The Potential of Reflective Journals in Studying Complexity 'In Action'
Bibliographic record
Abstract
As educators embrace theories of complexity to inform their teaching and research practice, theoretically relevant methods will be required to appropriately conduct and study complexity-based approaches to education. Action research has been identified as offering significant potential for studying complexity, acting as a form of ‘real life modeling’ for learning and teaching. In this paper it is argued that reflection, a key aspect of action research, can be a productive method for both studying and working with complexity in educational contexts. Reflective journals, more specifically, provide scope not only for gathering research data but also for promoting learning and change. As a teaching approach, reflective journals can reduce the impact of external control while providing opportunities to promote and document instability and disequilibrium. Reflective journals allow for documentation of emergence and bifurcation and embrace participants’ involvement in interpretation of data in inherently non-linear ways. Reflective journals assist to build up an holistic picture of the interplay between individuals’ histories and their current and emergent ‘state’, thus providing insight into ‘sensitivity to initial conditions’. This paper illustrates these theoretical ideas through a case study derived from a course in information and communication technology (ICT) for practicing teachers.
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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 | 0.122 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| 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".