Leading as designing: how educational developers in leadership and management studies can use architectural design to facilitate student learning about delivering innovation

This paper describes a qualitative observational study of how a work based learning masters leadership development programme for middle managers in health and social care in the UK introduced students to key aspects of delivering innovation, through a formative assignment on contemporary architectural design. Action learning and activity theoretical approaches were used to enable students to explore common principles of leading the delivery of innovation. Between 2001 and 2013 a total of 89 students in 7 cohorts completed the assignment. Evaluation lent support for the view that the assignment provided a powerful learning experience for many. Several students found the creativity, determination and dedication of architects, designers and structural engineers inspirational in their ability to translate a creative idea into a completed artefact, deploy resources and negotiate complex demands of stakeholders. Others expressed varying levels of self-empowerment as regards their capacity for fostering an equivalent creativity in self and others. Theoretical approaches in addition to activity theory, including Engeström’s concepts of stabilisation knowledge and possibility knowledge, are discussed to explain these differing outcomes and to clarify the challenges and opportunities for educational developers seeking to utilise cross-disciplinary, creative approaches in curriculum design.

Westminster Serious Games Platform (wmin-SGP) a tool for real-time authoring of roleplay simulations for learning

The use of serious games in education and their pedagogical benefit is being widely recognized. However, effective integration of serious games in education depends on addressing two big challenges: the successful incorporation of motivation and engagement that can lead to learning; and the highly specialised skills associated with customised development to meet the required pedagogical objectives. This paper presents the Westminster Serious Games Platform (wmin-SGP) an authoring tool that allows educators/domain experts without games design and development technical skills to create bespoke roleplay simulations in three dimensional scenes featuring fully embodied virtual humans capable of verbal and non-verbal interaction with users fit for specific educational objectives. The paper presents the wmin-SGP system architecture and it evaluates its effectiveness in fulfilling its purpose via the implementation of two roleplay simulations, one for Politics and one for Law. In addition, it presents the results of two types of evaluation that address how successfully the wmin-SGP combines usability principles and game core drives based on the Octalysis gamification framework that lead to motivating games experiences. The evaluation results shows that the wmin-SGP: provides an intuitive environment and tools that support users without advanced technical skills to create in real-time bespoke roleplay simulations in advanced graphical interfaces; satisfies most of the usability principles; and provides balanced simulations based on the Octalysis framework core drives. The paper concludes with a discussion of future extension of this real time authoring tool and directions for further development of the Octalysis framework to address learning.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.