Education Innovation
Our Education Innovation research area includes publications on online learning at scale, educational technology (which is any technology that supports teaching and learning), curriculum and programming tools for computer science education, diversity and broadening participation in computer science the hiring and onboarding process at Google.
Recent Publications
Equitable student persistence in computing research through distributed career mentorship
Audrey Rorrer
Cori Grainger
Proceedings of 54th Technical Symposium on Computer Science Education (SIGCSE’23), ACM (2023)
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Google’s CS Research Mentorship Program (CSRMP) cultivates pursuit and persistence in the computing research trajectory for students from historically marginalized groups through virtual career mentorship from industry professionals, a peer community, and just-in-time resources. Since 2018, 287 Google mentors have engaged 1,018 students from 247 institutions in the U.S. and Canada. The program employs socioemotional support and advocacy to navigate systemic barriers by validating students’ intersectional identities in order to improve outcomes in core constructs for students: self-efficacy, sense of belonging, research skills, motivation to pursue graduate school and research careers, and intersectional capital. Evaluation outcomes from 400 matched respondents (68% response rate) indicate that CSRMP affects positive, statistically significant change in those constructs that largely persists across demographic subgroups. 80% aim to pursue computing research careers, and significantly fewer students are undecided about their future career. We were also able to identify disaggregated learnings: Black, Indigenous, and Latinx students are significantly less likely to submit to a research conference, and students from Historically Marginalized Groups (defined within) are significantly less likely to apply to a CS graduate program. We discuss key design elements of the program, how the findings are informing future iterations, and the potential for the model to scale.
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Growing an Inclusive Community of K-12 CS Education Researchers
Monica McGill
Proceedings of 54th Technical Symposium on Computer Science Education (SIGCSE’23), ACM (2023)
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A recent study found that there is a litany of unmet needs that are serving as barriers for the CS education research community to grow in depth and breadth, including ensuring that the community is representative of the teachers and students that are studied. Cultivating a diverse, equitable, inclusive, and accessible CSEd research community requires simultaneous bottom-up and top-down alignment on practice standards, professional development, and wellbeing for all constituents that is rooted in politicized trust and collective impact. For this position paper, we engaged in an expository writing process using a confirmatory and elucidating research design to contextualize quantitative and qualitative data reported from our previous study within related work. Our results indicate that there is a variety of researcher-centered, researcher-adjacent, and research-centered barriers in CS education that affect researchers’ practice, and personal and professional identities. These results were validated by findings from research in other fields, such as education, psychology, and organizational change. These findings highlight the need for intentional changes to be made, both top-down and bottom-up, to sustain and grow the CS education research community in a way that equitably supports the evolving needs of a diverse set of students as well as the diverse set of researchers who study interventions.
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Business Intelligence Career Master Plan
Danny Moncada
Eduardo Chavez
Packt (2023) (to appear)
Understanding Immersive Research Experiences that Build Community, Equity, and Inclusion
Audrey Rorrer
Breauna Spencer
Deborah Holmes
Cori Grainger
SIGCSE '21: Proceedings of the 2021 ACM SIGCSE Technical Symposium on Computer Science Education (2021)
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In this experience report, we describe the rationale and need for immersive research experiences (IREs) in computer science (CS) that are designed to foster an inclusive community that encourages pursuit of graduate education for undergraduate women. Google’s exploreCSR supports institutions across the US to execute IREs in computing throughout the academic year. We describe the program design and framework, the evaluation model, and present outcomes from two years of implementation across 29 institutions, with 1,983 (92% female) student participants collectively. The unique features of the program are that it aligns goals, measurements, and best practices across a national network of hands-on, localized IREs, resulting in peer communities and a sizable sample of undergraduates who identify as women and/or African-American/Black, American Indian/Alaska Native/Native American, Hispanic/Latinx, and/or Native Hawaiian/Pacific Islander (AAHN). We discuss recommendations for effective IRE programming based on our evaluation and the features found to be particularly salient for AAHN women. The contribution of this work is in describing how a national initiative for IREs builds community and creates conditions known to support persistence of women in computer science.
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Ars gratia retium: Understanding How Artificial Neural Networks Learn To Emulate Art
Algorithmic and Aesthetic Literacy: Emerging Transdisciplinary Explorations for the Digital Age, Verlag Barbara Budrich, Stauffenbergstr. 7
51379 Leverkusen
Germany (2021)
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Chapter of a book "Algorithmic and Aesthetic Literacy Matter" by Lydia Schulze Heuling (Bergen, Norway) and Christian Fink (Flensburg, Germany).
Discusses the process of generating art through artificial neural networks on a high level with an intended audience of teachers and teacher-training institutions. The context is the connection between algorithmic and aesthetic literacy. Contains a high-level introduction of artificial neural networks with some historical perspective, an exposition of the relationship of computer algorithms and aesthetic expression especially where used in teaching programming, and an overview of DeepDream and Generative Neural Networks with some examples. It ends with a discussion of uses of generative neural networks as artistic tools and their perspective in education.
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College from home during COVID-19: A mixed-methods study of heterogeneous experiences
Margaret E. Morris
Kevin S. Kuehn
Jennifer Brown
Paula S. Nurius
Han Zhang
Yasaman S. Sefidgar
Xuhai Xu
Eve A. Riskin
Anind K. Dey
Jennifer C. Mankoff
Proceedings of the ACM on Human Computer Interaction (PACM HCI), ACM (2021)
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This mixed-method study examined the experiences of college students during the COVID-19 pandemic through surveys, experience sampling data collected over two academic quarters (Spring 2019 n1 = 253; Spring 2020 n2 = 147), and semi-structured interviews with 27 undergraduate students. There were no marked changes in mean levels of depressive symptoms, anxiety, stress, or loneliness between 2019 and 2020, or over the course of the Spring 2020 term. Students in both the 2019 and 2020 cohort who indicated psychosocial vulnerability at the initial assessment showed worse psychosocial functioning throughout the entire Spring term relative to other students. However, rates of distress increased faster in 2020 than in 2019 for these individuals. Across individuals, homogeneity of variance tests and multi-level models revealed significant heterogeneity, suggesting the need to examine not just means but the variations in individuals’ experiences. Thematic analysis of interviews characterizes these varied experiences, describing the contexts for students' challenges and strategies. This analysis highlights the interweaving of psychosocial and academic distress: Challenges such as isolation from peers, lack of interactivity with instructors, and difficulty adjusting to family needs had both an emotional and academic toll. Strategies for adjusting to this new context included initiating remote study and hangout sessions with peers, as well as self-learning. In these and other strategies, students used technologies in different ways and for different purposes than they had previously. Supporting qualitative insight about adaptive responses were quantitative findings that students who used more problem-focused forms of coping reported fewer mental health symptoms over the course of the pandemic, even though they perceived their stress as more severe. These findings underline the need for interventions oriented towards problem-focused coping and suggest opportunities for peer role modeling.
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