System upgrade: realising the vision for UK education

A report summarising the findings of the TEL programme in the wider context of technology-enhanced learning and offering recommendations for future strategy in the area was launched on 13th June at the House of Lords to a group of policymakers, technologists and practitioners chaired by Lord Knight. The report – a major outcome of the programme – is written by TEL director Professor Richard Noss and a team of experts in various fields of technology-enhanced learning. The report features the programme’s 12 recommendations for using technology-enhanced learning to upgrade UK education

Collecting and using student feedback Date: A guide to good practice

The purpose of this Guide is to help higher education institutions make the best use of their student feedback. This guide is based on a HEFCE funded project undertaken by the Centre for Higher Education Research and Information (CHERI). The purpose of this Guide is to help higher education institutions make the best use of their student feedback. All institutions collect feedback from their students and in many different forms. They use it to improve the quality of the education they provide. In recent years, there has been a shift in the balance between informal and formal types of student feedback with a greater emphasis on the latter. Now, new devolved forms of national quality assurance promise to give an important role to students and there is also an expectation that information from student feedback will be used to inform the choices of students when applying to higher education. Thus, as the importance attached to student feedback increases, ensuring that feedback is collected effectively and used wisely becomes an increasing priority for higher education institutions. This Guide draws on the experiences of the sector to highlight some of the good practices that exist as well as some of the problems that institutions are experiencing in using student feedback. Its focus is upon the use of student feedback for the purpose of enhancing the quality of teaching and learning. Other purposes are acknowledged but are not the main emphasis of this publication

Collaborative hybrid agent provision of learner needs using ontology based semantic technology

© Springer International Publishing AG 2017. This paper describes the use of Intelligent Agents and Ontologies to implement knowledge navigation and learner choice when interacting with complex information locations. The paper is in two parts: the first looks at how Agent Based Semantic Technology can be used to give users a more personalised experience as an individual. The paper then looks to generalise this technology to allow users to work with agents in hybrid group scenarios. In the context of University Learners, the paper outlines how we employ an Ontology of Student Characteristics to personalise information retrieval specifically suited to an individual’s needs. Choice is not a simple “show me your hand and make me a match” but a deliberative artificial intelligence (AI) that uses an ontologically informed agent society to consider the weighted solution paths before choosing the appropriate best. The aim is to enrich the student experience and significantly re-route the student’s journey. The paper uses knowledge-level interoperation of agents to personalise the learning space of students and deliver to them the information and knowledge to suite them best. The aim is to personalise their learning in the presentation/format that is most appropriate for their needs. The paper then generalises this Semantic Technology Framework using shared vocabulary libraries that enable individuals to work in groups with other agents, which might be other people or actually be AIs. The task they undertake is a formal assessment but the interaction mode is one of informal collaboration. Pedagogically this addresses issues of ensuring fairness between students since we can ensure each has the same experience (as provided by the same set of Agents) as each other and an individual mark may be gained. This is achieved by forming a hybrid group of learner and AI Software Agents. Different agent architectures are discussed and a worked example presented. The work here thus aims at fulfilling the student’s needs both in the context of matching their needs but also in allowing them to work in an Agent Based Synthetic Group. This in turn opens us new areas of potential collaborative technology