No / A mobile language learning system is implemented using an adventure game. The primary emphasis is upon graphical design and rich interaction with the user. A wide range of functionalities are described, and an efficient navigation system is proposed that uses contextual information, allowing the players to move seamlessly between mobile real and virtual worlds. The game environment is designed to have consistent graphics, dialogue, screens, and sequences of actions. Quick Response (QR) codes provide the necessary shortcuts for the players and Bluetooth connections automatically send and receive scores between teams. A response for every action is produced depending on the screen type, while keeping the file size manageable. Similar user tasks were kept spatially close together with a clearly designated beginning, middle and end. The main sources of error such as entering and extracting contextual data are predicted and simple error handling is provided. Unexpected events in mobile environments are tolerated and allowed. Internal locus of control is provided by ‘automatic pause’, ‘manual pause’ and ‘save’ commands to help players preserve their data and cognitive progress. The game environment is configurable for novice or expert players. This game is also suitable for students with auditory problems and female students are also specifically addressed.

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High-dimensional data is prevalent in all domains but is challenging to explore. Analysis and exploration of high-dimensional data are important for people in numerous fields. To help people explore and understand high-dimensional data, Andromeda, an interactive visual analytics tool, has been developed. However, our analysis uncovered several cognitive gaps relating to the Andromeda system: users do not realize the necessity of explicitly highlighting all the relevant data points; users are not clear about the dimensional information in the Andromeda visualization; and the Andromeda model cannot capture user intentions when constructing and deconstructing clusters. In this study, we designed and implemented solutions to address these gaps. Specifically, for the gap in highlighting all the relevant data points, we introduced a foreground and background view and distance lines. Our user study with a group of undergraduate students revealed that the foreground and background views and distance lines could significantly alleviate the highlighting issue. For the gap in understanding visualization dimensions, we implemented a dimension-assist feature. The results of a second user study with students with various backgrounds suggested that the dimension-assist feature could make it easier for users to find the extremum in one dimension and to describe correlations among multiple dimensions; however, the dimension-assist feature had only a small impact on characterizing the data distribution and assisting users in understanding the meanings of the weighted multidimensional scaling (WMDS) plot axes. Regarding the gap in creating and deconstructing clusters, we implemented a solution utilizing random sampling. A quantitative analysis of the random sampling strategy was performed, and the results demonstrated that the strategy improved Andromeda's capabilities in constructing and deconstructing clusters. We also applied the random sampling to two-point manipulations, making the Andromeda system more flexible and adaptable to differing data exploration tasks. Limitations are discussed, and potential future research directions are identified. / Master of Science / High-dimensional data is the dataset with hundreds or thousands of features. The animal dataset, which has been used in this study, is an example of high-dimensional dataset, since animals can be categorized by a lot of features, such as size, furry, behavior and so on. High-dimensional data is prevalent but difficult for people to analyze. For example, it is hard to find out the similarity among dozens of animals, or to find the relationship between different characterizations of animals. To help people with no statistical knowledge to analyze the high-dimensional dataset, our group developed a web-based visualization software called Andromeda, which can display data as points (such as animal data points) on a screen and allow people to interact with these points to express their similarity by dragging points on the screen (e.g., drag "Lion," "Wolf," and "Killer Whale" together because all three are hunters, forming a cluster of three animals). Therefore, it enables people to interactively analyze the hidden pattern of high-dimensional data. However, we identified several cognitive gaps that have negatively limited Andromeda's effectiveness in helping people understand high-dimensional data. Therefore, in this work, we intended to make improvements to the original Andromeda system to bridge these gaps, including designing new visual features to help people better understand how Andromeda processes and interacts with high-dimensional data and improving the underlying algorithm so that the Andromeda system can better understand people's intension during the data exploration process. We extensively evaluated our designs through both qualitative and quantitative analysis (e.g., user study on both undergraduate and graduate students and statistical testing) on our animal dataset, and the results confirmed that the improved Andromeda system outperformed the original version significantly in a series of high-dimensional data understanding tasks. Finally, the limitations and potential future research directions were discussed.

