Rethinking learning in the digital age:EDUsummIT summary reports
Bibliographic record
Abstract
This ebook is a collection of outcome reports by the thematic working groups (TWGs) of EDUsummIT 2017. 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. EDUsummIT was founded in 2009 to extend and further develop the work undertaken by the authors of the International Handbook of Information Technology in Primary and Secondary Education, edited by Joke Voogt and Gerald Knezek (2008). Since its inception, EDUsummIT has been held five times, firstly in the Hague (2009), then Paris (2011), Washington D.C. (2013), Bangkok (2015) and most recently in Borovets (2017). Between 70 and 140 participants from six continents have attended each of the EDUsummIT meetings. While EDUsummIT participants meet biennially, thematic groups focusing on pertinent research topics in ICT and education are formed prior to the Summit to prepare discussion papers. These papers are further developed during EDUsummIT. After each EDUsummIT, TWG findings are published in international journals and presented at major conferences. Previous EDUsummITs have been organised in association with international and national organisations actively supporting the use of information technology in education. These organisations include the Society for Information Technology and Teacher Education (SITE), the International Society for Technology in Education (ISTE), Kennisnet (The Netherlands), the International Federation for Information Processing (IFIP) Working Group 3.3 (Research into Educational Applications of Information Technologies), the Association of Teacher Educators (ATE), the Teacher Development and Higher Education Division at UNESCO and UNESCO Bangkok.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".