Languaging Network Learning: The Emergence of Connectivism in Architectonic Thought
Bibliographic record
Abstract
As technological advancements and online education transform higher education, the achievement gap among students is widening rather than closing. Critics suggest that we need to reassess the promises of online education and the connectivism or network learning that is sometimes employed as its pedagogical underpinning. As scholars and practitioners struggle to define connectivism as a learning theory, many often exclude language as a feature in its conceptualization. This practice is at odds with architectonic thought, the philosophical tradition in which constructivist theories of learning are rooted. This article reveals the central role that language and texts play in architectonic thought and why they are inseparable from our understanding of knowledge and network learning. When we recognize language as a medium and model for reflection and criticality in the architectonic tradition, we are better positioned to use pedagogy and computer technology to transform online education and reorient our competing views of connectivism.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".