Influence of Learning Design of the Formation of Online Communities of Learning
Bibliographic record
Abstract
This paper presents the findings of a study on a fully online Bachelor’s level course in Health Sciences at a European University conducted to explore the influence of learning design on the formation and evolution of different types of communities of learning. The impetus for the study came from the well-established effectiveness of community-based learning, a need for understanding learning design and analytics within networked structures and, the lack of theoretical grounding for social network analysis (SNA) in previous literature. Our study uses the Integrated Methodological Framework (IMF) which employs SNA as the key methodology for exploring community-based learning in light of the Communities of Practice (CoP) and Community of Inquiry (CoI) frameworks. The course comprised of three differently designed successive discussion forums spanning three weeks each. Network diagrams and SNA measures clearly showed the impact of the different learning designs on student engagement in the discussion forums. Based on CoP and CoI structural components within the IMF, a comparative analysis of whole-network properties of the three networks indicated the formation of a CoP, initiated and mediated by the tutor in discussion 1, sustained by the students in discussion 2, and disintegrated due to lack of guidance and facilitation in discussion 3. Qualitative analysis on the content of discussion posts revealed the importance of group oriented messages in the formation of the CoP. The paper discusses findings in terms of implications for learning design and analytics in online learning and the role of the tutor in community formation.
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How this classification was reachedexpand
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".