A Best Practices Approach to the Use of Information Technology in Education
Bibliographic record
Abstract
Based on the author's presentation at the International Conference on Computer Based Learning in Science, this paper discuses some high profile areas of interest and concern in the educational use of information and communication technology (ICT). The paper is influenced partly by a series of nine government funded projects in the use of ICT and partly by experience. In particular, the paper addresses four major areas: (1) establishing an electronic identity, including alternative delivery/online learning issues (e.g., curricula, quality of learning, staffing, administration, equality, and cost) and World Wide Web site development; (2) technological literacy of students, including the Information and Communication Technology Program Studies initiative by the Province of Alberta (Canada); (3) support for the use of ICT with an emphasis on security in a distributed network environment (e.g., physical security, viruses, and network access); and (4) access to information and protection of privacy. (Author/MES) Reproductions supplied by EDRS are the best that can be made from the original document. A Best Practices Approach to the Use of Information Technology in Education PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY-
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 | 0.068 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.031 | 0.014 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".