As large displays become more affordable, researchers investigate their productivity impacts and try to develop techniques for making the large display user experience more effective. Studies show that large displays enable users to create and manage more windows and engage in more complex multitasking behavior. Although recent work demonstrates significant productivity benefits of large shared displays, it shows numerous usability issues because current software design is not scaling well. Therefore, we took steps towards developing two user interface design guidelines for large shared displays. Specifically, empirical studies have been conducted to compare the effects of large shared display and personal display use. When each of them is used as a secondary display, large shared displays impose increased interruption and comprehension. Empirical and qualitative studies are designed to develop two user interface design guidelines for large shared displays. We designed a system called SuperTrack that uses a large shared display and the proposed guidelines to further enhance team efficiency and productivity in collaborative software development environments. Finally, an in-situ evaluation assesses the benefits of SuperTrack. Results show that exposing software development team members to a large shared display through SuperTrack leads to more communication among the members and improved group awareness — leading to increased productivity and efficiency. / Master of Science

High-dimensional data appear in all domains and it is challenging to explore. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Data analysis and exploration is an important skill given the amount of data collection in every field of work. However, learning this skill without an understanding of high-dimensional data is challenging. Users naturally tend to characterize data in simplistic one-dimensional terms using metrics such as mean, median, mode. Real-world data is more complex. To gain the most insight from data, users need to recognize and create high-dimensional arguments. Data exploration methods can encourage thinking beyond traditional one-dimensional insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be manipulated to assess the data from multiple perspectives. Manipulating can be difficult for users without a strong knowledge of the underlying algorithms. Visual analytics tools that afford object-level interaction (OLI) allow for generation of more complex insights, despite inexperience with multivariate data or the underlying algorithm. The goal of this research is to develop and test variations on types of interactions for interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To study interactive data analysis, we present an interface, Andromeda, that enables non-experts of statistical models to explore domain-specific, high-dimensional data. This application implements interactive weighted multidimensional scaling (WMDS) and allows for both parametric and observation-level interaction to provide in-depth data exploration. We performed multiple user studies to answer how parametric and object-level interaction aid in data analysis. With each study, we found usability issues and then designed solutions for the next study. With each critique we uncovered design principles of effective, interactive, visual analytic tools. The final part of this research presents these principles supported by the results of our multiple informal and formal usability studies. The established design principles focus on human-centered usability for developing interactive visual analytic systems that enable users to analyze high-dimensional data through object-level interaction. / Ph. D.

Wearable computers allow users the freedom to work in any environment including hazardous environments that may require protective clothing. Past research has shown that protective clothing interferes with manual materials handling tasks, medical tasks, and manual dexterity tasks. However, little information exists regarding how protective clothing affects task performance with wearable input devices. As a result, a study was conducted to address this issue and offer recommendations to enhance the compatibility of chemical protective clothing and wearable input devices. Sixteen active-duty soldiers performed a text-entry task with a wearable mouse and touch pad, while bare handed, wearing 7-mil, 14-mil, and 25-mil chemical protective gloves, wearing a respirator alone, and wearing the respirator and each of three gloves. Upon completion of the experiment, participants rated task difficulty, confidence using the input device, and input device preference. Task completion times were 9% slower with the 25-mil glove than the 7-mil glove. Text entry was not perceived as difficult when bare handed, or wearing the 7-mil and 14-mil gloves, suggesting that thin chemical protective gloves (i.e. 7-mil and 14-mil) are more suitable than thicker gloves for use with wearable input devices. When using the touch pad, task completion times were 17% faster than when using the mouse. Subjective ratings of difficulty, confidence, and preference provide strong support for the use of a touch pad input device rather than a mouse. / Master of Science

