Social Learning Frameworks for Analyzing Collaboration with Marginalized Learners
Bibliographic record
Abstract
Collaborative learning has potential to serve as a platform for fostering social connection, particularly in non-traditional contexts. Recent years have seen an increase in (long overdue) interest in supporting communities within such contexts through research. This has been often approached through ethnographic methods such as naturalistic observations, which are suitable for smaller, marginalized populations, However, the analysis of data produced by such methods often lack standardization, which limits generalizability of results and makes comparison across populations and learning contexts challenging. In this paper, we argue how greater grounding of data analysis in collaborative learning theories can provide standards for more meaningful comparison across contexts. We review Vygostky's social learning theories, shared social regulation of learning, and the trialogical approach. We discuss how anchoring inductive and deductive approaches in social frameworks may yield standardization metrics for unstructured, qualitative data from studies of social learning. We base this in our ongoing research on collaborative language learning between immigrant grandparents and grandchildren.
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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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".