Generic system architecture for context-aware, distributed recommendation

In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin

Measuring What Matters: Using Assessment and Accountability to Improve Student Learning

This report hails testing and accountability as key to improving student learning. CED cautions, however, that tests are a means, not an end, to school reform. More work must be done to ensure that tests are good measures of learning. CED's K-12 efforts will engage business leaders in sustaining support for performance measurement in education and in identifying and overcoming barriers to delivering public education in new ways

KAPTUR: exploring the nature of visual arts research data and its effective management.

KAPTUR (2011-2013), funded by JISC and led by the Visual Arts Data Service (VADS), is a highly collaborative project involving four institutional partners: the Glasgow School of Arts; Goldsmiths, University of London; University for the Creative Arts; and the University of the Arts London. The preservation and publication of research data is seen as positive and all UK Research Councils now require it as a condition of funding (RCUK 2012). As a result a network of data repositories are emerging (DataCite 2012a), some funded by Research Councils, others by institutions themselves. However, research data management practice within the visual arts appears ad hoc. None of the specialist arts institutions within the UK has implemented research data management policies (DCC 2011a), nor established research data management systems. KAPTUR seeks to investigate the nature of visual arts research data, making recommendations for its effective management; develop a model of best practice applicable to both specialist arts institutions and arts departments in multidisciplinary institutions; and apply, test and refine the model with the four institutional partners. This paper will explore the nature of visual arts research data and how effective data management can ensure its long term usage, curation and preservation