In this paper, we describe two experiments that show the powerful influence of interface complexity and entropy on online information sharing behaviour. 134 participants were asked to do a creativity test and answer six open questions against three different screen backgrounds of increasing complexity. Our data shows that, as an interface becomes more complex and has more entropy users refer less to themselves and show less information sharing breadth. However, their verbal creativity and information sharing depth do not suffer in the same way. Instead, an inverse U shaped relationship between Interface complexity and creativity as well as information sharing depth can be observed: Users become more creative and thoughtful until a certain tipping point of interface complexity is reached. At that point, creativity and th inking suffer, leading to significantly less disclosure. This result challenges the general HCI assumption that simplicity is always best for computers interface design , as users'creativity and information sharing depth initially increases with more interface complexity. Our results suggest that the Yerkes Dodson Law may be a key theory underlying online creativity and depth of online disclosures.

Mixing audio is not only an aural activity, but also, becoming increasingly more visual, and connections between the engineer’s action and interface are not yet fully understood. This study aimed to investigate how different amounts and types of data displayed in parametric equalizers impact mix decisions and workflow. A usability test with audio engineer students were conducted. The test consisted of three different interfaces with variable information displayed and subjects were to perform six common equalization tasks. Two categories of stimuli were used, surgical and aesthetic. The parameters that were measured was effectiveness, efficiency, workflow, and preference. A post session questionnaire was also conducted. Each interface and category were compared and a t-test for each comparison were conducted. Task completion and time for task completion were determined and calculated. Eight t-tests were also conducted between interfaces. Parameters adjusted, order of adjustment, and most used parameter were determined. Preference ratings were categorized and analyzed. The results show that the EQ with graphic display performed better for the surgical category. Significant differences were also found for the surgical category between interfaces with most dissimilarities. The workflow tended to alternate between interface designs. The preference rating showed a clear preference for the interface with graphic display.

Fully Autonomous Vehicles (FAVs) have the potential to provide safer vehicle operation and to enhance the overall transportation system. However, drivers and vehicles are not the only components that need to be considered. Research has shown that pedestrians are among the most unpredictable and vulnerable road users. To achieve full and successful implementation of FAVs, it is essential to understand pedestrian acceptance and intended behavior regarding FAVs. Three studies were developed to address this need: (1) development of a standardized framework to investigate pedestrians’ behaviors for the U.S. population; (2) development of a framework to evaluate their receptivity of FAVs; and (3) investigation of the influence of the external interacting interfaces of FAVs on pedestrian receptivity toward them. The pedestrian behavior questionnaire (PBQ) categorized pedestrian general behaviors into five factors: violations, errors, lapses, aggressive behaviors, and positive behaviors. The first four factors were found to be both valid and reliable; the positive behavior scale was not found to be reliable nor valid. A long (36-item) and a short (20-items) versions of the PBQ were validated by regressing scenario-based survey responses to the fiveactor PBQ subscale scores. The pedestrian receptivity questionnaire for FAVs (PRQF) consisted of three subscales: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and the reliability of each subscale was confirmed. Regression analyses showed that pedestrians’ intention to cross the road in front of a FAV was significantly predicted by both safety and interaction scores, but not by the compatibility score. On the other hand, acceptance of FAVs in the existing traffic system was predicted by all three subscale scores. Finally, an experimental study was performed to expose pedestrians to a simulated environment where they could experience a FAV. The FAV in the simulated environment was either equipped with external features (audible and/or visual) or had no external (warning) feature. The least preferred options were the FAVs with no features and those with a smiley face but no audible cue. The most preferred interface option, which instilled confidence for crossing in front of the FAV, was the walking silhouette.

Background: Electronic prescribing systems are designed to aid in the complex process of prescribing by providing patient information and decision support at the point of care. Successful implementation and effectiveness depend on a variety of factors, including usability and user interface design, which influence how the information and decision support are relayed to users. Poorly designed systems have been found to be associated with medication errors. Methods: We conducted a factorial design study to investigate the impact of screen density, highlighting, and placement of information, on the accuracy of prescribing when using an e-prescribing system. Study sessions were held during clinical pharmacology educational rounds, where residents and medical students answered simulated prescribing scenarios presented on various e-prescribing system interface configurations. Assignment of prescribing scenarios to interface configurations and presentation order were randomized between study sessions. Participants were also asked about their preferences for specific user interface configurations. Results: A total of 66 participants completed 844 prescribing cases, with 583 (69%) cases answered correctly. The presence of highlighting was associated with correct prescribing decisions (p-value = 0.001), with 181 out of 250 (72.4%) prescribing scenarios answered correctly on interfaces with highlighting of key clinical information, as opposed to 156 out of 242 (64.5%) on interface configurations without. Low screen density and central placement of information were not found to be statistically significant predictors of prescribing accuracy. The presence of highlighting was the only factor that the majority of participants (80.3%) preferred, but no effect was found when comparing prescribing accuracy on preferred versus non-preferred interface configurations. Conclusions: The factorial design methodology developed is a novel approach for efficient and objective evaluation of multiple user interface design factors in one study. Evidence-based design and usability principles are needed to enhance the design and appropriate use of e-prescribing systems as usability problems continue to be one of the primary reasons for dissatisfaction and poor levels of adoption. / Thesis / Master of Science (MSc)

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While exploring various subjects in multiple techniques in multiple mediums, I create abstract images that illustrate high technology, urban life, and personal feelings. My work is influenced by Eastern and Western culture. Media, publication, and fashion are derived from Western culture, and design elements are derived from Eastern culture. In doing this, my goal is to achieve more opportunities, greater depth, and a wider audience for my work by cross-utilizing these diverse culture aspects.

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.