A HUMAN-CENTERED APPROACH TO IMPROVING THE USER EXPERIENCE OF SOFTWARE UPDATES

Software updates are critical to the security of software systems and devices. Yet users often do not install them in a timely manner, leaving their devices open to security exploits. This research explored a re-design of automatic software updates on desktop and mobile devices to improve the uptake of updates through three studies. First using interviews, we studied users’ updating patterns and behaviors on desktop machines in a formative study. Second, we distilled these findings into the design of a low-fi prototype for desktops, and evaluated its efficacy for automating updates by means of a think-aloud study. Third, we investigated individual differences in update automation on Android devices using a large scale survey, and interviews. In this thesis, I present the findings of all three studies and provide evidence for how automatic updates can be better appropriated to fit users on both desktops and mobile devices. Additionally, I provide user interface design suggestions for software updates and outline recommendations for future work to improve the user experience of software updates.

(inter)FACE: A Study of Black Families Advocating for their Children’s Education

Black students are consistently overrepresented in categories of academic underachievement. Parent engagement has long been touted as an effective strategy for improving the educational outcomes of Black children. However, most parent engagement research reflects deficit based perspectives frame Black parents as problems that must be fixed or mitigated before they can positively contribute to their children’s education. Consequently, parent engagement research and frameworks ignore the perspectives of Black parents and the assets they use to participate effectively in parent engagement. In this case study, I draw on individual and focus group interview data, documents, and observations, to examine how fifteen Black families, collectively known as FACE: 1) define and participate in parental engagement, 2) experience barriers to and opportunities for engagement, and 3) experience benefits of engagement for their children and their own personal development. Guided by Black Feminist and Critical Race Theories, I show how Black families in this study used a myriad of engagement strategies to improve their children’s educational experiences which were invisible to schools and how they used school-sanctioned engagement activities to meet their own objectives. Ultimately, I argue that school-centered parent engagement frameworks and models are ineffective for empowering Black families and accounting for the essential ways that these families contribute to the well-being of their children. Based on my findings, I discuss implications for theory, practice and policy, and research, and make recommendations for a more family-centered approach to parent engagement.

FAMILY CHARACTERISTICS, STATE PROGRAM EFFECTIVENESS, AND REPORT OF INADEQUATE CARE AMONG FAMILIES OF YOUNG CHILDREN WITH HEARING LOSS

Approximately 1.6 per 1,000 newborns in the U.S. are born with hearing loss. Congenital hearing loss poses a risk to their speech, language, cognitive, and social-emotional development. Early detection and intervention can improve outcomes. Every state has an Early Hearing Detection and Intervention program (EHDI) to promote and track screening, audiological assessments and linkage to early intervention. However, a large percentage of children are “lost to system (LTS),” meaning that they did not receive recommended care or that it was not reported. This study used data from the 2009-2010 National Survey of Children with Special Health Care Needs and data from the 2011 EHDI Hearing Screening and Follow-Up Survey to examine how 1) family characteristics; 2) EHDI program effectiveness, as determined by LTS percentages; and 3) the family conditions of education and poverty are related to parental report of inadequate care. The sample comprised 684 children between the ages of 0 and 5 years with hearing loss. The results indicated that living in states with less effective EHDI programs was associated with an increased likelihood of not receiving early intervention services (EIS) and of reporting poor family-centered communication. Sibling classification was associated with both receipt of EIS and report of unmet need. Single mothers were less likely to report increased difficulties accessing care. Poor and less educated families, assessed separately, who lived in states with less effective EHDI programs, were more likely to report non-receipt of EIS and less likely to report unmet need as compared to similar families living in states with more effective programs. Poor families living in states with less effective programs were more likely to report less coordinated care than were poor families living in states with more effective programs. This study supports the conclusion that both family characteristics and the effectiveness of state programs affect quality of care outcomes. It appears that less effective state programs affect disadvantaged families’ service receipt report more than that of advantaged families. These findings are important because they may provide insights into the development of targeted efforts to improve the system of care for children with hearing loss.

INTERGENERATIONAL SUPPORT AND WELL-BEING OF OLDER ADULTS IN CHANGING FAMILY CONTEXTS

This dissertation consists of three papers that examine the complexities in upward intergenerational support and adult children’s influence on older adults’ health in changing family contexts of America and China. The prevalence of “gray divorce/repartnering ” in later life after age 55 is on the rise in the United States, yet little is known about its effect on intergenerational support. The first paper uses the life course perspective to examine whether gray divorce and repartnering affect support from biological and stepchildren differently than early divorce and repartnering, and how patterns differ by parents’ gender. Massive internal migration in China has led to increased geographic distance between adult children and aging parents, which may have consequences for old age support received by parents. This topic has yet to be thoroughly explored in China, as most studies of intergenerational support to older parents have focused on the role of coresident children or have not considered the interdependence of multiple parent-child dyads in the family. The second paper adopts the within-family differences approach to assess the influence of non-coresident children’s relative living proximity to parents compared to that of their siblings on their provision of support to parents in rural and urban Chinese families. The study also examines how patterns of the impact are moderated by parents’ living arrangement, non-coresident children’s gender, and parents’ provision of support to children. Taking a multigenerational network perspective, the third paper questions if and how adult children’s socioeconomic status (SES) influences older parents’ health in China. It further examines whether health benefits brought by adult children’s socioeconomic attainment are larger for older adults with lower SES and whether one of the mechanisms through which adult children’s SES affects older parents’ health is by changing their health behaviors. These questions are highly relevant in contemporary China, where adult children have experienced substantial gains in SES and play a central role in old age support for parents. In sum, these three papers take the life course, the within-family differences, and the multigenerational network perspective to address the complexities in intergenerational support and older adults’ health in diverse family contexts.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.