Bibliographic record
Abstract
Since the introduction of connectivism as a learning theory in 2004 a body of literature has developed both offering criticisms and expanding on applications and empirical validation. This article surveys recent literature on the topic, grouping it into themes, and developing an understanding of current perspectives in connectivism. It surveys current perspectives and criticisms of connectivism, views of connectivism as a pedagogy and as a theory of learning, recent evidence supporting connectivism, and a wider understanding of connectivism as it is developing today. Résumé Depuis l’introduction du connectivisme en tant que théorie de l’apprentissage en 2004, un corpus de littérature a été développé, offrant à la fois des critiques et une extension des applications et une validation empirique. Cet article examine la littérature récente sur le sujet, en le regroupant par thèmes et en développant une compréhension des perspectives actuelles en matière de connectivisme. Il passe en revue les perspectives actuelles et les critiques du connectivisme, les vues du connectivisme en tant que pédagogie et théorie de l’apprentissage, les preuves récentes soutenant le connectivisme et une compréhension plus large du connectivisme tel qu’il se développe aujourd’hui.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".