Proposing an integrated research framework for connectivism: Utilising theoretical synergies
Bibliographic record
Abstract
Connectivism is receiving acknowledgement as a fresh way of conceptualising learning in the digital age. Thus, as a relatively new instructional framework, it is imperative that research on its applicability and effectiveness in a variety of educational contexts is advanced. In particular, a high premium should be placed on context-specific research that is aimed not only at developing general principles but also at improving practice in local settings. Thus, developmental research approaches become imperative and as such it becomes increasingly necessary to have models that would assist scholars to understand the learning ecologies of connectivism. This paper therefore proposes a research framework for connectivism that integrates approaches commonly used in online learning environments. The paper integrates the theories of online communities of practice, design-based research, and activity theory to construct a research framework that is characterised by a synergistic relationship between them. It demonstrates the viability of the model by using an example of how it was operationalised in one research project. The framework, whose potential strength derives from integrating already established theoretical constructs, is presented as a proposal with the intention that it will be critiqued, tried, and improved upon where necessary and ultimately become part of the menu of other tools that serve connectivism research.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".