The Use of a Computer-Based Simulation Game (CSG) for Learning and Teaching

Introduction A computer-based simulation game (CSG) was used for the first time in a final-year undergraduate module. A change management simulation game was used in the seminar classes as a formative exercise that was linked to parts of the students’ summative assessment. The module evaluation suggests that most students learned from using the CSG.

An Investigation into the Use of Computer-based Simulation Games for Learning and Teaching in Business Management Courses

Computer-based simulation games (CSG) are a form of innovation in learning and teaching. CGS are used more pervasively in various ways such as a class activity (formative exercises) and as part of summative assessments (Leemkuil and De Jong, 2012; Zantow et al., 2005). This study investigates the current and potential use of CGS in Worcester Business School’s (WBS) Business Management undergraduate programmes. The initial survey of off-the-shelf simulation reveals that there are various categories of simulations, with each offering varying levels of complexity and learning opportunities depending on the field of study. The findings suggest that whilst there is marginal adoption of the use CSG in learning and teaching, there is significant opportunity to increase the use of CSG in enhancing learning and learner achievement, especially in Level 5 modules. The use of CSG is situational and its adoption should be undertaken on a case-by-case basis. WBS can play a major role by creating an environment that encourages and supports the use of CSG as well as other forms of innovative learning and teaching methods. Thus the key recommendation involves providing module teams further support in embedding and integrating CSG into their modules.

Strategic Management Simulation as a Blended Learning Dimension: Campus Based Students’ Perspectives

Although business simulations are widely used in management education, there is no consensus about how to optimise their application. Our research explores the use of business simulations as a dimension of a blended learning pedagogic approach for undergraduate business education. Accepting that few best-practice prescriptive models for the design and implementation of simulations in this context have been presented, and that there is little empirical evidence for the claims made by proponents of such models, we address the lacuna by considering business student perspectives on the use of simulations. We then intersect available data with espoused positive outcomes made by the authors of a prescriptive model. We find the model to be essentially robust and offer evidence to support this position. In so doing we provide one of the few empirically based studies to support claims made by proponents of simulations in business education. The research should prove valuable for those with an academic interest in the use of simulations, either as a blended learning dimension or as a stand-alone business education activity. Further, the findings contribute to the academic debate surrounding the use and efficacy of simulation-based training [SBT] within business and management education.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.