127,454 research outputs found

    Methodological development

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    Book description: Human-Computer Interaction draws on the fields of computer science, psychology, cognitive science, and organisational and social sciences in order to understand how people use and experience interactive technology. Until now, researchers have been forced to return to the individual subjects to learn about research methods and how to adapt them to the particular challenges of HCI. This is the first book to provide a single resource through which a range of commonly used research methods in HCI are introduced. Chapters are authored by internationally leading HCI researchers who use examples from their own work to illustrate how the methods apply in an HCI context. Each chapter also contains key references to help researchers find out more about each method as it has been used in HCI. Topics covered include experimental design, use of eyetracking, qualitative research methods, cognitive modelling, how to develop new methodologies and writing up your research

    Driving tasks and new information technologies

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    Knowledge Cartography for Controversies: The Iraq Debate

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    In analysing controversies and debates—which would include reviewing a literature in order to plan research, or assessing intelligence to formulate policy—there is no one worldview which can be mapped, for instance as a single, coherent concept map. The cartographic challenge is to show which facts are agreed and contested, and the different kinds of narrative links that use facts as evidence to define the nature of the problem, what to do about it, and why. We will use the debate around the invasion of Iraq to demonstrate the methodology of using a knowledge mapping tool to extract key ideas from source materials, in order to classify and connect them within and across a set of perspectives of interest to the analyst. We reflect on the value that this approach adds, and how it relates to other argument mapping approaches

    Methodological Flaws in Cognitive Animat Research

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    In the field of convergence between research in autonomous machine construction and biological systems understanding it is usually argued that building robots for research on auton- omy by replicating extant animals is a valuable strategy for engineering autonomous intelligent systems. In this paper we will address the very issue of animat construction, the ratio- nale behind this, their current implementations and the value they are producing. It will be shown that current activity, as it is done today, is deeply flawed and useless as research in the science and engineering of autonomy

    Ontology-driven conceptual modeling: A'systematic literature mapping and review

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    All rights reserved. Ontology-driven conceptual modeling (ODCM) is still a relatively new research domain in the field of information systems and there is still much discussion on how the research in ODCM should be performed and what the focus of this research should be. Therefore, this article aims to critically survey the existing literature in order to assess the kind of research that has been performed over the years, analyze the nature of the research contributions and establish its current state of the art by positioning, evaluating and interpreting relevant research to date that is related to ODCM. To understand and identify any gaps and research opportunities, our literature study is composed of both a systematic mapping study and a systematic review study. The mapping study aims at structuring and classifying the area that is being investigated in order to give a general overview of the research that has been performed in the field. A review study on the other hand is a more thorough and rigorous inquiry and provides recommendations based on the strength of the found evidence. Our results indicate that there are several research gaps that should be addressed and we further composed several research opportunities that are possible areas for future research

    Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for healthcare policy and decision making

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    Objective: Network meta-analyses have extensively been used to compare the effectiveness of multiple interventions for healthcare policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Study design and setting: Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov Chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. Results: We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whilst MMSE at threshold <25/30 appeared to have the best true negative rate. Conclusion: The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making

    Scoping analytical usability evaluation methods: A case study

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    Analytical usability evaluation methods (UEMs) can complement empirical evaluation of systems: for example, they can often be used earlier in design and can provide accounts of why users might experience difficulties, as well as what those difficulties are. However, their properties and value are only partially understood. One way to improve our understanding is by detailed comparisons using a single interface or system as a target for evaluation, but we need to look deeper than simple problem counts: we need to consider what kinds of accounts each UEM offers, and why. Here, we report on a detailed comparison of eight analytical UEMs. These eight methods were applied to it robotic arm interface, and the findings were systematically compared against video data of the arm ill use. The usability issues that were identified could be grouped into five categories: system design, user misconceptions, conceptual fit between user and system, physical issues, and contextual ones. Other possible categories such as User experience did not emerge in this particular study. With the exception of Heuristic Evaluation, which supported a range of insights, each analytical method was found to focus attention on just one or two categories of issues. Two of the three "home-grown" methods (Evaluating Multimodal Usability and Concept-based Analysis of Surface and Structural Misfits) were found to occupy particular niches in the space, whereas the third (Programmable User Modeling) did not. This approach has identified commonalities and contrasts between methods and provided accounts of why a particular method yielded the insights it did. Rather than considering measures such as problem count or thoroughness, this approach has yielded insights into the scope of each method
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