The strengthening of community care and the development of co-managed telehealth systems are vital components in addressing growing critical healthcare issues encountered worldwide. The global COVID pandemic highlights the challenges in providing appropriate co-managed home-based care in a systemic and financially viable way at scale. To develop practical and sustainable solutions it is important to understand the individual, institutional, and socio-technical opportunities and barriers potentially encountered when attempting to design and implement telehealth systems as part of a broader social healthcare network. In this thesis, I describe my work assessing the feasibility of deploying telehealth systems within the context of home based physical rehabilitation. I conducted an online survey and in-depth interviews with occupational and physical therapists to determine the issues impacting their current practices and the likelihood that a telehealth rehabilitation system might support or hinder their practice. Findings from this qualitative work highlighted the importance of maintaining the patient/therapist relationship, the need to empower the caregiver, and the potential for telehealth systems to provide quantitative and qualitative proof of care and patient progress. Building on these insights, I designed an interactive tablet application to assist therapists with the efficient and seamless installation and calibration of a telehealth system for stroke rehabilitation in the home. The application was evaluated in two studies with non-expert and expert users. The results from these studies indicate the efficiency of the application resulting from this design approach and the rich potential for integration of the system into clinical practice. / Master of Science / The movement away from traditional medical establishments and the development of virtual health systems designed to be placed in the home are vital components in addressing growing critical healthcare issues encountered worldwide. The global COVID-19 pandemic highlights the challenges in providing appropriate home-based care in a scaleable and financially viable way. To develop practical and sustainable solutions, it is important to understand the individual, institutional, and socio-technical opportunities and barriers potentially encountered when attempting to design and implement them. In this thesis, I describe my work assessing the feasibility of deploying home-based health systems designed to assist stroke patients with physical rehabilitation. I conducted an online survey and in-depth interviews with occupational and physical therapists to determine the issues impacting them and the likelihood that a home-based rehabilitation system might support or hinder their work. Findings from this qualitative work highlighted the importance of maintaining the patient/therapist relationship, the need to support the caregiver, and the potential for home-based systems to provide proof of patient improvement. Building on these insights, I designed a tablet application to assist therapists with the efficient and seamless set up and calibration of a home-based system for stroke rehabilitation. The application was evaluated in two studies with non-expert and expert users. The results from these studies indicate the effectiveness of the application resulting from this design approach and the potential for integration of the system into the lives of therapists.

As large displays become more affordable, researchers investigate their effects on productivity and try to develop techniques for making the large display user experience more effective. Recent work has demonstrated significant productivity benefits, but has also identified numerous usability issues with current software design not scaling well. Studies show that large displays enable users to create and manage more windows, as well as to engage in more complex multitasking behavior. In this thesis, we developed some user interface design guidelines for large shared displays. Specifically, empirical studies to compare the effects of using large shared displays against personal displays when each of them is used as a secondary display are presented, showing that large shared display impose higher interruption and comprehension to the user. Empirical and qualitative studies are designed to develop two user interface design guidelines for large shared displays. We designed a system called SuperTrack that uses LSD along with the guidelines to further enhance and improve team efficiency and productivity in collaborative software development environments. Finally, an in-situ evaluation assesses the benefits of SuperTrack based on our developed design guidelines in terms of improving software development efficiency and productivity. Results show that by exposing software development team members to a large shared display, a system that follows our developed user interface guidelines leads to higher communication among the team members and improved group awareness, leading to higher productivity and efficiency. / Master of Science

The goal of this research was to compare the learning outcomes of three methods of instruction: a text-based instructional system and two multimedia systems. The two multimedia systems used different interface designs. The first multimedia system used a topic-oriented interface which is somewhat standard in multimedia design. The second multimedia system presented a problem solving context and simulated an industrial setting where the user played the role of an industrial engineer. All three methods presented analogous information about Time Study Analysis, a work measurement technique used by industrial engineers. A between subjects experimental design with two independent measures examined two domains of learning: verbal information and intellectual skills. This design was used for two sessions to examine original learning and retention components of learning. Original learning was measured immediately following the instructional treatment. Retention was measured two weeks after treatment. Thirty subjects of similar backgrounds (undergraduates in Industrial and Systems Engineering) participated in the experiment’s two sessions. Post-tests were used to measure verbal information and intellectual skills domains of learning during each session. A combined score for both domains was calculated. The scores were analyzed using ANOVA (analysis of variance). No significant differences were found between the three instructional methods for the two domains or the combined score during either the original learning session or the retention session of the experiment. / Master of Science