Real World Learning and Authentic Assessment

As students increasingly adopt a consumerist lifestyle academics are under pressure to assess and mark more students’ assignments in quicker turn around periods. In no other area is the marketisation shift between student and academic more apparent in the accountability that academics now need to demonstrate to students in their grading and feedback (Boud & Molloy, 2013). When evaluating their higher education experience students are most likely to complain about their grading or feedback (Boud & Molloy, 2013) and National Student Survey results consistently indicate that this category, more than any other, has the highest student dissatisfaction rates (Race, 2014)

Real World Learning: Simulation and Gaming

Simulations and games are being used across a variety of subject areas as a means to provide insight into real world situations within a classroom setting; they offer many of the benefits of real world learning but without some of the associated risks and costs. Lean, Moizer, Derham, Strachan and Bhuiyan aim to evaluate the role of simulations and games in real world learning. The nature of simulations and games is discussed with reference to a variety of examples in Higher Education. Their role in real world learning is evaluated with reference to the benefits and challenges of their use for teaching and learning in Higher Education. Three case studies from diverse subject contexts are reported to illustrate the use of simulations and games and some of the associated issues

"Emotions in Intercultural Relations"

This chapter argues that Akira Iriye's early work revealed that emotions strongly influenced international and intercultural relations

RAMTaB : robust alignment of multi-tag bioimages

For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks