Otley College: An Online toolkit for tutorials in rural- based FE – meeting new funding needs

As part of an LSIS Regional Response Fund project, Otley College has developed an online toolkit for one-to-one tutorials, based on research undertaken with practitioners and learners. The toolkit has been developed to meet the needs of students and tutors, and attempts to fulfil the requirement for tutorials within a new straightened funding agreement.

FleSSR project: Sustainable business models for community cloud infrastructure in the UK higher education sector

Cloud-based infrastructure essentially comprises two offerings, cloud-based compute and cloud-based storage. These are perhaps best typified for most people by the two main components of the Amazon Web Services (AWS)1 public cloud offer, the Elastic Compute Cloud (EC2)2 and the Simple Storage Service (S3)3, though, of course, there are many other related services offered by Amazon and many other providers of similar public cloud infrastructure across the Internet.

The CAMEL Project: Collaborative Approaches to the Management of E-Learning

CAMEL is short for Collaborative Approaches to the Management of e-Learning and was a project funded by the HEFCE Leadership, Governance and Management programme. It set out to explore how institutions who were making effective use of e-learning and who were collaborating in regional lifelong learning partnerships might be able to learn from each other in a Community of Practice based around study visits to each of the partner institutions. This short publication highlights some of the things CAMEL participants found out about e-learning and about each other.

MIRACLE Project Plan

Social scientists have used agent-based models (ABMs) to explore the interaction and feedbacks among social agents and their environments. The bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behaviour with a high level of detail; however the stochastic nature and potential combinations of parameters of such models create large non-linear multidimensional “big data,” which are difficult to analyze using traditional statistical methods. Our proposed project seeks to address this challenge by developing algorithms and web-based analysis and visualization tools that provide automated means of discovering complex relationships among variables. The tools will enable modellers to easily manage, analyze, visualize, and compare their output data, and will provide stakeholders, policy makers and the general public with intuitive web interfaces to explore, interact with and provide feedback on otherwise difficult-to-understand models.