Capitalizing Networked Learning: Connectivism, Multiliteracies and the Architectonics of Pedagogy
Bibliographic record
Abstract
As connectivism is increasingly accepted as a theory of learning for the digital age, scholars and practitioners in education often overlook the dilemma that this creates for its most ardent advocates. In the academic literature, we increasingly find scholarly works that present insouciant descriptions of connectivism. However, such practices often underplay or ignore critiques of connectivism, allowing many of our contentions about its epistemological character and pedagogical effectiveness to calcify. In fact, it is becoming increasingly difficult to rationalize why so many educators have endorsed connectivism as a new theory of learning when there continues to be a need for more empirical testing and greater philosophical substantiation. To illustrate this paradox, this paper examines Stephen Downes’s consideration of connectivism and his connectivist model of literacy. Using the dialogic philosophy of Mikhail Bakhtin, it introduces an architectonic model of connectivism and multiliteracies as an alternative discourse and pedagogical paradigm. A key finding from this study suggests that the lack of attention to capitalist practices, power, and the intermediality of texts in networked learning help to conceal the ways in which connectivist practices rearticulate behaviorism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".