Online Learning as the Catalyst for More Deliberate Pedagogies: A Canadian University Experience
Bibliographic record
Abstract
This chapter explores issues of quality teaching, learning, and assessment in higher education courses from the perspective of teaching fully online (polysynchronous) courses in undergraduate and graduate programs in education at a technology university in Ontario, Canada. Online courses offer unique opportunities to capitalize on students’ and professors’ digital capabilities gained in out-of-school learning and apply them to an in-school, technology-enabled learning environment. The critical and reflective arguments in this paper are informed by theories of online learning and research on active learning pedagogies.Digital technologies have opened new spaces for higher education which should be dedicated to creating high-quality learning environments and high-quality assessment. Moving a course online does not guarantee that students will be able to meet the course outcomes more readily, however, or that they will necessarily understand key concepts more easily than previously in the physically copresent course environments. All students in higher education need opportunities to seek, critique, and construct knowledge together and then transfer newly-acquired skills from their coursework to the worlds of work, service, and life. The emergence of new online learning spaces helps us to reexamine present higher education pedagogies in very deliberate ways to continue to maintain or to improve the quality of student learning in higher education.In this chapter, active learning in fully online learning spaces is the broad theme through which teaching, learning, and assessment strategies are reconsidered. The key elements of our theoretical framework for active learning include (1) deliberate pedagogies to establish the online classroom environment; (2) student ownership of learning activities; and (3) high-quality assessment strategies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.050 | 0.018 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".