Skip to main content
SearchLogin or Signup

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

Published onJun 29, 2021
Humanizing Big Data: Making Sense of How Youth of Color Experience Personalized Educational Technologies


In a Web 2.0 paradigm, algorithms are applied to interpret personal preferences and make recommendations in educational and other youth settings. While this may allow educators to make claims of personalization or individualized learning these approaches often neglect a host of factors that might influence student learning beyond simple choice. In this paper, I pose the question, how do educational technologies shape the learning lives of students? Solutions that were marginally effective in the world of business and commerce should not impact millions of children, whose districts and leadership accept funding and parameters not out of choice but of necessity. We must instead think critically about how we might learn from the creative and agentive ways make, create, share, and produce with technologies and think about how we can shift schools away from emphasizing students’ roles as consumers of technology to creating, making, and producing with technologies.

Key Findings

  • Personalized educational technologies can reproduce and remedy educational inequities

  • Personalized educational technologies can support students with standards aligned content and assessments, freeing teachers up to focus on individual students and for students to have more autonomy over their learning

  • Personalized educational technologies can also - through their design features reproduce educational inequities by conceptualizing learning as content acquisition and creating a culture of answer-getting.

  • Algorithms can over simplify what it means for personalized technologies to really be personal and rooted in youth’s lived experiences.

  • Personalized technologies continue to reify existing systemic inequities; where youth of color are consistently using technology in reductive ways.

“Why do we got to do this? Zaire looked up at me with pained eyes as I walk over to see what he is working on that day. He has opened up Edgenuity to work on a Humanities assignment about myths. “I don’t want to read about myths!” I recognize the stressful relationship he has with complex text and the isolation of sitting in front of a computer to learn something that doesn’t seem salient. We work slowly, reading, discussing the content, and eventually it starts to click.

November 6, 2015

Students like Zaire are often on the receiving end of poorly conceived technological solutions touted to improve their learning and close achievement gaps. In this paper I draw from a two-year ethnographic research study at an urban public high school, the Design School, to explore how students engaged with personalized educational technologies. Personalized or student-centered learning solutions are educational technologies that are being increasingly adopted by public schools and districts within the broader context of corporate school reform efforts (Roberts-Mohoney, Means, & Garrison, 2016). Learning solutions that emphasize personalization are often misaligned with broader curriculum efforts in schools (Bingham et al., 2018) and emphasize the role of trackable data – referred to as data driven decision-making - in shaping students’ learning experiences (Roberts-Mohoney et al., 2016). They are rarely designed in conversation with students, educators, or parents, and are often misaligned to the needs of youth from marginalized communities (Reich et al., 2017).

These educational technology platforms apply logics to interpret personal preferences and make recommendations: in classrooms youth might engage with software that suggests books based on students’ 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-packaged curricula (Basham et al., 2016; Staker & Horn, 2012). However, personalization engines do not consider students’ histories in person (Holland & Lave, 2001) or factors beyond the screen –whether a student had a bad bus ride into school, how to pivot when a student wants to embark on something new and adventurous, or how to allow for opportunities for students to engage in dialogic learning experiences. Ultimately these conceptions of ‘student-centered’ learning draw on a framework of content transfer and delivery, or a dressed-up version of Freire’s banking model of education (Freire, 1993). Personalized learning solutions are rapidly becoming a new kind of standardization; laptops instead of books, online quizzes versus paper. They do not represent an inherent shift in how students learn so much as a shift in the medium for delivery. Educational technologies like these espouse an instructionist approach to learning where technology is used for content delivery–learning from technology –versus a more constructionist approach that encourages youth to learn through creating with technology (Kafai, 2006; Papert, 1980).

In this paper, I draw from the Design School students’ experiences to provide a foil for a larger argument about how personalized learning can reify gross inequities that already persist in public education. I pose the question, how do educational technologies shape the learning lives of students? Through an examination of one personalized learning technology, 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.

