During the last thirty years the rapid development of technology has left many educators struggling to come to terms with the changes the integration of technology brings to the teaching-learning environment. Governments and education administrators aroun
Bibliographic record
Abstract
During the last thirty years the rapid development of technology has left many educators struggling to come to terms with the changes the integration of technology brings to the teaching-learning environment. Governments and education administrators around the world are currently diverting limited resources into the provision of infrastructure and computers in the belief that the use of technology as a means of education delivery has the potential to significantly enhance teaching strategies and resources currently available to schools. For schools and teachers, the push for the implementation of technology from the administrative levels has meant changes to the learning environment, the necessity to acquire new skills and issues of accountability. Thus the impact of technology in education and on learning has been the subject of much debate and an increasing body of research has endeavoured to assess the impact of various technologies on student learning, with mixed results. The purpose of this article is to briefly review the research on the impact of technology in education, determine some of the recurrent issues identified by the research and to examine the role of the teacher librarian as a possible facilitator for change and the effective integration of technology in the curriculum.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".