aimed to foster social, cognitive, and teaching presence in a hybrid learning environment. A narrative self-study approach was employed to reflect on our teaching practices as an instructor and as a graduate teaching assistant (GTA) of this course. Data was obtained from our teaching reflections, which examined the impact of implementing a hybrid face-to-face driver model informed by the COI on enhancing preservice teachers' learning experiences. The results suggest that the hybrid course design provides a more inclusive and empowering learning environment. The flexible format allows students to demonstrate their learning and collaborate effectively while developing socioemotional awareness. By examining our experiences and knowledge, this article offers insight into how to effectively incorporate digital technologies and hybrid practices to promote a deeper understanding of lived experience in learning within the context of teacher education programs. With the rapid shift to online learning because of the COVID-19 pandemic, understanding how to design and implement hybrid courses becomes crucial in empowering preservice teachers for the digital era.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".