Presentation Of The Paper "A Metamodel Proposal For Developing Learning Ecosystems" In Hcii 2017
Bibliographic record
Abstract
This is the presentation of the paper entitled “A metamodel proposal for developing learning ecosystems” in the Emerging interactive systems for education session at the HCI International 2017 Conference, held in Vancouver, Canada, 9 - 14 July 2017. The definition and development of learning ecosystems is a complex process with a wide range of requirements. Although two different institutions or companies share the same problems and goals regarding their learning and training processes, the learning ecosystems to support them are different. The components of the ecosystem, including the human factor as a key element, and the relationships between them, change over time. In other words, learning ecosystems evolve as natural ecosystems; there are many factors, both internal and external, that influence an entity. The authors have defined and developed different learning ecosystems. Moreover, they have transferred the same learning ecosystem, specifically a learning eco-system for knowledge management in a PhD Program, to different domains. These experiences have provided the required information to define the ecosystems metamodel following the Model Driven Architecture proposed by the Object Management Group. The aim of this metamodel is define a Domain Specification Language to develop learning ecosystems.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".