The Emergence of Complexity Thinking and Its Influence on Educational Research
Bibliographic record
Abstract
The fast-paced and ever-changing modern world is witnessing the onset of a novel era of teaching methods, which often combine elements of traditional approaches such as reductionism and holism while providing prospects for fresh discourse, ideas, and outlooks. This paper aims to explain how perspectives on how education is understood have changed throughout time until complexity thinking emerged in more recent decades (Jacobson & Wilensky, 2022; Morin, 1992, 2011). In pursuit of this goal, the main characteristics of reductionism, holism, and systemic thinking are discussed, as well as how such transformations in perspectives have influenced the emergence of complexity thinking. As explained by Davis et al. (2015), complexity thinking started to spread among educational researchers not as a way of superimposing previous theories, but to present new points of view and possibilities instead. Complexity thinking in education is innovative as it goes against previous beliefs that learning occurs in linear ways, meaning that it recognizes and deals with conflict, uncertainty, and disharmony in learning processes. According to Jacobson and Wilensky (2022), educational researchers should continue to explore innovative pedagogies and technologies that embrace complexity, bringing crucial contributions to theories of teaching and learning.
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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 | 0.046 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".