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Browsing by Author "Bolchini, Davide"
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Item Active Reading Behaviors in Tablet-based Learning(AACE, 2015-07) Palilonis, Jennifer; Bolchini, Davide; Department of Human-Centered Computing, School of Informatics and ComputingActive reading is fundamental to learning. However, there is little understanding about whether traditional active reading frameworks sufficiently characterize how learners study multimedia tablet textbooks. This paper explores the nature of active reading in the tablet environment through a qualitative study that engaged 30 students in an active reading experience with two tablet textbook modules. We discovered novel study behaviors learners enact that are key to the active reading experience with tablet textbooks. Results illustrate that existing active reading tools do little to support learners when they struggle to make sense of and subsequently remember content delivered in multiple media formats, are distracted by the mechanics of interactive content, and grapple with the transient nature of audiovisual material. We collected valuable user feedback and uncovered key deficiencies in existing active reading tools that hinder successful multimedia tablet textbook reading experiences. Our work can inform future designs of tools that support active reading in this environment.Item ACTIVE READING ON TABLET TEXTBOOKS(2015-04-17) Palilonis, Jennifer Ann; Defazio, Joseph; Bolchini, Davide; Butler, Darrell; Voida, AmyTo study a text, learners often engage in active reading. Through active reading, learners build an analysis by annotating, outlining, summarizing, reorganizing and synthesizing information. These strategies serve a fundamental meta-cognitive function that allows content to leave strong memory traces and helps learners reflect, understand, and recall information. Textbooks, however, are becoming more complex as new technologies change how they are designed and delivered. Interactive, touch-screen tablets offer multi-touch interaction, annotation features, and multimedia content as a browse-able book. Yet, such tablet textbooks-in spite of their increasing availability in educational settings-have received little empirical scrutiny regarding how they support and engender active reading. To address this issue, this dissertation reports on a series of studies designed to further our understanding of active reading with tablet textbooks. An exploratory study first examined strategies learners enact when reading and annotating in the tablet environment. Findings indicate learners are often distracted by touch screen mechanics, struggle to effectively annotate information delivered in audiovisuals, and labor to cognitively make connections between annotations and the content/media source from which they originated. These results inspired SMART Note, a suite of novel multimedia annotation tools for tablet textbooks designed to support active reading by: minimizing interaction mechanics during active reading, providing robust annotation for multimedia, and improving built-in study tools. The system was iteratively developed through several rounds of usability and user experience evaluation. A comparative experiment found that SMART Note outperformed tablet annotation features on the market in terms of supporting learning experience, process, and outcomes. Together these studies served to extend the active reading framework for tablet textbooks to: (a) recognize the tension between active reading and mechanical interaction; (b) provide designs that facilitate cognitive connections between annotations and media formats; and (c) offer opportunities for personalization and meaningful reorganization of learning material.Item Advocacy in Mental Health Social Interactions on Public Social Media(2022-02) Cornet, Victor P.; Holden, Richard J.; Bolchini, Davide; Brady, Erin; Mohler, George; Hong, Michin; Lee, SangwonHealth advocacy is a social phenomenon in which individuals and collectives attempt to raise awareness and change opinions and policies about health-related causes. Mental health advocacy is health advocacy to advance treatment, rights, and recognition of people living with a mental health condition. The Internet is reshaping how mental health advocacy is performed on a global scale, by facilitating and broadening the reach of advocacy activities, but also giving more room for opposing mental health advocacy. Another factor contributing to mental health advocacy lies in the cultural underpinnings of mental health in different societies; East Asian countries like South Korea have higher stigma attached to mental health compared to Western countries like the US. This study examines interactions about schizophrenia, a specific mental health diagnosis, on public social media (Facebook, Instagram, and Twitter) in two different languages, English and Korean, to determine how mental health advocacy and its opposition are expressed on social media. After delineation of a set of keywords for retrieval of content about schizophrenia, three months’ worth of social media posts were collected; a subset of these posts was then analyzed qualitatively using constant comparing with a proposed model describing online mental heath advocacy based on existing literature. Various expressions of light mental health advocacy, such as sharing facts about schizophrenia, and strong advocacy, showcasing offline engagement, were found in English posts; many of these expressions were however absent from the analyzed Korean posts that heavily featured jokes, insults, and criticisms. These findings were used to train machine learning classifiers to detect advocacy and counter-advocacy. The classifiers confirmed the predominance of counter-advocacy in Korean posts compared to important advocacy prevalence in English posts. These findings informed culturally sensitive recommendations for social media uses by mental health advocates and implications for international social media studies in human-computer