From Connectivism to MOOCs:Connecting Knowledge & Sharing Resources——An Interview with Stephen Downes
Bibliographic record
Abstract
The network era witnesses the disruptive revolutions in the education mode,where learners face the new challenges such as how can they deal with the open learning environments to actively develop learning competences instead of waiting for the teachers to bring knowledge forward. That is how people integrated the knowledge sources of globalization together to form a sense- making learning in the explosive growth of information and fragmented knowledge. In this interview,we are very pleased to have the originator of Connectivism and the Massive Open Online Course( MOOCs) —Stephen Downes to share with us his viewpoint on open learning,and propose the approachto learn effectively in the digital age. As a pioneer in the field of online learning,Stephen advocated that through establishing a network connection between nodes of knowledge under open educational resource movement,people can construct the learning network to acquire knowledge and develop competences effectively. This idea is widespread in the world from Connectivism to the MOOCs.Stephen Downes works for the National Research Council of Canada where he has served as a Senior Researcher.He specializes in the fields of online learning,new media,pedagogy and philosophy,and is perhaps best known for his daily newsletter,OLDaily,which is distributed by web,email and RSS to thousands of subscribers around the world. Stephen,known as the originator of the Massive Open Online Course( MOOCs) with George Siemens,is also a pioneer in the domain of Learning object and Metadata,and the first adopters and developers of RSS content syndication in education,which leads to the concept of e- learning 2. 0. As an editor and consultant of a number of( online) professional media,he often presented the reports and lectures about the field of online learning,and published hundreds of articles both online and in print about learning networks and related technologies.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".