Conceptual Framing

In the United States education has long reinforced social and economic hierarchies, limiting the quality and breadth of academic learning experiences in schools for youth of color1. Specifically, Black and Latinx youth have been relegated to second class citizenship, tracked into technical and task-oriented careers that would benefit large scale employers but not allow for the social mobility and change that schools promise (Anderson, 1988; Sanchez, 1993). Moreover, the educational system has been historically structured to silence, oppress, and demand compliance from students youth in urban public schools (Anyon, 1981; Ferguson, 2020; Fine, 1991) resulting in educational environments that are often uncaring (Valenzuela, 1999) and punitive (Ferguson, 2020), and that disproportionately discipline students rather than encouraging their academic achievement (Thomas & Stevenson, 2009). Moreover, schools in the United States have largely been used to reinforce differences in service of the political economy (Holt, 1999; Omi & Winant, 1999). In our contemporary moment, particularly amidst a pandemic, the gross inequalities that exist in our educational systems are laid even more bare.

There are a variety of ways in which students of color experience vast educational inequities. The design of curriculum, pedagogical practices, and the very nature of how knowledge is perceived are factors that shape students’ academic lives in their everyday school experiences. Nieto (1999) argues that learning for many students of color gets reduced to “reproduction of socially sanctioned knowledge" (p.3): students are positioned as empty vessels that require filling because they don’t have the currency required to navigate school success, which implicitly justifies the banking model of education (Freire, 1993). Others have documented the lack of recognition within formal education of students’ funds of knowledge (Moll et al., 1992), out-of-school literacies (Vaughan, 2020; Hull & Schultz, 2001), and their capacities for learning and leadership. Black and Latinx students are also more likely to have more inexperienced educators, less academic technology and materials, and inadequate school facilities (Darling-Hammond, 2010), and these factors are further exacerbated by a culture of low expectations, and an emphasis on standardized testing over authentic learning (Gadsden et al., 1996; Nieto, 1999).

One of the persistent equity challenges is in how students engage in educational technology use in school. Scholarship on the digital divide attends to infrastructure (e.g. broadband connectivity) and access (owning computers and mobile devices, opportunities to use them in school, etc.), and increasingly now also to deciphering how young people engage with technology in educational environments, as well as the quality and diversity of students’ learning experiences with technology. Students in well-resourced schools have more access to sophisticated technologies, educational tools, increased teacher expertise, and professional facilities, rendering their experiences different and varied from those in under-resourced schools (Dolan, 2016; Warschauer & Matuchniak, 2010; Warschauer et al., 2018). Well-resourced schools can offer better and more varied educational opportunities that are built around more than simply passing standardized exams. Instructional technology in low-SES schools focus disproportionately on drill and practice, memorization, and preparation for standardized tests, positioning students as consumers of technology versus producers (Dolan, 2016). While access to and availability of technology has increased, the opportunity to use technology to facilitate creativity, critical thinking, and problem solving still eludes students in the most marginalized schools and communities (Rowsell et al., 2017; Warschauer et al., 2018). In his examination of adolescents’ technology use in three different middle schools, Rafalow (2020) found that White students were generally encouraged and even praised for inviting their technology practices into the classroom, while Black and Latinx students were discouraged or worse, disciplined for doing the same. Thus, even with access to potentially engaging and rich instructional technologies, students are not able to leverage these technologies in equitable ways (Rafalow, 2020). The digital divide is rightfully seen as an equity issue that goes beyond physical resources and infrastructure to reveal the ways in which the use of instructional technologies also reifies systemic educational inequities, with youth of color disproportionately being affected.

What we also know is that when given access and opportunities young people, including youth of color, engage in creative and critical production in a range of learning contexts, cultivate and nurture deep technical skills, produce a range of sophisticated multimodal compositions, and engage in participatory cultures (Ito et al., 2010; Ito et al., 2013; Ito et al., 2020; Kafai & Peppler, 2011; Moje, 2000; Pinkard et al., 2020). There are many educational learning technologies that have been developed specifically for tinkering, play, exploration, and learning, conceived through a Constructionist approach to learning, engaging students through design and problem solving (Kafai, 2006; Resnick & Rosenbaum, 2013; Vossoughi & Bevan, 2014). These applications of technology are educational in that they facilitate youth’s creative endeavors and fuel more creativity, questioning, and in many cases educational participation. Alternatively, the instructionist approaches of much of the personalization technology simply attempt to impart standards-aligned content, but are less concerned with engaging students in making, creating, and practicing with technology. The use of technology in classrooms, particularly under-resourced schools, still reflects a very staid vision of school as a site for content acquisition (Dolan, 2016; Rafalow, 2020; Warschauer et al., 2018). This technocratic conceptualization of education suggests that simple proximity to technology will have implications for youth’s learning, when in reality it is not just access to technology but the opportunities to engage creatively and playfully that have the potential to be transformative (Warschauer et al., 2018).


