Competency Based Assessment Using Virtual Reality (VERT): Is It a Realistic Possibility ?

The education of the radiography profession is based within higher education establishments, yet a critical part of all radiography programmes is the clinical component where students learn the practical skills of the profession. Assessments therefore not only have to assess a student’s knowledge, but also their clinical competence and core skills in line with both Health and Care Professions Council and the Society and College of Radiographers requirements. This timely thesis examines the possibility of using the Virtual Environment for RadioTherapy (VERT) as an assessment tool to evaluate a student’s competence so giving the advantage of a standard assessment and relieving time pressures in the clinical department. A mixed methods approach was taken which can be described as a Quantitative Qualitative design with the emphasis being on the Quantitative element; a so called QUAN  qual design. The quantitative evaluation compared two simulations, one in the virtual reality environment and another in the department using a real treatment machine. Students were asked to perform two electron setups in each simulation; the order being randomly decided and so the study would be described as a randomised cross-over design. Following this, qualitative data was collected in student focus groups to explore student perspectives in more depth. Findings indicated that the performance between the two simulators was significantly different, p < 0∙001; the virtual simulation scoring significantly lower than the hospital based simulation overall and in virtually all parameters being assessed. Thematic analysis of the qualitative data supported this finding and identified 4 main themes; equipment use, a lack of reality, learning opportunities and assessment of competence. One other sub-theme identified for reality was that of the environment and senses.

A Scalable Design Framework for Variability Management in Large-Scale Software Product Lines

Variability management is one of the major challenges in software product line adoption, since it needs to be efficiently managed at various levels of the software product line development process (e.g., requirement analysis, design, implementation, etc.). One of the main challenges within variability management is the handling and effective visualization of large-scale (industry-size) models, which in many projects, can reach the order of thousands, along with the dependency relationships that exist among them. These have raised many concerns regarding the scalability of current variability management tools and techniques and their lack of industrial adoption. To address the scalability issues, this work employed a combination of quantitative and qualitative research methods to identify the reasons behind the limited scalability of existing variability management tools and techniques. In addition to producing a comprehensive catalogue of existing tools, the outcome form this stage helped understand the major limitations of existing tools. Based on the findings, a novel approach was created for managing variability that employed two main principles for supporting scalability. First, the separation-of-concerns principle was employed by creating multiple views of variability models to alleviate information overload. Second, hyperbolic trees were used to visualise models (compared to Euclidian space trees traditionally used). The result was an approach that can represent models encompassing hundreds of variability points and complex relationships. These concepts were demonstrated by implementing them in an existing variability management tool and using it to model a real-life product line with over a thousand variability points. Finally, in order to assess the work, an evaluation framework was designed based on various established usability assessment best practices and standards. The framework was then used with several case studies to benchmark the performance of this work against other existing tools.