Implicit Measures of Lostness and Success in Web Navigation

In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The user’s task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design

Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking

Large, high-resolution displays carry the potential to enhance single display groupware collaborative sensemaking for intelligence analysis tasks by providing space for common ground to develop, but it is up to the visual analytics tools to utilize this space effectively. In an exploratory study, we compared two tools (Jigsaw and a document viewer), which were adapted to support multiple input devices, to observe how the large display space was used in establishing and maintaining common ground during an intelligence analysis scenario using 50 textual documents. We discuss the spatial strategies employed by the pairs of participants, which were largely dependent on tool type (data-centric or function-centric), as well as how different visual analytics tools used collaboratively on large, high-resolution displays impact common ground in both process and solution. Using these findings, we suggest design considerations to enable future co-located collaborative sensemaking tools to take advantage of the benefits of collaborating on large, high-resolution displays

P2P Mapper: From User Experiences to Pattern-Based Design

User experience is an umbrella term referring to a collection of information that covers the user’s behavior and interaction with a system. It is observed when the user is actively using a service or interacting with information, includes expectations and perceptions, and is influenced by user characteristics and application or service characteristics. User characteristics include knowledge, experience, personality and demographics. We propose a process and supporting software tool called Persona to Pattern (P2P) Mapper, which guides designers in modeling user experiences and identifying appropriate design patterns. The three-step process is: Persona Creation (a representative persona set is developed), Pattern Selection (behavioral patterns are identified resulting in an ordered list of design patterns for each persona), and Pattern Composition (patterns are used to create a conceptual design). The tool supports the first two steps of the process by providing various automation algorithms for user grouping and pattern selection combined with the benefit of rapid pattern and user information access. Persona and pattern formats are augmented with a set of discrete domain variables to facilitate automation and provide an alternative view on the information. Finally, the P2P Mapper is used in the redesign of two different Bioinformatics applications: a popular website and a visualization tool. The results of the studies demonstrate a significant improvement in the system usability of both applications

Understanding Novice Users\u27 Help-seeking Behavior in Getting Started with Digital Libraries: Influence of Learning Styles

Users\u27 information needs have to be fulfilled by providing a well-designed system. However, end users usually encounter various problems when interacting with information retrieval (IR) systems and it is even more so for novice users. The most common problem reported from previous research is that novice users do not know how to get started even though most IR systems contain help mechanisms. There is a deep gap between the system\u27s help function and the user\u27s need. In order to fill the gap and provide a better interacting environment, it is necessary to have a clearer picture of the problem and understand what the novice users\u27 behaviors are in using IR systems. The purpose of this study is to identify novice users\u27 help-seeking behaviors while they get started with digital libraries and how their learning styles lead to these behaviors. While a novice user is engaged in the process of interacting with an IR system, he/she may easily encounter problematic situations and require some kind of help in the search process. Novice users need to learn how to use a new IR environment by interacting with help features to fulfill their searching needs. However, many research studies have demonstrated that the existing help systems in IR systems cannot fully satisfy users\u27 needs. In addition to the system side problems, users\u27 characteristics, such as preference in using help, also play major roles in the decision of using system help. When viewing help-seeking as a learning activity, learning style is an influential factor that would lead to different help-seeking behaviors. Learning style deeply influences how students process information in learning activities, including learning performance, learning strategy, and learning preferences. Existing research does not seem to consider learning style and help-seeking together; therefore, the aim of this study is to explore the effects of learning styles on help-seeking interactions in the information seeking and searching environment. The study took place in an academic setting, and recruited 60 participants representing students from different education levels and disciplines. Data were collected by different methods, including pre-questionnaire, cognitive preference questionnaire, think-aloud protocol, transaction log, and interview. Both qualitative and quantitative approaches were employed to analyze data in the study. Qualitative methods were first applied to explore novice users\u27 help-seeking approaches as well as to illustrate how learning styles lead to these approaches. Quantitative methods were followed to test whether or not learning style would affect help-seeking behaviors and approaches. Results of this study highlight two findings. First, this study identifies eight types of help features used by novice users with different learning styles. The quantitative evidence also verifies the effect of learning styles on help-seeking interactions with help features. Based on the foundation of the analysis of help features, the study further identified fifteen help-seeking approaches applied by users with different learning styles in digital libraries. The broad triangulation approach assumed in this study not only enables the illustration of novice users\u27 diversified help-seeking approaches but also explores and confirms the relationships between different dimensions of learning styles and help-seeking behaviors. The results also suggest that the designs and delivery of IR systems, including digital libraries, need to support different learning styles by offering more engaging processing layouts, diversified input formats, as well as easy-to-perceive and easy-to-understand modes of help features

Player Behavior Modeling In Video Games

Player Behavior Modeling in Video Games In this research, we study players’ interactions in video games to understand player behavior. The first part of the research concerns predicting the winner of a game, which we apply to StarCraft and Destiny. We manage to build models for these games which have reasonable to high accuracy. We also investigate which features of a game comprise strong predictors, which are economic features and micro commands for StarCraft, and key shooter performance metrics for Destiny, though features differ between different match types. The second part of the research concerns distinguishing playing styles of players of StarCraft and Destiny. We find that we can indeed recognize different styles of playing in these games, related to different match types. We relate these different playing styles to chance of winning, but find that there are no significant differences between the effects of different playing styles on winning. However, they do have an effect on the length of matches. In Destiny, we also investigate what player types are distinguished when we use Archetype Analysis on playing style features related to change in performance, and find that the archetypes correspond to different ways of learning. In the final part of the research, we investigate to what extent playing styles are related to different demographics, in particular to national cultures. We investigate this for four popular Massively multiplayer online games, namely Battlefield 4, Counter-Strike, Dota 2, and Destiny. We found that playing styles have relationship with nationality and cultural dimensions, and that there are clear similarities between the playing styles of similar cultures. In particular, the Hofstede dimension Individualism explained most of the variance in playing styles between national cultures for the games that we examined