Learning Ecologies: Connecting Social Constructivism and Distance Learning
Bibliographic record
Abstract
Social constructivist teaching practices are understood to foster deep learning through socio-cultural interactions, asserting that individual learning is limited in comparison to what can be learned as a community. Social constructivist principles are embedded within Saskatchewan curricula with little mention of how that might be achieved in asynchronous distance learning. The lack of direct connections places a burden on distance learning teachers, policymakers, and course designers to discover how to actualize social constructivist education practices, within an asynchronous learning environment. This mixed methods study used an online survey and semi-structured interviews to understand teachers’ experiences with social constructivist practices in high school asynchronous distance learning within Saskatchewan. \nThrough the reflexive thematic analysis of the semi-structured interviews and the open-ended survey questions, three themes were constructed. The “Teacher as Catalyst” theme identified the dynamic role that teachers take on to be responsive to student learning needs (e.g., creating flexible learning paths, increasing resources, and strengthening relationships). The theme “Student Agency” represents two key teacher perspectives regarding students’ reluctance to take part in collaborative learning with peers. Namely, student readiness and student buy-in. The final theme, “Alignment of Purpose, Pedagogy, and Person” depicts how the learning ecosystem influences pedagogical decisions and the learning experiences of students.\nThe findings support the integral role of student-teacher relationships to support learning and suggest that under the right conditions, the intent and stance of the Saskatchewan curricula can be achieved in asynchronous distance learning environments provided there is an alignment in purpose, pedagogy, and person. However, the research findings did not support a strong student desire for social constructivist practices.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".