Humanizing Big Data: Making Sense of How Youth of Color Experience Personalized Educational Technologies

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Humanizing Big Data: Making Sense of How Youth of Color Experience Personalized Educational Technologies

Humanizing Big Data: Making Sense of How Youth of Color Experience Personalized Educational Technologies

Youth and children of color often are on the receiving end of poorly conceived technocratic solutions touted to improve their learning and close achievement gaps. Often referred to as personalized or student-centered learning solutions, these types of educational technologies are being increasingly adopted by public schools and districts within the broader context of corporate school reform efforts (Roberts-Mohoney, Means, & Garrison, 2016). Personalized educational technology and learning solutions are often removed or misaligned to broader curriculum efforts in schools (Bingham, et al., 2018) and emphasize the role of trackable data – often referred to as data driven decision-making - in shaping students’ learning experiences (cf Roberts-Mohoney et al., 2016). Moreover, personalized technologies are rarely designed in conversation with educators, parents, or families and for marginalized communities and innovation projects are often misaligned to their needs (Reich, Ito, MITS Team, 2017). In this paper I draw from a two-year ethnographic research study at an urban public high school that was part of an innovation network, to explore how educational technologies, from Google docs to proprietary predictive software, permeated youth’s lives. I pose the question, how did educational technologies shape the lives of students in a public high school within an innovation network? In doing so, I hope to illuminate the practical and everyday ways that youth of color from marginalized communities experience technology because it is important to humanize what big data can often obscure.

In a Web 2.0 paradigm, logics are applied to interpret preferences and make recommendations: in classrooms youth might engage with software that suggests books based on a student’s reading levels, previous selections, or assessment data. Politically, personalization allows districts to communicate that they value youth as learners and individuals within learning contexts. Practically, a shift towards “personalized” learning gives resource-starved districts a way to lessen costs and deal with increasing class sizes by outsourcing teaching and learning to online programs and pre-developed curricula (cf Staker & Horn, 2012). However, personalization engines do not consider, students’ histories in person (Holland & Lave, 2001) or factors beyond the screen like whether a student had a bad bus ride into school, or how to pivot when a student wants to embark on something new and adventurous, or allow for opportunities for students to ask questions and be in dialogue with a set of ideas. Ultimately these conceptions of ‘student-centered’ draw on a framework of content transfer and delivery, or a dressed-up banking model of education (Freire, 1993). By emphasizing preferences as personal, the personalized learning approach is rapidly becoming a new kind of standardization; laptops instead of books, online quizzes versus paper. These kinds of technologies espouse an instructionist approach to learning where technology is for content delivery versus a more constructionist approach to learning that nurtures youth to learn using a range of technological tools (Kafai, 2006; Papert, 1980).

The empirical data that I would draw from in this paper comes from a larger two-year multimodal ethnographic research study I conducted at an urban high school that I will refer to as Design School. The larger research study traced the identities, making, learning, and literacy practices, and experiences of youth, ages 14-16, who were navigating a newly designed high school in its inaugural years. The Design School sought to reimagine high school for students that offered a college readiness curriculum – but could not – for a variety of reasons – find their way into one of the specialized magnet schools in the district. Implementing asynchronous and personalized learning facilitated by Google Chromebooks, Google Educational suite, personalized learning software, and classroom management software, the Design School sought to offer students’ many avenues to be academically successful.

In this paper, I will revisit the experiences of six focal students- examining how their educational and academic lives were shaped by technologies they were required to use in the name of learning and academic advancement. Using integrative and cross-conceptual memos (Emerson, Fretz, & Shaw, 2011), I will explore how students personally experienced the aforementioned personalized learning technologies. I will offer insights into the challenges and concerns as well as the affordances these tools can have in shaping youth’s lives. I use these students’ experiences to provide a foil for a larger argument about the concerns and perils of engaging youth in using technologies but not creating space for them to be critical of the technologies or have choice. Students of color are constantly policed, surveilled, and documented, by the government, and now companies have access to all the minutia of their academic lives; information that was once private and limited to parents and teachers. Thus, it is crucial to humanize the face of educational technology solutions to offer more nuance to this debate. As the call suggests, “if left unattended children’s digital experiences will continue to diverge… while vulnerable and lower-income children experience the ill effects of an algorithmic world.” Therefore, it is imperative to understand the ways technology intimates itself in the lives of youth, particularly the most vulnerable among us.


Bingham, A. J., Pane, J. F., Steiner, E. D., & Hamilton, L. S. (2018). Ahead of the curve:Implementation challenges in personalized learning school models. Educational Policy, 32(3), 454-489.

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing ethnographic fieldnotes. Chicago:University of Chicago Press.

Freire, P. (1993). Pedagogy of the oppressed. New York: Continuum International Publishing Group.

Holland, D., & Lave, J. (2001). History in person: Enduring struggles, contentious practice, intimate identities. SAR Press.

Kafai, Y. B. (2006). Playing and making games for learning: Instructionist and constructionist perspectives for game studies. Games and culture, 1(1), 36-40.

Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic Books.

Reich, J., Ito, M., & Team, M. S. (2017). From Good Intentions to Real Outcomes. Connected Learning Alliance. Retrieved from:

Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing human capital development: Personalized learning technology and the corporatization of K-12 education. Journal of Education Policy, 31(4), 405-420.

Staker, H., & Horn, M. B. (2012). Classifying K-12 Blended Learning. Innosight Institute.


Veena Vasudevan is a Postdoctoral Fellow of Children and Family Learning at the American Museum of Natural History. Her research explores youth identities, learning, literacies, and making practices. Her past research includes several years working on designing games for learning with Scratch and MaKey MaKey as a member of the Scratch team at University of Pennsylvania’s Graduate School of Education (Penn GSE). She designed and is currently teaching a course on computational thinking for Penn GSE’s online certificate program. She has spent over a decade working with urban school districts including New York and Philadelphia. Prior to her PhD she worked as the Director of Knowledge Sharing at the New York City Department of Education and also as a consultant to the New York State Education Department, helping to design online resources to facilitate sharing amongst city and state educators, leading the user experience design and implementation. She holds a Master of Public Administration (MPA) from the School of International and Public Affairs at Columbia University, and a PhD from University of Pennsylvania’s Graduate School of Education.

Postdoctoral Fellow



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