National staff development model for LSC staff, IAG staff and partnership staff: research report, quality development

This report outlines the findings and recommendations resulting from a national Quality Development Fund (QDF) project commissioned by the Learning and Skills Council (LSC) in July 2002. The aim of the project was to produce a development and training programme aimed at information advice and guidance (IAG) staff. Outputs from the project include a staff development framework on compact disk (CD), detailing the skills and knowledge required in various work areas within the IAG initiative and correlating these with specific training and development opportunities. This report also lists eight recommendations for further improvement put forward by the project team. This report is of interest to IAG managers and coordinators in local Learning and Skills Councils (local LSCs), IAG partnerships and other IAG organisations. The absence of a prescribed IAG staff development and competence model has meant that local partnerships have been empowered to come up with their own definitions of ‘competence’ and ‘experience’ locally. This flexibility has been enhanced by the promulgation of the matrix standard and its predecessor which state that ‘staff competence and the support they are given are sufficient to deliver the service’ but intentionally does not define what competencies are required to deliver IAG. The aim of this project was to develop a comprehensive competence-based training pack for IAG delivery staff, front-line and support staff in delivery organisations, IAG coordinators/ managers and LSC staff involved in the initiative

Curvature-aided Incremental Aggregated Gradient Method

We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the problem of training a classifier for a d-dimensional problem, where the number of training data is $m$ and $m \gg d \gg 1$, the CIAG method seeks to accelerate incremental aggregated gradient (IAG) methods using aids from the curvature (or Hessian) information, while avoiding the evaluation of matrix inverses required by the incremental Newton (IN) method. Specifically, our idea is to exploit the incrementally aggregated Hessian matrix to trace the full gradient vector at every incremental step, therefore achieving an improved linear convergence rate over the state-of-the-art IAG methods. For strongly convex problems, the fast linear convergence rate requires the objective function to be close to quadratic, or the initial point to be close to optimal solution. Importantly, we show that running one iteration of the CIAG method yields the same improvement to the optimality gap as running one iteration of the full gradient method, while the complexity is $O(d^2)$ for CIAG and $O(md)$ for the full gradient. Overall, the CIAG method strikes a balance between the high computation complexity incremental Newton-type methods and the slow IAG method. Our numerical results support the theoretical findings and show that the CIAG method often converges with much fewer iterations than IAG, and requires much shorter running time than IN when the problem dimension is high.Comment: Final version submitted to Allerton Conference 2017 on Oct 8, 201

Local information, advice and guidance for adults in England: towards a national framework

"Starting from April 1999, the Government ... intends to work on a partnership basis with local providers of [learning opportunity] information, advice and guidance (IAG) services in England to bring about significant improvements in the quality and coverage of local services... The overall aim is to ensure that a local information and advice service of reliable quality is available to adult learners and potential learners who live in every part of England... Beyond setting out the Government’s broad objectives for the development of IAG services, the purpose of this paper is to initiate a dialogue with providers and other interested parties about how those objectives can best be brought about." - Page 1. Consultation responses closed 12/02/1999