Interactive maps: What we know and what we need to know

This article provides a review of the current state of science regarding cartographic interaction a complement to the traditional focus within cartography on cartographic representation. Cartographic interaction is defined as the dialog between a human and map mediated through a computing device and is essential to the research into interactive cartography geovisualization and geovisual analytics. The review is structured around six fundamental questions facing a science of cartographic interaction: (1) what is cartographic interaction (e.g. digital versus analog interactions interaction versus interfaces stages of interaction interactive maps versus mapping systems versus map mash-ups); (2) why provide cartographic interaction (e.g. visual thinking geographic insight the stages of science the cartographic problematic); (3) when should cartographic interaction be provided (e.g. static versus interactive maps interface complexity the productivity paradox flexibility versus constraint work versus enabling interactions); (4) who should be provided with cartographic interaction (e.g. user-centered design user ability expertise and motivation adaptive cartography and geocollaboration); (5) where should cartographic interaction be provided (e.g. input capabilities bandwidth and processing power display capabilities mobile mapping and location-based services); and (6) how should cartographic interaction be provided (e.g. interaction primitives objective-based versus operator-based versus operand-based taxonomies interface styles interface design)? The article concludes with a summary of research questions facing cartographic interaction and offers an outlook for cartography as a field of study moving forward

Doctor of Philosophy

dissertationClinical decision support systems (CDSS) and electronic health records (EHR) have been widely adopted but do not support a high level of reasoning for the clinician. As a result, workflow incongruity and provider frustrations lead to more errors in reasoning. Other successful fields such as defense, aviation, and the military have used task complexity as a key factor in decision support system development. Task complexity arises during the interaction of the user and the tasks. Therefore, in this dissertation I have utilized different human factor methods to explore task complexity factors to understand their utility in health information technology system design. The first study addresses the question of generalizing complexity through a clinical complexity model. In this study, we integrated and validated a patient and task complexity model into a clinical complexity model tailored towards healthcare to serve as the initial framework for data analysis in our subsequent studies. The second study addresses the question of the coping strategies of infectious disease (ID) clinicians while dealing with complex decision tasks. The study concluded that clinicians use multiple cognitive strategies that help them to switch between automatic cognitive processes and analytical processes. The third study identified the complexity contributing factors from the transcripts of the observations conducted in the ID domain. The clinical complexity model developed in the first study guided the research for identifying the prominent complexity iv factors to recommend innovative healthcare technology system design. The fourth study, a pilot exploratory study, demonstrated the feasibility of developing a population information display from querying real complex patient information from an actual clinical database as well as identifying the ideal features of population information display. In summary, this dissertation adds to the knowledge about how clinicians adapt their information environment to deal with complexity. First, it contributes by developing a clinical complexity model that integrates both patient and task complexity. Second, it provides specific design recommendations for future innovative health information technology systems. Last, this dissertation also suggests that understanding task complexity in the healthcare team domain may help to better design of interface system

Recommending video content for use in group-based reminiscence therapy

REMPAD is a semi-automated cloud-based system used to facilitate digital reminiscence therapy for patients with mild-to-moderate dementia, enacted in a group setting. REMPAD uses profiles for participants and groups to proactively recommend interactive video content from the Internet to match these profiles. In this chapter, we focus on the design of the system and then the system architecture, the system build, data curation, and usage scenarios. We also report a series of steps carried out as part of our user-centered design approach to system development, and a series of analyses on interaction logs which indicate various levels of effectiveness for different configurations of the recommendation algorithm we use. The results indicate high user satisfaction when using the system, and strong tendency towards repeated use in future

Diverse perceptions of smart spaces

This is the era of smart technology and of ‘smart’ as a meme, so we have run three workshops to examine the ‘smart’ meme and the exploitation of smart environments. The literature relating to smart spaces focuses primarily on technologies and their capabilities. Our three workshops demonstrated that we require a stronger user focus if we are advantageously to exploit spaces ascribed as smart: we examined the concept of smartness from a variety of perspectives, in collaboration with a broad range of contributors. We have prepared this monograph mainly to report on the third workshop, held at Bournemouth University in April 2012, but do also consider the lessons learned from all three. We conclude with a roadmap for a fourth (and final) workshop, which is intended to emphasise the overarching importance of the humans using the spac

Interactive Data Analysis with Next-step Natural Language Query Recommendation

Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries. Nevertheless, interactive data analysis is a demanding process, especially for novice data analysts. When exploring large and complex SQL databases from different domains, data analysts do not necessarily have sufficient knowledge about different data tables and application domains. It makes them unable to systematically elicit a series of topically-related and meaningful queries for insight discovery in target domains. We develop a NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions. The system adopts a data-driven approach to suggest semantically relevant and context-aware queries for application domains of users' interest based on their query logs. Also, the system helps users organize query histories and results into a dashboard to communicate the discovered data insights. With a comparative user study, we show that our system can facilitate a more effective and systematic data analysis process than a baseline without the recommendation module.Comment: 14 pages, 6 figure

Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This sixth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). Themes: • Big-data inquiry: thinking with data • Learners making science • Navigating post-truth societies • Immersive learning • Learning with internal values • Student-led analytics • Intergroup empathy • Humanistic knowledge-building communities • Open Textbooks • Spaced Learnin

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers