Capitalizing Networked Learning: Connectivism, Multiliteracies and the Architectonics of Pedagogy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it