Tele-learning : the challenge for the third millennium : IFIP 17th World Computer Congress - TC3 stream on tele-learning, August 25-30, 2002, Montréal, Québec, Canada
Bibliographic record
Abstract
IFIP And TC3. Foreword. Stream 3 International Programme Committee. The Challenge For The Third Millenium. Teacher Education (TED) Track - Invited Paper. Design Of Teacher E-Learning: The Scenario Model B.B. Andresen. Teacher Education (TED) Track - Papers. Developing Technology Competencies Among Egyptian College Of Education Students M.M. Aly. Co-Operative Parent-Child Learning: In Computerised Technological Environments U. Armon. ICT-Supported Teaching And Learning: Some Priorities And Objectives For The Future R.M. Bottino. Learning Aids And Learners' Activities In The Field Of Object-Oriented Modelling T. Brinda, S.E. Schubert. Experiment Around A Training Engine A. Brygoo, et al. Collaborative Learning Of Mathematics: Problem-Solving And Problem-Posing Supported By 'Knowledge Forum' E. De Corte, et al. Teaching Historical Truth: Pages From The History Of Russian Computer Science Y.I. Fet. Creating Technology Mentor Teachers Through A Digital Sabbatical Opportunity On-Line C.P. Fulford, et al. Pedagogical ICT Licences: A Danish National Initiative To Offer Teachers Technology Literacy U. GjOrling. Electronic Testing System B. Gradinarova, O. Jelezov. Banjos On The Snowy: Implementing E-Activism In Education L. Kelly, P. Nicholson. Streaming Technology - How Does It Affect Education? Report From A Project Using Satellite-Based Communication A. Knierzinger, et al. National Strategy For Teacher Training In New ICT Use: The Ukrainian Case V. Kolos. 5 Additional Chapters. Life-Long Learning - Professional Development (LL-PD) Track - Invited Paper. Life-Long Learning In Virtual Learning Organisations: Designing Virtual Learning Environments T.J. Van Weert. Life-LongLearning - Professional Development (LL-PD) Track - Papers. Using Case Studies To Promote Life-Long Learning R.M. Aiken, et al. Virtual Institute For The Modelling Of Industrial Manufacturing Systems: Integration Of On-Line And 'Face-To-Face' Learning In An International Platform For Teaching And Research S. Cavalieri, et al. Quality Of Working Life, Knowledge-Intensive Work Processes And Creative Learning Organisations: Information Processing Paradigm Versus Self-Organisation Theory K. Fuchs-Kittowski, F. Fuchs-Kittowski. Public And Private Partnerships For Intense E-Business Training T. Gulledge, J. Sherwin. Adaptive Context-Aware Learning Environments: Live Spaces As A Basis For Life-Long Learning In Computer Science G. Quirchmayr, J. Slay. Learning Environments And Responsibility: Three Types Of Learning Environments M. Hezemans, M. Ritzen. Issues In The Organisational And Change Context For Innovations Using ICT In Higher Education M. Saunders, et al.Cultural Differences Of Female Enrolment In Tertiary Education In Computer Science B. Schinzel. 5 additional Chapters. Learning Technologies (LT) Track - Invited Paper. Modelling And Delivering Distributed Learning Environments G. Paquette. Learning Technologies (LT) Track - Papers. A Distributed And Co-Operative Environment To Help The Rehabilitation Of Children With Down's syndrome A.M.P. Almeida, F.M.S. Ramos. Information systems and educational engineering: Bridging two concepts through meta modelling M.-N. Bessagnet, et al. Emerging base for telE-learning in India R.M. Bhatt, K. Subramanian. Web-adaptive training system based on cognitive student style M.A.M. Souto, et al. A methodological and physical instrumentation to support experimentation in telE-learning A. Dufresne
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.118 | 0.033 |
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".