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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".