Immersive Learning Research Network

Computer games have now been around for over three decades and the term serious games has been attributed to the use of computer games that are thought to have educational value. Game-based learning (GBL) has been applied in a number of different fields such as medicine, languages and software engineering. Furthermore, serious games can be a very effective as an instructional tool and can assist learning by providing an alternative way of presenting instructions and content on a supplementary level, and can promote student motivation and interest in subject matter resulting in enhanced learning effectiveness. REVLAW (Real and Virtual Reality Law) is a research project that the departments of Law and Computer Science of Westminster University have proposed as a new framework in which law students can explore a real case scenario using Virtual Reality (VR) technology to discover important pieces of evidence from a real-given scenario and make up their mind over the crime case if this is a murder or not. REVLAW integrates the immersion into VR as the perception of being physically present in a non-physical world. The paper presents the prototype framework and the mechanics used to make students focus on the crime case and make the best use of this immersive learning approach.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

Teaching by leading and managing learning environments

The aim of this chapter is to promote an understanding of how different environments or settings within which students are asked or required to learn - such as large groups, small groups and laboratory and practice settings – have an impact on how they approach their learning and hence on the design and delivery of teaching. It provides an overview of underpinning principles and concepts before exploring their application in practice. The focus is on face-to-face teaching and learning.