Employer-responsive provision survey: a reflective report
Bibliographic record
Abstract
Employer engagement is an area of increasing importance to the strategic development of higher education institutions (hereafter referred to as 'institutions').The skills required of the future workforce and the predicted demographic changes are encouraging institutions to become more fl exible in the types of learner they recruit, the range of learning opportunities they make available and the modes of study they offer.There is a growth in the number of learners accessing higher education while being employed and using their workplace as a site of learning.Where provision is developed for and in conjunction with particular employers, this may be termed employerresponsive provision.There are many positive aspects to the growth of this activity, but it is recognised that it can be considered as more complex and potentially present different challenges compared to more traditional provision.2During spring 2009, the Quality Assurance Agency for Higher Education (QAA) undertook a project through its Institutional and Subject Centre Liaison Schemes to explore the extent and perceptions of employer-responsive provision among institutions in England, Wales and Northern Ireland.The aim of the project was to demonstrate the various approaches institutions are adopting to the quality assurance of such provision, and to inform QAA as to what additional support, information and guidance might be necessary to ensure that both internal and external quality assurance arrangements are appropriate and effective for this kind of provision. 3The project involved collecting and analysing data from two main sources: semi-structured interviews with institutional representatives involved in the quality assurance and delivery of work-based learning and employer engagement, and similar discussions with relevant staff of the Higher Education Academy's Subject Centre Network (see Appendices, p 37). Sixty institutions and 11 Subject Centres participated in the survey and the project also drew on discussions which took place at a QAA conference hosted in July 2009. 1 The project was informed by the reference points published by QAA to support institutions in the management of quality and standards, collectively known as the Academic Infrastructure. 2 These consist of: The framework for higher education qualifi cations in England, Wales and Northern Ireland (FHEQ) the Code of practice for the assurance of academic quality and standards in higher education (Code of practice) subject benchmark statements programme specifi cations. 4A number of reports were considered in this project, including a statement published by QAA in July 2008 on 'Quality assurance and the HEFCE priority for higher education learning linked to employer engagement and workforce development' (hereafter referred to as the QAA Statement) 3 and a range of publications by other organisations.Through this project, QAA has gained a better understanding of: how institutions see their position regarding employer engagement and work-based learning: 20 institutional respondents stated that they had changed their mission statement in order to become more business-facing and responsive to employers the range of approaches being adopted by institutions for the quality assurance of work-based learning and employer-responsive provision the quality assurance matters faced by institutions with respect to this provision 1 1 More details can be found at: www.qaa.ac.uk/events/liaisonconference09.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
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".