712 research outputs found

    Evaluating Digital Libraries: A Longitudinal and Multifaceted View

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    published or submitted for publicatio

    Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects

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    These are the Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

    Analysing, visualising and supporting collaborative learning using interactive tabletops

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    The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

    A Framework for Students Profile Detection

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    Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

    Quality assessment technique for ubiquitous software and middleware

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    The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

    Multimodal interaction for deliberate practice

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    Computer based laboratory simulation in maritime education

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    Measuring and understanding self-handicapping in education

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    Self-handicapping is intentionally fabricating obstacles to performance. It is very prevalent in education where it interferes with learning and lowers academic achievement. Few self-handicapping experiments have approximated authentic learning situations, elevating concerns about ecological validity and generalizability. This study addressed several methodological concerns by (a) posing a task common in education, and (b) offering participants multiple occasions to choose among several productive, neutral, or self-handicapping approaches to learning. Undergraduate learners were randomly assigned to receive contingent or non-contingent success feedback on three learning tasks. Each task offered multiple occasions to claim or practise self-handicapping by making selections within a component of the software. Those selections caused changes in the learning environment while participants worked on tasks and generated data about self-handicapping more realistically situated and in finer grain than data gathered in prior research. Results indicate this method for unobtrusively recording data about self-handicapping validly represented the construct. Learners’ choices reflected preferences for certain handicaps and described patterns of hidden versus blatant self-handicapping. Evidence for self-handicapping and self-regulated learning across tasks was found. Some learners repeatedly self-handicapped, Others self-regulated learning over time by demonstrating metacognitive awareness, monitoring, and control of learning activities regardless of feedback provided. Encouraging metacognition may aid self-handicappers to more productively self-regulate their learning over time
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