Reducing information overload by optimising information retrieval approaches

The information within an organisation forms a fundamental part of its success. In recent years the volume of information housed and processed by organisations has increased exponentially and grown to such a rate that it can be difficult to harness and make successful use of that information. This growth of information has led to the increasing prevalence of the concept of information overload. Although information overload is not a new concept, it is still considered a large-scale problem, with its effect upon the workplace and employees becoming increasingly detrimental. With the increase in available information comes the potential for increased overload. This research addresses some of the potential barriers that may exist preventing effective discovery, storage and sharing of information and thus increasing the information overload problem. [Continues.

Visualisation and dynamic querying of large multivariate data sets

The legitimacy and effectiveness of current methods and theories that guide the construction of visualisations is in question and there is a lack of any scientific support for many of these methods. A review of existing visualisation techniques demonstrates some of the innate strengths and weaknesses within the approaches used. By focusing on the more specific task of developing visualisations for large sets of multivariate data, the lack of any kind of guidance in this development process is acknowledged. A prototype visualisation tool based on the well-documented techniques of Parallel Coordinates and Dynamic Queries has been developed taking into account these findings. Incorporating new and novel ideas addressing identified weaknesses in current visualisations, this prototype also provides the basis for demonstrating, testing and evaluating these concepts

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Evaluation of an anthropomorphic user interface in a travel reservation context and affordances

This paper describes an experiment and its results concerning research that has been going on for a number ofyears in the area of anthropomorphic user interface feedback. The main aims of the research have been to examine theeffectiveness and user satisfaction of anthropomorphic feedback in various domains. The results are of use to all interactivesystems designers, particularly when dealing with issues of user interface feedback design. There is currently somedisagreement amongst computer scientists concerning the suitability of such types of feedback. This research is working toresolve this disagreement. The experiment detailed, concerns the specific software domain of Online Factual Delivery in thespecific context of online hotel bookings. Anthropomorphic feedback was compared against an equivalent non-anthropomorphicfeedback. Statistically significant results were obtained suggesting that the non-anthropomorphic feedback was more effective.The results for user satisfaction were however less clear. The results obtained are compared with previous research. Thissuggests that the observed results could be due to the issue of differing domains yielding different results. However the resultsmay also be due to the affordances at the interface being more facilitated in the non-anthropomorphic feedback

Usability of disaster apps : understanding the perspectives of the public as end-users : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Listed in 2020 Dean's List of Exceptional ThesesMultiple smartphone applications (apps) exist that can enhance the public’s resilience to disasters. Despite the capabilities of these apps, they can only be effective if users find them usable. Availability does not automatically translate to usability nor does it guarantee continued usage by the target users. A disaster app will be of little or no value if a user abandons it after the initial download. It is, therefore, essential to understand the users’ perspectives on the usability of disaster apps. In the context of disaster apps, usability entails providing the elements that effectively facilitate users in retrieving critical information, and thus enabling them to make decisions during crises. Establishing good usability for effective systems relies upon focussing on the user whereby technological solutions match the user’s needs and expectations. However, most studies on the usability of disaster context technologies have been conducted with emergency responders, and only a few have investigated the publics’ perspectives as end-users. This doctoral project, written within a ‘PhD-thesis-with-publication’ format, addresses this gap by investigating the usability of disaster apps through the perspectives of the public end-users. The investigation takes an explicitly perceived usability standpoint where the experiences of the end-users are prioritised. Data analysis involved user-centric information to understand the public’s context and the mechanisms of disaster app usability. A mixed methods approach incorporates the qualitative analysis of app store data of 1,405 user reviews from 58 existing disaster apps, the quantitative analysis of 271 survey responses from actual disaster app users, and the qualitative analysis of usability inquiries with 18 members of the public. Insights gathered from this doctoral project highlight that end-users do not anticipate using disaster apps frequently, which poses particular challenges. Furthermore, despite the anticipated low frequency of use, because of the life-safety association of disasters apps, end-users have an expectation that the apps can operate with adequate usability when needed. This doctoral project provides focussed outcomes that consider such user perspectives. First, an app store analysis investigating user reviews identified new usability concerns particular to disaster apps. It highlighted users’ opinion on phone resource usage and relevance of content, among others. More importantly, it defined a new usability factor, app dependability, relating to the life-safety context of disaster apps. App dependability is the degree to which users’ perceive that an app can operate dependably during critical scenarios. Second, the quantitative results from this research have contributed towards producing a usability-continuance model, highlighting the usability factors that affect end-users’ intention to keep or uninstall a disaster app. The key influences for users’ intention to keep disaster apps are: (1) users’ perceptions as to whether the app delivers its function (app utility), (2) whether it does so dependably (app dependability), and (3) whether it presents information that can be easily understood (user-interface output). Subsequently, too much focus on (4) user-interface graphics and (5) user-interface input can encourage users to uninstall apps. Third, the results from the qualitative analysis of the inquiry data provide a basis for developing guidelines for disaster app usability. In the expectation of low level of engagement with disaster app users, the guidelines list recommendations addressing information salience, cognitive load, and trust. This doctoral project provides several contributions to the body of knowledge for usability and disaster apps. It reiterates the importance of investigating the usability of technological products for disasters and showcases the value of user-centric data in understanding usability. It has investigated usability with particular attention to the end-users’ perspectives on the context of disaster apps and, thus, produces a theoretical usability-continuance model to advance disaster app usability research and usability guidelines to encourage responsible design in practice

