Enhancing ‘ICT Teaching’ in English Schools: Vital Lessons
Bibliographic record
Abstract
Despite substantial investments in digital technology in schools the impact has been less than advocates anticipated. This raises issues about the effectiveness of past approaches to the continuing professional development (CPD) of teachers. Vital was a £9.4million programme, funded by English governments between 2009 and 2013, to enhance the use of digital technology and the teaching of computing in schools. Vital, which was provided by the Open University (UK), developed an evolving range of professional development, informed by a review of the literature and extensive experience of supported open learning and developing online communities. Underpinning all of Vital’s provision was a view of teachers as experts, and practitioner research as incorporating all the key elements of effective CPD identified in the literature. The evolving models of CPD developed by Vital during three distinct phases of its operation are described. These include: supported online courses; community websites; TeachMeets and TeachShares; the In-house Professional Development Partnership; and the development and sharing of evidence through EdFutures.net. Based on Vital’s experiences some suggestions are made about what constitutes effective CPD.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".