Creating a transformative learning community: Generating collective knowledge in an early childhood graduate course
Bibliographic record
Abstract
This collaborative paper explores the process of creating a learning community as a complex learning system in an early childhood graduate course. Reflected here are the course instructor's experiences and those of various students who took the course at different times. This exploration was inspired by the students’ (re)created and repeated experiences of transformation as a result of taking the course. As a group, the authors asked; is there a particular ingredient in that course that facilitated the repeatable nature of this transformation? Complexity science/theory as applied to notions of learning and teaching (Capra, 2002; Davis & Simmt, 2003; Waldrop, 1992) provided the framework for this exploration. Five conditions that must be present in order for individuals to come together into collectives that might supersede the possibilities of the individual—internal diversity, redundancy, decentralized control, organized randomness, and neighbor interactions (Davis & Simmt, 2003)—were used to examine the collective learning experiences in that course. Reflective journal entries and the instructor's personal reflections on the process of planning, conducting, and evaluating the course were used to illustrate how these conditions were created and how they were used to occasion the type of learning students experienced as transformative.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".