HOW TO BE INNOVATIVE DESIGNING EDUCATIONAL AND INTERACTIVE ENVIRONMENT FOR CHILDREN- PAPER PRESENTED AT ED-MEDIA 2000, MONTREAL, CANADA
Bibliographic record
Abstract
LUDOMATICA looks to effectively attend children's educational needs. It seeks to help them to creatively think and act, validating their rights to high-quality education and to actively participate as social change agents. This validation is accomplished through innovations in formal, non-formal and informal education, using Information and Telecommunication Technologies within a non-conventional pedagogical proposal, thus building playful, creative, collaborative and interactive learning environments for children. [1] As a graphic designer who works at Ludomatica creating interactive learning environments for Colombian children between the ages of 7 and 12 (Elementary school ages), I challenge myself to design exciting spaces where kids will feel comfortable and will take control of their pursuit of knowledge. Ludomatica’s target population comes from urban or rural marginal zones where it has been abandoned, mistreated, and/or has lived in high-risk situations. These children are under State protection [2], most of them do not have very good academic records, some of them have never been in a traditional academic institution, and others do not even know how to read nor write. The
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".