Research-based implications for policy and practice: outcomes from EDUsummIT 2019 (Quebec) – The 6th International Summit on Information Technology in Education
Bibliographic record
Abstract
EDUsummIT (International Summit on ICT in Education) is a global knowledge building community of researchers, educational practitioners, and policy makers committed to supporting the effective integration of research and practice in the field of ICT in education. In 2019, more than 100 researchers, practitioners and policy makers from across the world have gathered to discuss ways to fast track what researchers know about information technology in education, into sound policies and best practices at the local, regional, and national/international level. This symposium focuses on outcomes from the Thematic Working Groups (TWGs) from EDUsummIT 2019, then seeks audience feedback and input on the findings and ways to best bring the Call to Action recommendations into implemented reality. Five Thematic Working Group topics relevant to an EdMedia audience will be featured in this symposium, followed by an integrative Call to Action summary. Presenters will invite feedback, suggestions and recommendation from attendees during all phases of the symposium. Especially encouraged will be discussion of how individual attendees might take appropriate portions of the recommendations away from the symposium and begin implementations at their local levels.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".