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Browsing by Author "Soto, Hortensia, committee member"

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    Exploring women of color's expressions of mathematical identity: the role of institutional resources and mathematical values
    (Colorado State University. Libraries, 2023) Street, Ciera, author; Ellis Hagman, Jessica, advisor; Soto, Hortensia, committee member; Arnold, Elizabeth, committee member; Most, David, committee member
    There is a persistent and growing global call to examine, challenge, and transform exclusionary structures and systems within mathematics education (Laursen & Austin, 2020; Reinholz et al., 2019; Thomas & Drake, 2016; Wagner et al., 2020). An important component of this call examines students' mathematical identity. While a growing body of work considers how students' social identities interplay with their mathematical identity (e.g., Akin et al., 2022; English-Clarke et al., 2012), few studies consider mathematical identity at the intersection of gender and race (Ibourk et al., 2022; Leyva, 2016; 2021). This dissertation study explores undergraduate women of color's expressions of mathematical identity and the institutional structures and ideologies that influence these expressions. Following a three-paper model, each paper utilizes critical theories and an intersectional lens to recognize the gendered and racialized context of higher education mathematical spaces and the ways these discourses influence women of color's mathematical identity. The first paper employs large-scale quantitative and qualitative data from a national survey on students' undergraduate calculus experiences to explore women of color's expressions of mathematical identity. Informed by Data Feminism, I use a cluster analysis to group women of color survey respondents based on four subdomains of mathematical identity and contextualize each group using qualitative survey responses. The second paper draws from Nasir's (2011) material and relational identity resources to examine the institutional resources available and accessible to undergraduate women of color to support their mathematical identity. Results from participant interviews indicate various supportive identity resources, such as peer relationships and student support programs. The results also describe unavailable, inaccessible, or detrimental identity resources, such as the lack of representation within the mathematics faculty and an exclusionary mathematics community. Using a sociopolitical lens, the third paper discusses the sociohistorical background of white, patriarchal mathematical values and the ways these values create inequities in undergraduate mathematical spaces. Interviews with participants suggest a clear misalignment between these sociohistorical mathematical values and women of color's mathematical and mathematics education values. Together, these three papers emphasize within-group differences among women of color's mathematical identity and the different ways material, relational, and ideological resources can support or hinder women of color's mathematical identities. I conclude this dissertation study by illustrating connections across the three papers. I also provide implications for teaching, policy, and research to challenge exclusionary mathematical systems and support women of color's mathematical identity.
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    Intentional microgesture recognition for extended human-computer interaction
    (Colorado State University. Libraries, 2023) Kandoi, Chirag, author; Blanchard, Nathaniel, advisor; Krishnaswamy, Nikhil, advisor; Soto, Hortensia, committee member
    As extended reality becomes more ubiquitous, people will more frequently interact with computer systems using gestures instead of peripheral devices. However, previous works have shown that using traditional gestures (pointing, swiping, etc.) in mid-air causes fatigue, rendering them largely unsuitable for long-term use. Some of the same researchers have promoted "microgestures"---smaller gestures requiring less gross motion---as a solution, but to date there is no dataset of intentional microgestures available to train computer vision algorithms for use in downstream interactions with computer systems such as agents deployed on XR headsets. As a step toward addressing this challenge, I present a novel video dataset of microgestures, classification results from a variety of ML models showcasing the feasibility (and difficulty) of detecting these fine-grained movements, and discuss the challenges in developing robust recognition of microgestures for human-computer interaction.