B. du Boulay

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness.

The effects of graphical and textual visualisations in multi-representational debugging environments

The effects of graphical and textual visualisations in a multi-representational debugging environment were investigated in computing students who used a software debugging environment (SDE) that allowed them to view the execution of programs in steps and that provided them with concurrently displayed, adjacent, multiple and linked representations. The experimental results are in agreement with research in the area that suggests that good debugging performance is associated with a balanced use of the available representations. Additionally, these results raise the issue of whether graphical visualisations promote a more judicious representation use than textual ones for program debugging in multi-representational environments.

Statistical Modelling and Structured Image Description for Intelligent Tutoring in MR Imaging of the Head

A collaborative research project between the Medical Systems Research Group, De Montfort University Leicester, and the School of Cognitive and Computing Sciences, University of Sussex, is examining the feasibility of utilising statistical-based principles and a structured image description language for tutoring about radiological image interpretation and diagnosis. This paper describes the construction of a sophisticated intelligent tutoring system (ITS) for the interpretation of magnetic resonance images in the diagnosis of cerebral disease. The outcome of the project will be a tutoring system and diagnostic aid for MR images which can be evaluated in a realistic educational setting. An evaluation of the system will show whether computer-assisted tutoring can be effective in supplementing conventional teaching, by teaching a systematic approach to image interpretation and by providing exposure to a large archive of annotated images.

Structural Knowledge and Language Notational Properties in Program Comprehension

Several accounts of program comprehension have taken the theory of text comprehension by Kinstch as a starting point to model the mental representations built when programmers understand a computer program. A crucial point that these accounts try to explain is how these mental representations are organised. According to Kintschs theory, the mental representations built as a product of the text comprehension process are interrelated propositional networks whose organisation is determined by the main idea of the text. In program comprehension, this main idea has been understood in terms of functionality. This paper contends this notion, proposing that in program understanding programmers mental representations are multifaceted and organised through several criteria. Which of these is the most important one depends on the programming language employed among other factors. The fact that functional information appeared as crucial might have been because most of the empirical research that has been undertaken has employed procedural languages. This claim is tested empirically by analysing the mental representations of programmers in Prolog, a declarative programming language. The results support our claim by showing that in this case data structure information is more important than function