Abstract-The paper introduces a conceptual model for the design of serious games and uses the Technology Acceptance Model (TAM) for its validation. A specially developed game introduced international students to public transport in Southampton. After completing the game, participants completed a short questionnaire and the data was analysed using structural equation modelling (SEM). The results identified the attributes and combinations of attributes that led the learner to accept and to use the serious game for learning. These findings are relevant in helping game designers and educational practitioners design serious games for effective learning.
Building innovative m-learning systems can be challenging, because innovative technology is tied to innovative practice, and thus the design process needs to consider the social and professional context in which a technology is to be deployed. In this chapter we describe a methodology for codesign in m-learning, which includes stakeholders from the domain in the technology design team. Through a case study of a project to support nurses on placement, we show that co-design should be accompanied by co-deployment in order to manage the reception and eventual acceptance of new technology in a particular environment. We present both our co-design and co-deployment methodologies, and describe the techniques that are applicable at each stage.
The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic − urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management.
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