The Ideals and Reality of Participating in a MOOC
Bibliographic record
Abstract
'CCK08' was a unique event on Connectivism and Connective Knowledge within a MOOC (Massive Open Online Course) in 2008. It was a course and a network about the emergent practices and the theory of Connectivism, proposed by George Siemens as a new learning theory for a digital age. It was convened and led by Stephen Downes and George Siemens through the University of Manitoba, Canada. Although the event was not formally advertised, more than 2000 participants from all over the world registered for the course, with 24 of these enrolled for credit. The course presented a unique opportunity to discover more about how people learn in large open networks, which offer extensive diversity, connectivity and opportunities for sharing knowledge. Learners are increasingly exercising autonomy regarding where, when, how, what and with whom to learn. To do this, they often select technologies independent of those offered by traditional courses. In CCK08 this autonomy was encouraged and learning on the course was distributed across a variety of platforms. This paper explores the perspectives of some of the participants on their learning experiences in the course, in relation to the characteristics of connectivism outlined by Downes, i.e. autonomy, diversity, openness and connectedness/interactivity. The findings are based on an online survey which was emailed to all active participants and email interview data from self-selected interviewees. The research found that autonomy, diversity, openness and connectedness/interactivity are indeed characteristics of a MOOC, but that they present paradoxes which are difficult to resolve in an online course. The more autonomous, diverse and open the course, and the more connected the learners, the more the potential for their learning to be limited by the lack of structure, support and moderation normally associated with an online course, and the more they seek to engage in traditional groups as opposed to an open network. These responses constrain the possibility of having the positive experiences of autonomy, diversity, openness and connectedness/interactivity normally expected of an online network. The research suggests that the question of whether a large open online network can be fused with a course has yet to be resolved. Further research studies with larger samples are needed, as is an investigation into the ethical considerations which may need to be taken into account when testing new theory and practice on course participants.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".