Academic staff development in the area of technology enhanced learning in the UK HEIs
Bibliographic record
Abstract
This paper reports on a study on staff development in the area of technology enhanced learning in UK Higher Education Institutions (HEIs) that took place in November 2011.Data for this study were gathered via an online survey emailed to the Heads of e-Learning Forum (HeLF) which is a network comprised of one senior member of staff per UK institution leading the enhancement of learning and teaching through the use of technology.Prior to the survey, desk-based research on some universities' publicly available websites gathered similar information about staff development in the area of technology enhanced learning.The online survey received 27 responses, approaching a quarter of all UK HEIs subscribed to the Heads of e-Learning forum list (118 is the total number).Both pre-1992 (16 in number) and post-1992 Universities (11 in number) were represented in the survey and findings indicate the way this sample UK HEIs are approaching staff development in the area of TEL.The survey's main research question was 'what provision do UK HEIs make for academic staff development in the area of technology enhanced learning'.Twelve questions, both closed and openended, were devised in order to gather enough information about how staff development needs in the area of technology enhanced learning are addressed by different UK institutions.Following the justification of the adopted research methodology, the findings from the online survey are analyzed and discussed and conclusions are drawn.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".