Developing an online network to promote teaching and learning
Bibliographic record
Abstract
Integrating the collective expertise of Professors, Graduate Students, Post Docs, Sessionals, and Teaching Staff is key to the continued advancement and improvement of teaching and learning. However, time constraints can inhibit opportunities to share educational practices. Therefore, we designed an online network, allowing asynchronous participation while developing community and identifying and applying successful educational practices. The network is a facilitated interactive network built within a Learning Management System, structured around weekly posts of curated content followed by prompts that allow members to engage interactively, along with a space for personal reflective practice.\nIn this session we will present the design of the network, results since the launch, and discussion of challenges and solutions. Development of this network allows sharing of transformational practices beyond content, focusing on techniques and experiences that are content agnostic. The outcome of this network is to create a resource based on user’s experience, provide a place for reflection, and spark the development and updating of Teaching Philosophies and Teaching Dossiers.\nWe endeavour for this network to provide a broadly applicable platform that can inspire similar networks at Universities and Departments across Canada. Given the diversity of Teaching and Learning Practices across Universities and disciplines, development of similar networks can provide the opportunity to distill best practices, offer responses to challenges, and enhance Teaching and Learning for students at Canadian Universities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".