Teaching as Coevolving: An Approach to Online Course Design
Bibliographic record
Abstract
The growth of online learning in higher education, over the last decade and its exponential development due to COVID-19, has opened up exciting possibilities for instructors by providing access to new modes of course design not possible within the constraints of a traditional classroom. One approach to enhancing the student online learning experience is Universal Design for Learning (UDL), in which students are able to engage with the material in a manner appropriate to their current situation. By using this approach, courses can be designed in ways that allow students to take personalized paths to achieve the course outcomes. The purpose of this presentation is to outline the lessons learned for designing online courses using UDL. The courses used courses provided multiple entry points for learning, so that students, with all their diversities, can adapt activities to fit their needs, emergent abilities, and interests. UDL can be expressed in four sub-principles: 1) providing multiple means of representation, with spaces for unanticipated possibilities to emerge; 2) providing multiple means for students to express what they know and what they have learned; 3) offering ways into, and explorations beyond, planned experiences; 4) permitting and nurturing specialized interests of individuals, while enhancing possibilities for the collective.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".