Site and Context

The Design School, located in a large metropolis in the Northeast, opened in the Fall of 2014, during the maker movement zeitgeist sweeping the educational reform landscape which sought support students in cultivating identities as makers and participate in maker-oriented educational activities (Honey & Kanter, 2013). The inaugural class had 99 students at the start of the school year. The student body was 82.8% African American, 14.1% Latino, 1% White, 1% Asian and 1% Other. Students with disabilities made up 13.1% of the student population and 100% of the students were economically disadvantaged. Due to attrition the first school year ended with 89 students. In its second year, the demographics mirrored the year prior, while the number of students increased, as the school added a new group of freshman, bringing the total to 172 students.

The founding school principal, Mr. Gilmore, was passionate about innovating on facets of teaching and learning that he felt had not worked in his nine years as a history teacher in the district: 1) Student-centered learning: he wanted school to prepare his students for the real world by encouraging independence; 2) Asynchronous learning: he wanted students to be able to move at their own pace; 3) Competency-Based Grading: he wanted their work to be rooted in real-world or ‘wicked’ problems and move away from what he often likened to ‘learning as worksheets’ by focusing on evidence-based measures of understanding. To facilitate students’ independence and self-paced learning, the school adopted a 1:1 laptop program, leveraging fairly inexpensive Google Chromebook laptops for each student. Beyond that he and his small new staff were committed to encouraging youth leadership, employing restorative justice over antiquated and over-used disciplinary measures, and supporting students’ social-emotional development and growth.

While rich in an energetic staff and vision, the Design School was, like most public schools in the district, lacking in substantial resources. The school itself was co-located with another new high school in the same ‘innovation’ network. Both schools inhabited one floor in a former elementary building that had been shuttered years prior, creating an odd juxtaposition – tall high school students navigating through a school originally built for children under ten. A hodgepodge of desks, tables, and chairs peppered the classrooms in the Design School, which had been collected by Mr. Gilmore from other high schools that had closed in the district.

In the first year (2014) the curriculum was designed and curated by the school’s five teachers, and was available via Google Classrooms. In addition to Chromebooks, two of the seven classrooms were outfitted with outdated iMacs that lacked IT infrastructure;–no unique user accounts, logins, or directories to save independent work. If students created a movie file they could save it, but with no guarantee that it would be there the next day. There were also no additive technologies like microphones, headphones, or other tools that would have allowed students to engage in creative production in a fully immersive way. The software was out-of-date and there was no comprehensive policy to maintain the machines. Nor was there any formal training for students (especially in the first year) on how to use any of the technology. There were other restrictions – YouTube and other social media sites were blocked –although students eventually figured out how to get around the district firewalls–and cell phones and personal devices went through a range of policies from totally banned to eventually being accepted in classroom spaces. None of the classrooms had projector screens so slides, films, and other curriculum materials were projected onto small wall-mounted whiteboards and in some classrooms, the wall. Some rooms didn’t have permanent projectors, so floating projectors were wheeled in and out of the principal’s office on carts that had also been sourced from closed schools.

During the school’s second year, the administration and staff (both new and existing) began working on developing more systems and structures to support their model of asynchronous, student-centered, and competency-based learning. The pedagogical structure for teaching and learning in the school's second year was comprised of daily action plans that students used to set mini-goals in service of larger curricular goals, engaging in self-driven content via Edgenuity or Google Classrooms, intermittent mini-lessons that occurred in small groups, and one-on-one check-ins with teachers that students could schedule. Within the core disciplines (humanities, science, mathematics) students worked towards larger performance tasks e.g. the production of an original myth in Humanities. The Design School teachers collaborated across grades to select units and content they felt would align to their curricular goals and the larger themes they wanted to impart (e.g. identity, conflict, etc.).


