Engagement in Online Learning: It’s Not All About Faculty!
Bibliographic record
Abstract
In this chapter, we, the authors Bishop, Etmanski and Page, argue for the need to disrupt the traditional notion of faculty solely as expert. We redefine the online faculty role to be that of a facilitator who creates the space for students to engage with both content and other students in the class. We discuss the adult learning principles behind our practices and our attention to building community. To illustrate what our online teaching work looks like in practice, we begin by providing a creative script on what online learning could look like. We then speak to utilising the specific strategies of online forums, behind the scenes outreach, synchronous meetings and assignments to create rich engagement in the online environment for higher education and learning.We place a strong emphasis on building community among our students from the start of course and throughout. Recognising that people respond differently to different scenarios and have different learning preferences, we seek to offer a diverse range of options for experiencing community, with the intention of offering the possibility of belonging for everyone. The intention to create space for engagement in online learning has challenged us to continually ask ourselves how we can adapt or create new activities and experiences for the online learning environment, so as to enhance engagement.
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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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".