Strategies for information management in education: Some international experience
Bibliographic record
Abstract
Background: Recent analysis of the management of information and communications technologies in South African education suggests strongly that there is only limited strategic thinking that might guide policy-makers, school principals, teachers, learners and suppliers of educational technologies. It is clear that here in South Africa, as elsewhere, the actual practice of technology-mediated education is driven more by the available technologies than by actual learner needs, good management principles and the wider national imperative. There might be lessons to be learned from experience elsewhere.Objectives: This article reports and analyses conversation with eight international educators in Europe, Canada, the United States, New Zealand and Australia. All are managing the impact of technology in different ways (reactive and pro-active), at different levels (pre-primary through to senior citizen), in different roles (teachers, administrators and senior managers) and in different contexts (schools and universities).Method: Open-ended conversations with educators and educational administrators in developed countries were recorded, transcribed and analysed. The qualitative analysis of the content was done in the style of ‘open coding’ and ‘selective coding’ using a qualitative content analysis tool.Results: Whilst technology is still seen to drive much thinking, it is found that that success is not derived from the technology, but from a full and proper understanding of the needs and aspirations of those who are directly involved in educational processes, and by means of a managerial focus that properly recognises the context within which an institution exists.Conclusion: Whilst this result might be expected, the detailed analysis of the findings further reveals the need to manage investments in educational technologies at different levels and in different ways.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".