Bibliographic record
Abstract
The digital revolution enabled by social and ubiquitous technologies is constantly transforming macro and micro levels of society including industry, organizations and government as well as ways in which we communicate, we work, and we carry on our daily lives. Education is therefore also being challenged to respond to evolving societal demands by supporting the development of competent and engaged citizens. In this context, individuals’ capability to get involved and exploit the affordances of networked environments for learning and development may condition their opportunities to cope with societal and labor demands. In this chapter, the metaphor of learning ecologies is proposed to provide a framework from which to analyze interactions between individuals and their environment, and the way their experiences across different contexts throughout life promote and shape learning processes. Learning ecologies allow us to explore frontier pedagogies connecting formal, non-formal and informal educational contexts, acting as personal strategies that may orchestrate life-long, life-wide and life-deep learning. We start by defining and framing learning ecologies, providing the theoretical roots and reviewing some recent studies in the field. Next, we propose constructs and models but also strategies and tools that may be of help to enhance and support personal ecologies for learning. Finally, the concept of personal pedagogies is proposed to refer to a set of autonomy and agency skills and attitudes that can be dynamically integrated by individuals to support an ecology for self-development and personal learning. We articulate from this perspective several trends in the area of self-directed learning located in the technological and pedagogical intersection: MOOCs, current awareness, e-portfolios and social networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".