The class as a learning entity (complex adaptive system): An idea from complexity science and educational research
Bibliographic record
Abstract
Educational theorists are making increasing use of the metaphors and concepts of complexity thinking in their discourses. In particular, Professors Brent Davis, Elaine Simmt, and Dennis Sumara have written extensively about using complexity thinking to shift attention from the individual student as the locus of learning (cognizing agent) to the social collective—the class—as the locus of learning. In this model, the class (students and teacher) is (potentially) a complex adaptive system. The students and teacher remain complex adaptive systems in their own right, but through dynamic local interactions there is the possibility of emergent behaviours indicative of learning that transcends that of the individuals within the class. The social collective we know as a class becomes an instance of the Aristotlean adage, “The whole is greater than the sum of its parts.” (With the coda that we cannot understand the whole by merely understanding the components.) Davis, Simmt, and Sumara have segued from complexity-informed descriptions of educational collectives to discussions about facilitating the self-organization of classes into complex adaptive systems – learning systems, in their language. In this paper, I discuss complex adaptive systems and look at how Davis, Simmt, and Sumara developed their thesis that the class collective, rather than individual student, is the appropriate level to investigate learning and teaching. We conclude by addressing some of the possibilities and challenges inherent in such a redescription of communities of learners.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".