School Informatics: The Vision, the Learning, the Information, the Technology and the Need for Research
Bibliographic record
Abstract
School librarians and school libraries have been affected by a number of changes over the past decade. Development in leaming, such as the development of information skills programs; in teaching, such as the greater use of learning resources by teachers; and in technology, such as the availability of electronic information resources such as the Internet; have all affected the nature of the school librarian's work. Taking a holistic view of schools, it can be seen that developments in these areas have been the focus of research of a number of related disciplines. Researchers in the area of learning have sought to identify the impact of new technologies on the learning process in schools. Researchers in IT in education have studied the increasing sophistication of computer assisted learning packages and school networks. Researchers in educational administration have examined the potential impact of IT of improved record keeping and information management in schools.Researchers in school librarianship have examined the growth of information skills/literacy programmes in schools as well as the growth in the range of electronic information resources such as CO-ROMs, online databases and the Internet This paper proposes that these disciplines could usefully contribute to a new discipline entitled school informatics which would examine the impact of new technologies on leaming and teaching from a perspective which seeks to examine how learning and teaching can be improved in schools by the integon of related but as yet separate aspects of IT in today's schools. The central focus of school infomatics should be on learning in the classroom, in the school library and elsewhere and not on individual advances in technology. This paper outlines a vision for school infomatics and the relation of that vision to learning, teaching, information resources and information technology in schools. A research study of 2 UK schools' views on intranet developments is induded in the paper.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.034 | 0.034 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".