Bibliographic record
Abstract
Emergent Learning, Connections, Design for LearningIn a digital world dominated by social media, networks, and instant communication, the creation of viable, effective, and sustainable learning environments remains a challenge for designers, administrators, teachers, and learners.The purpose of this special issue was to examine this challenge through a lens of connections, emergence, chaos, complexity, fractals, and quantum theory, which are terms that originated and have been widely studied in the natural sciences, and which are now appearing as important interdisciplinary ways to understand both natural and social sciences, including education.The question therefore arises, are the traditions of what it means to teach and learn being challenged by these concepts, or are we simply experiencing the natural evolution of education through a process of emergence, connections, and the design experience?As editors of this issue, we proposed the following frameworks to provide a prompt for the submitted papers.• Emergence encourages random encounters, paying attention to your neighbours, and "more" being different.Through such encounters and interactions we can look for patterns in the signs which can be extrapolated to an entire system, the intelligence of which comes from the bottom up, and where low-level rules can create high levels of sophistication.• The connections being made between people through social networks has emphasised "connectivism," an emergent theory of learning where the interactions that are generated by these connections, whether informal or formal, have the potential to result in new, emergent knowledge.• For designers, taking account of emergence and connections can challenge the tradi-
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.162 |
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".