interaction.Item aiDance: A Non-Invasive Approach in Designing AI-Based Feedback for Ballet Assessment and Learning(2021-12) Trajkova, Milka; Cafaro, Francesco; Bolchini, Davide; Dombrowski, Lynn; Fusco, Judi; Hickey, Daniel; Magerko, Brian; Toenjes, JohnSince its codified genesis in the 18th century, ballet training has largely been unchanged: it relies on tools that lack adequate support for both dancers and teachers. In particular, providing effective augmented feedback remains challenging as it can be limited, not always provided at the proper time, and highly subjective as it depends on the visual experience of an instructor. Designing a ballet assessment and learning tool with the aim of achieving a meaningful educational experience is an interdisciplinary challenge due to the fine motor movements and patterns of the art form. My work examines how we can effectively augment ballet learning in three phases using mixedmethod approaches. First, through my past professional experience as a ballet dancer, I explore how the design and in-lab evaluation of augmented visual and verbal feedback can improve the technical performance for novices and experts via remote learning. Second, I investigate the learning and teaching challenges that currently exist in traditional in-person training environments for dancers and teachers. Furthermore, I study the current technology use, reasons for non-use, and derive design requirements for future use. Lastly, I focus on how we can design aiDance, an AI-based feedback tool that attempts to represent an affordable and non-invasive approach that augments teachers’ abilities to facilitate assessment in the 21st century and pirouette towards the enhancement of learning. With this empirical work, I present insights that inform the HCI community at the intersection of dance and design in addressing the first steps towards the standardization of motor learning feedback.Item ANFORA (AURAL NAVIGATION FLOWS ON RICH ARCHITECTURES)(Office of the Vice Chancellor for Research, 2012-04-13) Ghahari, Romisa R.; George-Palilonis, Jennifer; Bolchini, DavideExisting web applications make users focus their visual attention on the mobile device while browsing content and services on-the-go. To support eyes-free, mobile experiences, designers can minimize the in-teraction with the device by leveraging the auditory channel. Whereas acoustic interfaces have shown to be effective to reduce visual atten-tion, a perplexing challenge is designing aural information architec-tures typical of the web. To address this problem, we introduce Aural Navigation Flows on Rich Architectures (ANFORA), a novel design framework that transforms existing information architectures as linear, aural flows. We demonstrate our approach in ANFORAnews, a semi-aural mobile site designed to browse large collections of news stories. A study with frequent news readers (N=20) investigated the usability and navigation experience with ANFORAnews in a mobile setting. Aural flows are enjoyable, easy-to-use and appropriate for eyes-free, mobile contexts. Future work will optimize the mechanisms to customize con-tent and control the aural navigation.Item The audio/visual mismatch and the uncanny valley: an investigation using a mismatch in the human realism of facial and vocal aspects of stimuli(2011-03-16) Szerszen, Kevin A.; MacDorman, Karl F.; Faiola, Anthony; Bolchini, Davide; Lu, Amy ShirongEmpirical research on the uncanny valley has primarily been concerned with visual elements. The current study is intended to show how manipulating auditory variables of the stimuli affect participant’s ratings. The focus of research is to investigate whether an uncanny valley effect occurs when humans are exposed to stimuli that have an incongruity between auditory and visual aspects. Participants were exposed to sets of stimuli which are both congruent and incongruent in their levels of audio/visual humanness. Explicit measures were used to explore if a mismatch in the human realism of facial and vocal aspects produces an uncanny valley effect and attempt to explain a possible cause of this effect. Results indicate that an uncanny valley effect occurs when humans are exposed to stimuli that have an incongruity between auditory and visual aspects.Item Brand and usability in content-intensive websites(2014-07-11) Yang, Tao; Bolchini, Davide; Pfaff, Mark; MacDorman, Karl F.; Cox, Anthony D.Our connections to the digital world are invoked by brands, but the intersection of branding and interaction design is still an under-investigated area. Particularly, current websites are designed not only to support essential user tasks, but also to communicate an institution's intended brand values and traits. What we do not yet know, however, is which design factors affect which aspect of a brand. To demystify this issue, three sub-projects were conducted. The first project developed a systematic approach for evaluating the branding effectiveness of content-intensive websites (BREW). BREW gauges users' brand perceptions on four well-known branding constructs: brand as product, brand as organization, user image, and brand as person. It also provides rich guidelines for eBranding researchers in regard to planning and executing a user study and making improvement recommendations based on the study results. The second project offered a standardized perceived usability questionnaire entitled DEEP (design-oriented evaluation of perceived web usability). DEEP captures the perceived website usability on five design-oriented dimensions: content, information architecture, navigation, layout consistency, and visual guidance. While existing questionnaires assess more holistic concepts, such as ease-of-use and learnability, DEEP can more transparently reveal where the problem actually lies. Moreover, DEEP suggests that the two most critical and reliable usability dimensions are interface consistency and visual guidance. Capitalizing on the BREW approach and the findings from DEEP, a controlled experiment (N=261) was conducted by manipulating interface consistency