The nature of physics

A small refrigeration plant, for teaching refrigeration theory, used a control console built to traditional design guidelines: one output in the display for each sensor in the plant. This style of console is notorious for inducing high cognitive loads on operators and for displaying redundant data. Often, the high cognitive load is the result of inconsistency between the intent for displaying the data and the format of the displayed data. An interface based on ecological interface design (EID) theory was designed and implemented. The completed interface provided the operator with information that was commensurate with his mental model derived from the system image. During the testing of the new interface, the expert operator's mental model of the refrigeration system was modified due to improved observation of the refrigeration plant's operational parameters. The application achieved the desired result and reduced the operator's workload by removing a cognitive task - that of determining the system stability-from the operator's task list.

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Applying ecological interface design to experimental apparatus used to monitor a refrigeration plant

Increasing the usability of software by integrating usability engineering into the development cycle has become common practice. Although usability engineering is effective, it can be expensive, and organizations want to receive the best possible returns on their investments. Oftentimes, however, organizations spend large sums of money collecting usability problem data through activities such as usability testing, but do not receive acceptable returns on those investments during redesign. The primary reason is that there is an almost complete lack of methods and tools for usability problem data analysis to transform raw usability data into effective inputs for developers. In this thesis, we develop an infrastructure for usability problem data analysis to address the need for better returns on usability engineering investments. The infrastructure consists of four main components: a framework, a process, tools, and semantic analysis technology. Embedded within the infrastructure is the User Action Framework, a conceptual framework of usability concepts, which is used to organize usability data. The process addresses extraction of usability problems from raw usability data, diagnosis of problems according to usability concepts, and reporting of problems in a form that is usable by developers. The tools leverage the framework and guide practitioners through the process, while the semantic analysis technology supplements the capabilities of the tools to automate parts of the process.

An Infrastructure to Support Usability Problem Data Analysis

It is well accepted that learnability is a crucial attribute of usability that should be considered in almost every software system. A good learnability leads within a short time and with minimal effort to a high level of proficiency of the user. Therefore, expensive training time of complex systems is reduced. However, there is only few consensus on how to define and evaluate learnability. In addition, gathering detailed information on learnability is quite difficult. In todays books on usability evaluation, learnability gets only few attention, research publications are spread to several other fields and the term learnability is also used in other context.

The objective of this thesis is to give an structured overview of learnability and methods for evaluation and additionally assist in the evaluator’s individual choice of an appropriate method. First of all, several definitions of learnability are discussed. For a deeper understanding psychological background knowledge is provided. Afterwards, methods to asses learnability are presented. This comprises nine methods that seem particularly appropriate to measure learnability. As this methods are very diverse, a framework based on analytical hierarchy process is provided. This framework aims to classify presented methods with respect to certain criteria and assess practitioners in selecting an appropriate method to measure learnability.

Measuring Learnability in Human-Computer Interaction

The structure of a physical behaviour description facility

Models of physical systems range from those of initial individual cognition to mathematical representations on a computer which are accepted as the developed final models. It is conjectured that a formalization of the qualitative cognitive models will help us to understand how they are formed and will eventually help us to produce better computer models. The structure of these models would provide qualitative descriptions and explanations of behaviour which could be assimilated by non-specialists. It is argued that cognitive models should be produced with an awareness of the possible form of the final computer model. To illustrate this, a case study of the development of the cognitive and computer models of a naturally parallel physical process is presented. This early work is part of the broader goal of producing an appropriate computing environment through which various models and techniques are combined for producing explanations. A procedure for developing models from the primitive stage to computer implementation is suggested. Theories in cognitive science and research on mental models are briefly discussed.

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S. Chandra

Papers

Cognitive and computer models of physical systems