Usability guidelines informing knowledge visualisation in demonstrating learners' knowledge acquisition

There is growing evidence that knowledge co-creation and interactivity during learning interventions aid knowledge acquisition and knowledge transfer. However, learners have mostly been passive consumers and not co-creators of the knowledge visualisation aids created by teachers and instructional designers. As such, knowledge visualisation has been underutilised for allowing learners to construct, demonstrate and share what they have learned. The dearth of appropriate guidelines for the use of knowledge visualisation for teaching and learning is an obstacle to using knowledge visualisation in teaching and learning. This provides a rationale for this study, which aims to investigate usability-based knowledge visualisation guidelines for teaching and learning. The application context is that of Science teaching for high school learners in the Gauteng province of South Africa. Following a design-based research methodology, an artefact of usability-based knowledge visualisation guidelines was created. The artefact was evaluated by testing learners’ conformity to the visualisation guidelines. Qualitative and quantitative data was captured using questionnaires, interviews and observations. The findings indicate that the guidelines considered in this study had various degrees of impact on the visualisations produced by learners. While some made noticeable impact, for others it could be considered negligible. Within the context of high school learning, these results justify the prioritisation of usability-based knowledge visualisation guidelines. Integrating Human Computer Interaction usability principles and knowledge visualisation guidelines to create usability-based knowledge visualisation guidelines provide a novel theoretical contribution upon which scientific knowledge visualisation can be expanded.School of ComputingM. Sc. (Computing

Approaches to visualising linked data: a survey

The uptake and consumption of Linked Data is currently restricted almost entirely to the Semantic Web community. While the utility of Linked Data to non-tech savvy web users is evident, the lack of technical knowledge and an understanding of the intricacies of the semantic technology stack limit such users in their ability to interpret and make use of the Web of Data. A key solution in overcoming this hurdle is to visualise Linked Data in a coherent and legible manner, allowing non-domain and non-technical audiences to obtain a good understanding of its structure, and therefore implicitly compose queries, identify links between resources and intuitively discover new pieces of information. In this paper we describe key requirements which the visualisation of Linked Data must fulfil in order to lower the technical barrier and make the Web of Data accessible for all. We provide an extensive survey of current efforts in the Semantic Web community with respect to our requirements, and identify the potential for visual support to lead to more effective, intuitive interaction of the end user with Linked Data. We conclude with the conclusions drawn from our survey and analysis, and present proposals for advancing current Linked Data visualisation efforts

Foresight Review on Design for Safety

This review explores how a culture of design for safety can enhance the safety of the world around us. Design for safety goes beyond legislation, regulations and standards. These all play an important role for established products and services but their limited scope often leads to missed opportunities to enhance safety by taking a broader perspective. Design is applied to both mature industries (which have many years of experience and a good understanding of risks and how to reduce them) and emerging industries (that use new technologies requiring new ways of controlling risk which may not yet be known or understood). An example of an emerging risk is the internet that is enabling rapid innovation of new products which generate data. This data is widely shared across the internet and the risks associated with this are as yet not fully understood by the public. A design for safety culture takes a holistic approach to understanding the influences that affect safety. Such influences are varied and take into account the broader environment within which design operates, including complex interactions, behaviour and culture. It goes beyond traditional design methods and focuses on the goal of a safer design. Implementing design for safety requires an understanding of the challenges and the methods to address them. It needs multidisciplinary teams that bring together people with the relevant skills to understand the challenges and a collaborative approach of ‘designing with’ rather than the more traditional approach of ‘designing for’. This can be achieved through an international diverse community that works together to identify and share best practices

Using data analysis and Information visualization techniques to support the effective analysis of large financial data sets

There have been a number of technological advances in the last ten years, which has resulted in the amount of data generated in organisations increasing by more than 200% during this period. This rapid increase in data means that if financial institutions are to derive significant value from this data, they need to identify new ways to analyse this data effectively. Due to the considerable size of the data, financial institutions also need to consider how to effectively visualise the data. Traditional tools such as relational database management systems have problems processing large amounts of data due to memory constraints, latency issues and the presence of both structured and unstructured data The aim of this research was to use data analysis and information visualisation techniques (IV) to support the effective analysis of large financial data sets. In order to visually analyse the data effectively, the underlying data model must produce results that are reliable. A large financial data set was identified, and used to demonstrate that IV techniques can be used to support the effective analysis of large financial data sets. A review of the literature on large financial data sets, visual analytics, existing data management and data visualisation tools identified the shortcomings of existing tools. This resulted in the determination of the requirements for the data management tool, and the IV tool. The data management tool identified was a data warehouse and the IV toolkit identified was Tableau. The IV techniques identified included the Overview, Dashboards and Colour Blending. The IV tool was implemented and published online and can be accessed through a web browser interface. The data warehouse and the IV tool were evaluated to determine their accuracy and effectiveness in supporting the effective analysis of the large financial data set. The experiment used to evaluate the data warehouse yielded positive results, showing that only about 4% of the records had incorrect data. The results of the user study were positive and no major usability issues were identified. The participants found the IV techniques effective for analysing the large financial data set