The effect of a suggestive interview on children’s memory of a repeated event: Does it matter whether suggestions are linked to a particular incident?

This study examined the impact of linking misleading information to a particular occurrence of a repeated event. Children aged 5- to 6-years took part in the same staged event four times and 16 target details varied in each occurrence (e.g., the colour of a cloak varied each time). Three days or three weeks later they were asked questions, some of which included false information, about the final occurrence. The next day, the children were required to recall what happened in the final occurrence. Compared to children whose biasing interview was not focused on any particular occurrence of the repeated event, linking the biasing interview to the final occurrence increased the number of suggested details that were reported. Interestingly, the children whose biasing interview was not focused on any occurrence were also less likely to report the false suggestions than another group of children who had only experienced the event once and whose biasing interview was linked to that single occurrence. These findings have implications for how lawyers and investigative interviewers question children about multiple incidents

The Blended Learning Unit, University of Hertfordshire: A Centre for Excellence in Teaching and Learning, Evaluation Report for HEFCE

The University of Hertfordshire’s Blended Learning Unit (BLU) was one of the 74 Centres for Excellence in Teaching and Learning (CETLs) funded by the Higher Education Funding Council for England (HEFCE) between 2005 and 2010. This evaluation report follows HEFCE’s template. The first section provides statistical information about the BLU’s activity. The second section is an evaluative reflection responding to 13 questions. As well as articulating some of our achievements and the challenges we have faced, it also sets out how the BLU’s activity will continue and make a significant contribution to delivery of the University of Hertfordshire’s 2010-2015 strategic plan and its aspirations for a more sustainable future. At the University of Hertfordshire, we view Blended Learning as the use of Information and Communication Technology (ICT) to enhance the learning and learning experience of campus-based students. The University has an excellent learning technology infrastructure that includes its VLE, StudyNet. StudyNet gives students access to a range of tools, resources and support 24/7 from anywhere in the world and its robustness, flexibility and ease of use have been fundamental to the success of the Blended Learning agenda at Hertfordshire. The BLU has comprised a management team, expert teachers seconded from around the University, professional support and a Student Consultant. The secondment staffing model was essential to the success of the BLU. As well as enabling the BLU to become fully staffed within the first five months of the CETL initiative, it has facilitated access to an invaluable spectrum of Blended Learning, research and Change Management expertise to inform pedagogically sound developments and enable change to be embedded across the institution. The BLU used much of its capital funding to reduce barriers to the use of technology by, for example, providing laptop computers for all academic staff in the institution, enhancing classroom technology provision and wirelessly enabling all teaching accommodation. Its recurrent funding has supported development opportunities for its own staff and staff around the institution; supported evaluation activities relating to individual projects and of the BLU’s own impact; and supported a wide range of communication and dissemination activities internally and externally. The BLU has led the embedding a cultural change in relation to Blended Learning at the University of Hertfordshire and its impact will be sustained. The BLU has produced a rich legacy of resources for our own staff and for others in the sector. The University’s increased capacity in Blended Learning benefits all our students and provides a learning experience that is expected by the new generation of learners in the 21st century. The BLU’s staffing model and partnership ways of working have directly informed the structure and modus operandi of the University’s Learning and Teaching Institute (LTI). Indeed a BLU team will continue to operate within the LTI and help drive and support the implementation of the University’s 2010-2015 Strategic plan. The plan includes ambitions in relation to Distance Learning and Flexible learning and BLU will be working to enable greater engagement with students with less or no need to travel to the university. As well as opening new markets within the UK and overseas, even greater flexibility for students will also enable the University to reduce its carbon footprint and provide a multifaceted contribution to our sustainability agenda. We conclude this executive summary with a short paragraph, written by Eeva Leinonen, our former Deputy Vice-Chancellor, which reflects our aspiration to transform Learning and Teaching at the University of Hertfordshire and more widely in the sector. ‘As Deputy Vice Chancellor at Hertfordshire I had the privilege to experience closely the excellent work of the Blended Learning Unit, and was very proud of the enormous impact the CETL had not only across the University but also nationally and internationally. However, perhaps true impact is hard to judge at such close range, but now as Vice Principal (Education) at King's College London, I can unequivocally say that Hertfordshire is indeed considered as the leading Blended Learning university in the sector. My new colleagues at King's and other Russell Group Universities frequently seek my views on the 'Hertfordshire Blended Learning' experience and are keen to emulate the successes achieved at an institutional wide scale. The Hertfordshire CETL undoubtedly achieved not only what it set out to achieve, but much more in terms of scale and impact. All those involved in this success can be justifiably proud of their achievements.’ Professor Eeva Leinonen, Vice Principal (Education), King's College, Londo

Patterns of current account adjustment: insights from past experience

The paper examines over seventy episodes of current account adjustment in industrial and major emerging market economies. It argues that these episodes were characterised by strongly divergent economic developments. To reduce this divergence, the paper classifies episodes with similar characteristics in three groups, using cluster analysis. A majority of cases was characterised by internal adjustment through a slowdown of domestic demand and did not involve significant exchange rate movements. In some cases, the adjustment was mainly external, facilitated by a relatively modest exchange rate depreciation and without economic slowdown. Finally, some cases involved a crisis-like combination of a severe slowdown and a significant currency depreciation. Using a multinomial logit, we find that this classification of episodes helps improve the predictability of current account adjustment. JEL Classification: F32, C14, C25cluster analysis, current account adjustment, external imbalances, multinomial logit

MCPLOTS: a particle physics resource based on volunteer computing

The mcplots.cern.ch web site (MCPLOTS) provides a simple online repository of plots made with high-energy-physics event generators, comparing them to a wide variety of experimental data. The repository is based on the HEPDATA online database of experimental results and on the RIVET Monte Carlo analysis tool. The repository is continually updated and relies on computing power donated by volunteers, via the LHC@HOME platform.Comment: 30 page

Implementing Loss Distribution Approach for Operational Risk

To quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the Loss Distribution Approach. There are many modeling issues that should be resolved to use the approach in practice. In this paper we review the quantitative methods suggested in literature for implementation of the approach. In particular, the use of the Bayesian inference method that allows to take expert judgement and parameter uncertainty into account, modeling dependence and inclusion of insurance are discussed

Patterns of Current Account Adjustment – Insights from Past Experience

The paper examines episodes of current account adjustment in individual economies. A central finding is that these episodes are very divergent and can be usefully classified, on the basis of cluster analysis, in three groups. A majority of cases is characterised by internal adjustment, exhibiting slowing domestic demand growth. In some cases, the adjustment was mainly external, facilitated by an exchange rate depreciation but without economic slowdown. Finally, some cases involved a crisis-like combination of a severe slowdown and a significant currency depreciation. Using a multinomial logit, we find that this classification of episodes helps improve the predictability of current account adjustment.external imbalances, current account adjustment, cluster analysis, multinomial logit