Incorporating Topics That Aren’t Distance-Friendly Into an Online Program: One Development Team’s Experience
Bibliographic record
Abstract
The Native Species and Natural Processes certificate at the University of Victoria is an advanced-level online program of four courses to introduce students to state-of-the-art topics in the field of ecological restoration. The program posed some unique challenges for course developers.The development team needed to find ways to create online courses that support a practical approach for topics that normally require tangible hands-on work. The solutions to these challenges required a creative problem-solving approach to accommodate the unique elements of the development process and the delivery of each course. The solutions employed included(1) creation of a “connection to place” by use of extensive visuals in slide shows,(2) use of problem-based learning to develop critical thinking skills,(3) engagement of students via case studies to bridge the different languages inherent in different ecosystems,(4) conducting of virtual site visits to design real-world resto- ration projects,(5) inclusion of “fireside chat” audio to reinforce the idea of multiple perspec- tives and uncertainty,(6) establishment of a community of practice to engage students in collaborative learning,(7) creation of assign- ments that involve scaffolding projects and peer review,(8) allowance for students to customize projects to accommodate their geog- raphy and different realities, and(9) develop- ment of a design charrette to practice collabora- tive decision making and design.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".