Conceptualizing Formal And Informal Learning In Moocs As Activity Systems
Bibliographic record
Abstract
This article considers formal and informal learning activities in massive open online courses (MOOCs). MOOCs are often broadly positioned as either cMOOCs (based on connectivistic pedagogies) or xMOOCs (based on cognitivistic/behavioristic pedagogies). In a recent International Review of Research in Open and Distance article, Anders (2015) proposed a tripartite scheme for placing MOOCs on a continuum from content-based (xMOOCs) to community/task-based (cMOOCs) to network-based hybrids. Anders’ model is based on a meta-analysis of literature-based case studies of existing pedagogical approaches in MOOCs. In contrast, our in situ case study examined an emergent, hybrid MOOC design. The study shared in this article is focused on establishing the presence of both formal and informal learning activities in a network-based hybrid approach to MOOC design. The establishment of these two activity systems extended to include opportunities for boundary crossings between them. An outcome is a cultural-historical activity theory-informed model that extends commonly used and recognized MOOC typologies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".