Learning in school, home, and community : ICT for early and elementary education : IFIP TC3/WG3.5 international working conference on learning with technologies in school, home and community, June 30-July 5, 2002, Manchester, United Kingdom
Bibliographic record
Abstract
Preface. Part One: Learning. Learning in school and out: Formal and informal experiences with computer games in mathematical contexts N. Yelland. Using technology to encourage social problem solving in preschoolers M.B. Medvin, D. Reed, D. Behr, E. Spargo. Using electronic mail communication and metacognitive instruction to improve mathematical problem solving B. Kramarski, A. Liberman. Online searching as apprenticeship M. Pearson. The use of virtual reality three-dimensional simulation technology in nursery school teacher training for the understanding of children's cognitive perceptions Y.J. Katz. Exploring visible mathematics with IMAGINE: Building new mathematical cultures with a powerful computational system I. Kalas, A. Blaho. Cooperative networks enable shared knowledge: Rapid dissemination of innovative ideas and digital culture K. Crawford. Part Two: Teaching. Developing an ICT capability for learning S. Kennewell. Separated by a common technology? Factors affecting ICT-related activity in home and school D. Benzie. The interaction between primary teachers' perceptions of ICT and their pedagogy A.M. Loveless. Capacity building in tele-houses: A model for tele-mentoring M. Turcsanyi-Szabo. Part Three: Policy. ICT for rural education: A developing country perspective P. Hepp, E. Laval. National plans and local challenges: Preparing for lifelong learning in a digital society S. Rosvik. Learning online: E-learning and the domestic market in the UK M. Scanlon, D. Buckingham. Glimpses of educational transformation: Making choices at a turning point B.S. Somekh. How do we know that ICT has an impact on children's learning? A review of techniques and methods to measure changes in pupils' learningpromoted by the use of ICT M.J. Cox. Index.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".