and visual guidance of an anonymized university website to see how these variables may affect the university's image. Unexpectedly, consistency did not significantly predict brand image, while the effect of visual guidance on brand perception showed a remarkable gender difference. When visual guidance was significantly worsened, females became much less satisfied with the university in terms of brand as product (e.g., teaching and research quality) and user image (e.g., students' characteristics). In contrast, males' perceptions of the university's brand image stayed the same in most circumstances. The reason for this gender difference was revealed through a further path analysis and a follow-up interview, which inspired new research directions to unpack even more the nexus between branding and interaction design.Item Data-Driven Accountability: Examining and Reorienting the Mythologies of Data(2020-05) Verma, Nitya; Dombrowski, Lynn; Bolchini, Davide; Young, Alyson; Seybold, Peter; Voida, Amy; Muller, MichaelIn this work, I examine and design sociotechnical interventions for addressing limitations around data-driven accountability, particularly focusing on politically contentious and systemic social issues (i.e., police accountability). While organizations across sectors of society are scrambling to adopt data-driven technologies and practices, there are epistemological and ethical concerns around how data use influences decisionmaking and actionability. My work explores how stakeholders adopt and handle the challenges around being data-driven, advocating for ways HCI can mitigate such challenges. In this dissertation, I highlight three case studies that focus on data-driven, human-services organizations, which work with at-risk and marginalized populations. First, I examine the tools and practices of nonprofit workers and how they experience the mythologies associated with data use in their work. Second, I investigate how police officers are adopting data-driven technologies and practices, which highlights the challenges police contend with in addressing social criticisms around police accountability and marginalization. Finally, I conducted a case study with multiple stakeholders around police accountability to understand how systemic biases and politically charged spaces perceive and utilize data, as well as to develop the design space around how alternative futures of being data-driven could support more robust and inclusive accountability. I examine how participants situate the concepts of power, bias, and truth in the data-driven practices and technologies used by and around the police. With this empirical work, I present insights that inform the HCI community at the intersection of data design, practice, and policies in addressing systemic social issues.Item Data-To-Question Generation Using Deep Learning(IEEE, 2023) Koshy, Nicole; Dixit, Anshuman; Jadhav, Siddhi Shrikant; Penmatsa, Arun V.; Samanthapudi, Sagar V.; Kumar, Mothi Gowtham Asok; Anuyah, Sydney Oghenetega; Vemula, Gourav; Herzog, Patricia Snell; Bolchini, DavideMany publicly available datasets exist that can provide factual answers to a wide range of questions that benefit the public. Indeed, datasets created by governmental and non- governmental organizations often have a mandate to share data with the public. However, these datasets are often underutilized by knowledge workers due to the cumbersome amount of expertise and embedded implicit information needed for everyday users to access, analyze, and utilize their information. To seek solutions to this problem, this paper discusses the design of an automated process for generating questions that provide insight into a dataset. Given a relational dataset, our prototype system architecture follows a five-step process from data extraction, cleaning, pre-processing, entity recognition using deep learning, and questions formulation. Through examples of our results, we show that the questions generated by our approach are similar and, in some cases, more accurate than the ones generated by an AI engine like ChatGPT, whose question outputs while more fluent, are often not true to the facts represented in the original data. We discuss key limitations of our approach and the work to be done to bring to life a fully generalized pipeline that can take any data set and automatically provide the user with factual questions that the data can answer.Item Data-To-Question Generation Using Deep Learning(IEEE, 2023-08) Koshy, Nicole Rachel; Dixit, Anshuman; Jadhav, Siddhi Shrikant; Penmatsa, Arun V.; Samanthapudi, Sagar V.; Kumar, Mothi Gowtham Ashok; Anuyah, Sydney Oghenetega; Vemula, Gourav; Herzog, Patricia Snell; Bolchini, Davide; Lilly Family School of PhilanthropyMany publicly available datasets exist that can provide factual answers to a wide range of questions that benefit the public. Indeed, datasets created by governmental and nongovernmental organizations often have a mandate to share data with the public. However, these datasets are often underutilized by knowledge workers due to the cumbersome amount of expertise and embedded implicit information needed for everyday users to access, analyze, and utilize their information. To seek solutions to this problem, this paper discusses the design of an automated process for generating questions that provide insight into a dataset. Given a relational dataset, our prototype system architecture follows a five-step process from data extraction, cleaning, pre-processing, entity recognition using deep learning, and questions formulation. Through examples of our results, we show that the questions generated by our approach are similar and, in some cases, more accurate than the ones generated by an AI engine like ChatGPT, whose question outputs while more fluent, are often not true to the facts represented in the original data. We discuss key limitations of our approach and the work to be done to bring to life a fully generalized pipeline that can take any data set and automatically provide the user with factual questions that the data can answer.