Bibliographic record
Abstract
Ethical education practices require that all students have access to quality learning resources, necessary learning supports, diverse learning strategies, and deep learning opportunities. When it comes to learning strategies and opportunities, collaborative learning practices foster deep learning through socio-cultural interactions, asserting that individual learning is limited compared to what can be learned as a community. Education systems have an ethical obligation to ensure that what is advocated for in curricula can be achieved and will be supported. Although K–12 curricula are typically rooted in collaborative approaches, many asynchronous secondary online learning courses continue to be associated with individual learning approaches. This research used insights gleaned from 35 survey responses and 18 semi-structured interviews with secondary asynchronous distance learning teachers to analyze how collaborative learning is actualized and examine barriers to its implementation. Collaborative online learning opportunities were increasingly prevalent when communities outside of the school were leveraged for experiential learning and when students were paced as a cohort. The data indicated that an increase in collaborative learning was not likely to occur unless the learning ecosystem valued online learning as equitably as face-to-face learning in terms of investment in research-based pedagogy, student support, teacher support, and teaching and learning resources. Until such time, distance learning students will be disadvantaged concerning building collaborative competence that can lead to deeper learning opportunities.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".