FRAMEWORK FOR TEACHING PARALLEL FLIPPED CLASSROOMS
Bibliographic record
Abstract
Advancements in information technology has given rise to a new flipped learning environment that is increasingly used at post-secondary institutions. This new pedagogical approach provides a personalized learning experience by accommodating different students’ learning styles. Students interact with the course material prior to attending scheduled face-to-face instruction, where learning is reinforced by working through examples and application problems. This paper provides a practical guiding framework for the collaboration and coordination of multiple instructors in a flipped delivery course style, based upon a literature review, qualitative research, and experience. We used a qualitative approach using a questionnaire to gather lessons learned and suggestions from instructors. The responses were analyzed to extract common themes which were mapped to create a conceptual framework for successful multi-instructor flipped course delivery. Recommendations are made as per three chronological sequences of before, during and after the course offering. The framework aims to support the planning, implementation and evaluation stages of organizing and managing a multi-instructor flipped course. This paper stresses the importance of the teaching team proactively completing the planning and design of course components before the start of the course. Quantitative student feedback received from the fall 2018 course offering in Schulich School of Engineering at the University of Calgary is used to support the flipped classroom delivery, multi-instructor delivery style.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".