Online Evolution: Advantages and Challenges of Online Course Components
Bibliographic record
Abstract
Abstract –"For a Copernican revolution to take place it does not matter what means are used provided this goal is achieved: a shift in what counts as centre and what counts as periphery." – Bruno Latour [1] As Michael Wesch pointed out in his 2008 talk at the University of Manitoba, "A Portal to Media Literacy," the conventional lecture hall set-up reinforces an authoritarian view of education as the passive reception of scarce and valuable bits of information. [2] This is the opposite of the exploratory, questioning discovery we would like our students to has as their learning experience. The problem of the conventional lecture hall is, as well, exacerbated in large classes of 800 or more students. However, the evolution of media, from television through to online media of today, has created opportunities, challenges and obstacles that lecturers today continue to experiment with to create relevant, interactive classes - of whatever size.
 At the same time, the numbers of students who can be reached with Massive Open Online Courses (MOOCs) dwarfs even our largest lecture courses and there are definite advantages that can be gained through online learning - reaching people in remote areas, enabling working people to take courses, providing credible, university-level information to any curious person who wants to learn. And while so far MOOCs may not have been uniformly living up to their promise, we do have evidence that students are using them to augment their lecture experience.
 This teaching case will investigate how one large first year design/communication course has slowly incorporated online elements to shift the power dynamic from the instructor as centre of focus to the student and their learning experience as central. Begun simply, in the beginning, with audio capture of lectures, we have moved on to video lecture capture, live and recorded online help session, and supplementary videos. This last, importantly, are developed not only by the lecturers, but by interested Teaching Assistants (TA) and even students still in the course. We are, step-by-step, finding the components that will best allow students to be able to construct their own learning experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".