Eva-Catherine Hillemann

User needs for enhanced engagement with cultural heritage collections

This paper presents research carried out in order to elicit user needs for the design and development of a digital library and research platform intended to enhance user engagement with cultural heritage collections. It outlines a range of user constituencies for this digital library. The paper outlines a taxonomy of intended users for this system and describes in detail the characteristics and requirements of these users for the facilitation and enhancement of their engagement with and use of textual and visual cultural artefacts.

Tracking the UFO's paths: using eye-tracking for the evaluation of serious games

Computer games are undoubtedly an enormously successful genre. Over the past years, a continuously growing community of researchers and practitioners made the idea of using the potential of computer games for serious, primarily educational purposes equally popular. However, the present hype over serious games is not reflected in sound evidence for the effectiveness and efficiency of such games and also indicators for the quality of learner-game interaction is lacking. In this paper we look into those questions, investigating a geography learning game prototype. A strong focus of the investigation was on relating the assessed variables with gaze data, in particular gaze paths and interaction strategies in specific game situations. The results show that there a distinct gender differences in the interaction style with different game elements, depending on the demands on spatial abilities (navigating in the threedimensional spaces versus controlling rather two-dimensional features of the game) as well as distinct differences between high and low performers.

Evaluating a digital humanities research environment: the CULTURA approach

Digital humanities initiatives play an important role in making cultural heritage collections accessible to the global community of researchers and general public for the first time. Further work is needed to provide useful and usable tools to support users in working with those digital contents in virtual environments. The CULTURA project has developed a corpus agnostic research environment integrating innovative services that guide, assist and empower a broad spectrum of users in their interaction with cultural artefacts. This article presents (1) the CULTURA system and services and the two collections that have been used for testing and deploying the digital humanities research environment, and (2) an evaluation methodology and formative evaluation study with apprentice researchers. An evaluation model was developed which has served as a common ground for systematic evaluations of the CULTURA environment with user communities around the two test bed collections. The evaluation method has proven to be suitable for accommodating different evaluation strategies and allows meaningful consolidation of evaluation results. The evaluation outcomes indicate a positive perception of CULTURA. A range of useful suggestions for future improvement has been collected and fed back into the development of the next release of the research environment.

A Framework for Cognitive Bias Detection and Feedback in a Visual Analytics Environment

This paper presents a framework that supports the detection and mitigation of cognitive biases in visual analytics environments for criminal analysis. Criminal analysts often use visual analytics environments for their analysis of large data sets, for gaining insights on criminal events and patterns of criminal events, and for drawing conclusions and making decisions. However, due to the nature of human cognition, these cognitive processes may lead to systematic errors, so-called cognitive biases. The most prominent and relevant cognitive bias in the intelligence field is the confirmation bias, in which an analyst disproportionally considers and selects information that supports the initial expectation and hypothesis. The framework presented in this paper describes a model, how the possible occurence of the confirmation bias can be detected automatically, while the analyst makes use of the visual environment. Moreover, based on this information, different feedback methods are employed that support and encourage the mitigation of the confirmation bias. This framework is in a work-in-progress state and contains research objectives and directions, the framework design, initial implementations, plans for further development and integration, as well as user-centric evaluation.

Exploring the Challenges of Implementing Guidelines for the Design of Visual Analytics Systems

Intelligence analysis activities are increasingly seen as a sense-making activity. Information systems supporting these activities have, therefore, to be designed in a way to enable analysts to engage in sensemaking in an efficient manner. In the context of the VALCRI project, we developed guidelines for an appropriate design of intelligence analysis systems. Such guidelines can be very valuable in the design process, but their application is sometimes not straightforward. We describe such problems and also suggest possible solutions for these application problems within the context of intelligence analysis.

An evaluation system for digital libraries

Evaluation is an important task for digital libraries, because it reveals relevant information about their quality. This paper presents a conceptual and technical approach to support the systematic evaluation of digital libraries in three ways and a system is presented that assists during the entire evaluation process. First, it allows for formally modelling the evaluation goals and designing the evaluation process. Second, it allows for data collection in a continuous and non-continuous, invasive and non-invasive way. Third, it automatically creates reports based on the defined evaluation models. On the basis of an example evaluation it is outlined how the evaluation process can be designed and supported with this system.

The Role of Cognitive Biases in Criminal Intelligence Analysis and Approaches for their Mitigation

In the domain of criminal intelligence analysis, each day an analyst has to make sense and to create insight of a large amount of different data. However, due to the nature of human cognition, these cognitive processes may lead to systematic errors, so-called cognitive biases. In this paper, based on relevant state-of-the-art, preliminary ideas how to support the mitigation of cognitive biases - included in a visual analytics environment for criminal intelligence analysis currently being developed in the VALCRI project - are presented. By analysing user requirements and aligning them to the state-of-the-art research in the area of cognitive biases and their mitigation, eight cognitive biases have been identified as the most relevant ones. Six design guidelines have been proposed that help to mitigate one of them, the confirmation bias. At the current stage, the suggested mitigation strategies and resulting guidelines are the basis for further research, development, and experiments in order to derive evidence-based scientific results.

A Framework for Measuring Imagination in Visual Analytics Systems

This paper presents a framework for measuring imagination support in criminal analysis systems. Imagination is important for criminal analysts in their everyday work when they have to solve criminal cases. Typically, they are faced with a huge amount of information that is often ill-structured, do not contain all relationships, and are characterised by many uncertainties. In order to draw correct conclusions and to solve cases, analysts need imagination to find out facts from such data, or in other words: to detect the signals out from the noise. This paper describes a general framework for introducing imagination support in criminal analysis systems. The framework consists of two parts, first the operationalisation of imagination, and second, guidelines for an experimental setting of evaluating criminal analysis systems regarding their imagination support. This work is intended to serve as a baseline for future evaluation work of criminal analysis systems.