Abstract. Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors 'semantically' (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. 'ordinary' users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves.

BACKGROUND Movement analysis in the clinical setting is frequently restricted to observational methods to inform clinical decision making, which has limited accuracy. Fixed-site optical expensive movement analysis laboratories provide ‘gold-standard’ kinematic measurements, however they are rarely accessed for routine clinical use. Wearable inertial measurement units (IMUs) have been demonstrated as comparable, inexpensive and portable movement analysis toolkit. MoJoXlab has therefore been developed to work with generic wearable IMUs. However, before using MoJoXlab in clinical practice there is a need to establish its validity in participants with and without knee conditions across a range of tasks with varying complexity. OBJECTIVE This paper presents the validation of MoJoXlab software for using generic wearable IMUs in calculating hip, knee and ankle joint angle measurements in the sagittal, frontal and transverse planes, for walking, squatting and jumping in healthy participants and those with anterior cruciate ligament reconstruction. METHODS Movement data were collected from 27 healthy participants and 20 participants with Anterior Cruciate Ligament (ACL) reconstruction. In each case, participants wore seven ‘MTw2’ IMUs to monitor their movement in walking, jumping and squatting tasks. Hip, knee and ankle joint angles were calculated in the sagittal, frontal and transverse plane using two different software packages: Xsens’s validated proprietary MVN Analyze, and MoJoXlab. Results were validated by comparing the generated waveforms, cross-correlation (CC) and normalized root mean square error (NRMSE) values. RESULTS Across all joints and activities, for both healthy and ACL reconstruction data, the cross-correlation and normalized root mean square error for the sagittal plane are: 0.99 ± 0.01 and 0.042 ± 0.025 respectively; for the frontal plane: 0.88 ± 0.048 and 0.18 ± 0.078; and for the transverse plane (hip and knee joints only): 0.85 ± 0.027 and 0.23 ± 0.065. On comparing results from the two different software systems, the sagittal plane is very highly correlated, with frontal and transverse planes showing strong correlation. CONCLUSIONS This paper demonstrates that non-proprietary software such as MoJoXlab can accurately calculate joint angles for movement analysis applications comparable to proprietary software, for walking, squatting and jumping, in healthy individuals and those following anterior cruciate ligament reconstruction. MoJoXlab can be used with generic wearable IMUs that can provide clinicians accurate objective data when assessing patients’ movement, even when changes are too small to be observed visually. The availability of easy-to-setup, non-proprietary software for calibration, data collection and joint angle calculation has the potential to increase the adoption of wearable IMU sensors in clinical practice, as well as in free living conditions, and may provide wider access to accurate, objective assessment of patients’ progress over time. CLINICALTRIAL

Abstract. Observational studies in the literature have highlighted low levels of user satisfaction in relation to the support for ontology visualization and exploration provided by current ontology engineering tools. These issues are particularly problematic for non-expert users, who rely on effective tool support to abstract from representational details and to be able to make sense of the contents and the structure of ontologies. To address these issues, we have developed a novel solution for visualizing and navigating ontologies, KC-Viz, which exploits an empirically-validated ontology summarization method, both to provide concise views of large ontologies, and also to support a 'middle-out' ontology navigation approach, starting from the most information-rich nodes (key concepts). In this paper we present the main features of KC-Viz and also discuss the encouraging results derived from a preliminary empirical evaluation, which suggest that the use of KC-Viz provides performance advantages to users tackling realistic browsing and visualization tasks. Supplementary data gathered through questionnaires also convey additional interesting findings, including evidence that prior experience in ontology engineering affects not just objective performance in ontology engineering tasks but also subjective views on the usability of ontology engineering tools.

The Personal Inquiry project is an investigation into the role that technologies can play in enabling effective inquiry. While it is generally agreed that inquiry-based learning has potential for student learning, especially in science, three main challenges remain. The first is to provide effective support for inquiry learning, for both students and teachers; the second is to be able to support inquiry learning across a range of contexts, including formal settings such as classrooms, and informal settings such as the home, and the final challenge is to support inquiries that engage the students. This paper addresses how inquiry-based activities for students and the teacher orchestration of such activities across time and contexts can be supported by technology using scripting. Personalization of the inquiries in terms of relevance and providing students with choice about the inquiries they carry out is an important part of the project's objective to engage students. A framework for the inquiry learning process is presented, and how this framework has influenced the design of the software nQuire is illustrated. Examples are drawn from trials with the software in several different settings with children working on science and geography investigations.

This paper describes the development of nQuire, a software application to guide personal inquiry learning. nQuire provides teacher support for authoring, orchestrating and monitoring inquiries as well as student support for carrying out, configuring and reviewing inquiries. nQuire allows inquiries to be scripted and configured in various ways, so that personally relevant, rather than off-the-shelf inquiries, can be created and used by teachers and students. nQuire incorporates an approach to specifying learning flow that provides flexible access to current inquiry activities without precluding access to other activities for review and orientation. Dependencies between activities are automatically handled, ensuring decisions made by the student or teacher are propagated through the inquiry. nQuire can be used to support inquiry activities across individual, group and class levels at different parts of the inquiry and offers a flexible, web-based approach that can incorporate different devices (smart phone, netbook, PC) and does not rely on constant connectivity.

The media curation craze has spawned a multitude of new sites that help users to collect and share web content. Some market themselves as spaces to explore a common interest through different types of related media. Others are promoted as a means for creating and sharing stories, or producing personalized newspapers. Still others target the education market, claiming that curation can be a powerful learning tool for web-based content. But who really benefits from the curation task: the content curator or the content consumer? This paper will argue that for curation to fully support learning, on either side, then the curation site has to allow the content curator to research and tell stories through their selected content and for the consumer to rewrite the story for themselves. This brings the curation task inline with museum practice, where museum professionals tell stories through careful selection, organization and presentation of objects in an exhibition, backed up by research. This paper introduces the notion of 'recuration' to describe a process in which shared content can be used as part of learning.

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The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.