Guidelines For Pursuing and Revealing Data Abstractions

Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration

Translation and human-computer interaction

This paper seeks to characterise translation as a form of human-computer interaction. The evolution of translator-computer interaction is explored and the challenges and benefits are enunciated. The concept of cognitive ergonomics is drawn on to argue for a more caring and inclusive approach towards the translator by developers of translation technology. A case is also made for wider acceptance by the translation community of the benefits of the technology at their disposal and for more humanistic research on the impact of technology on the translator, the translation profession and the translation process

Glitchspace:teaching programming through puzzles in cyberspace

There is an increasing need to address the player experience in games-based learning. Whilst games offer enormous potential as learning experiences, the balance between entertainment and education must be carefully designed and delivered. Successful commercial games tend to focus gameplay above any educational aspects. In contrast, games designed for educational purposes have a habit of sacrificing entertainment for educational value which can result in a decline in player engagement. For both, the player experience is critical as it can have a profound effect on both the commercial success of the game and in delivering the educational engagement. As part of an Interface-funded research project Abertay University worked with the independent games company, Space Budgie, to enhance the user experience of their educational game Glitchspace. The game aimed to teach basic coding principles and terminology in an entertaining way. The game sets the player inside a Mondrian-inspired cyberspace world where to progress the player needs to reprogramme the world around them to solve puzzles. The main objective of the academic-industry collaborative project was to analyse the user experience (UX) of the game to increase its educational value for a standalone educational version. The UX design focused on both pragmatic and hedonic qualities such playability, usability and the psychological impact of the game. The empirical study of the UX design allowed all parties to develop a deeper understanding of how the game was being played and the initial reactions to the game by the player. The core research question that the study sought to answer was whether when designing an educational game, UX design could improve philosophical concepts like motivation and engagement to foster better learning experiences.</p

Using software visualization technology to help genetic algorithm designers

This work is part of a three year PhD project to examine how Software Visualization(SV) can be applied to support the design and construction of Genetic Algorithms (GAs). A user survey carried out at the start of this project identified a set of key system features required by GA users. A visualization system embodying these features was then designed and a prototype built. This paper describes what genetic algorithms are and how they can be applied. It then reviews some of the survey results and their impact on the design of the visualization system. The paper concludes with an exploration of how the resulting prototype may be evaluated

HILT : a terminology mapping service with a DDC spine

The role of DDC in the ongoing HILT (High-level Thesaurus) project is discussed. A phased initiative, funded by JISC in the UK, HILT addresses an issue of likely interest to anyone serving users wishing to cross-search or cross-browse groups of networked information services, whether at regional, national or international level - the problem of subject-based retrieval from multiple sources using different subject schemes for resource description. Although all three phases of HILT to date are covered, the primary concern is with the subject interoperability solution piloted in phase II, and with the use of DDC as a spine in that approach

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on