Edgenuity is a web-based platform that school districts have adopted for a range of uses including credit recovery, personalization, and increasingly, remote instruction (Edgenuity, n.d.; Farmer, 2016; Llewllyn, 2019). The platform offers a slate of standards-aligned courses(linked to either the Common Core State Standards or state-specific standardized assessments. Edgenuity courses leverage video lectures, readings, formative assessments like quizzes (multiple choice or open-ended), and more summative assessments like exams.

Edgenuity leverages mastery approaches to learning in which a demonstration of skills and conceptual knowledge means a student can move to the next big topic. As such, the platform is designed to introduce topics using videos, short readings, and other multimedia formats, and then offers formative assessments like multiple choice or open-ended quizzes within each lesson. These assessments utilize keyword grading (Edgenuity Help Center, n.d.; Smith, 2020) which requires students to use specific words to get more than 0%. There are a range of types of assignments and scenarios, but most of the formative assessments require the use of at least one key word to get a score of 100%. The other elements of content delivery are customizable and teachers can track students’ progress and evaluate whether they have met sufficient expectations to move on, or if they need to revisit the same content (Edgenuity, n.d., Eddy & Ballenger, 2016).

Edgenuity’s model is didactic and does not facilitate dialogue. For example, there is no peer to peer engagement or even immediate feedback. Instead, students individually engage with content, respond to prompts, and receive automated grades from the system for interim assessments.Teacher-produced grades are received for more culminating assignments, but, Edgenuity also makes it possible for teachers to leave more (if not all) the grading in a course to the system (Edgenuity Help Center, n.d.). Moreover, because of the mastery approach, students have to return to the content to receive credit on formative and summative assessments, but it’s not explicit how (if at all) the platform encourages critical thinking or problem solving.

The Design School adopted Edgenuity to facilitate the type of asynchronous and self-paced learning that was critical to their model. Students’ assignments on Edgenuity for mathematics and reading were informed by their scores on computerized adaptive tests called the Measures of Academic Progress (or MAP) that they took at the beginning of the 2015 school year. The scores were then used to create an individualized learning plan (ILP) for students that would be reflected in Edgenuity’s MyPath, a “supplemental program that offers data-driven differentiated instruction for math and reading” (Edgenuity, 2014). The Design School adopted MyPath to help students “catch up in places they were behind and get ahead in places where they were strong” (Student facing artifact, 20152). In addition, the school adopted the Keystone Biology curriculum from Edgenuity to supplement classroom instruction and help students with state assessments. The school’s offline curriculum wraparound was intended to bolster the online technologies like Google classrooms and Edgenuity, and to give students more structure. As the students’ stories below suggest, this was a work in progress that needed more refinement and attention to ensure that students felt connected to their learning experiences and rooted in a set of big ideas.

Positionality and Context

I came to the Design School as a member of a design-based project that was examining the shifting nature of students’ literacy practices within three interdisciplinary labs that were part of the school’s attempt to reimagine traditional urban public education. In addition to supporting the ethnographic research team, I was also embarking on my dissertation data collection, an educational ethnography that sought to understand the lived experiences of students at a maker-oriented high school. I particularly wanted to focus on how learning that was asynchronous, student-centered, and maker-oriented might shift how students engaged with school. As I spent time with students and explored the ethnographic context, my focus shifted to supporting students in developing three youth-led spaces– a film club, a dance team, and a youth empowerment group–while also continuing to center their learning and literacy. Working with students in these out-of-school spaces of their own making allowed me to get to know them and observe their creativity, leadership, and technological savvy. I was also able to inhabit school classrooms and work closely with students on their academic work.

In my research, I wrote (Clifford & Marcus, 1986) and pictured (Ruby, 2000) culture by collecting a range of multimodal ethnographic data between 2014-2016, including ethnographic field notes, photographs, short films, audio recordings as well as interviews with students and staff. I also collected student artifacts and school materials (e.g. announcements, student memos, assignments). The students in my study were 13-15 when the study began and 16-18 years old when the study concluded. In my study, I had six focal students, and another ten students who I worked closely with over the course of two years both in the youth-led spaces and in other educational and out-of-school contexts. In this paper, I draw from ten students’ experiences, including three of the six focal students in the original study (see Table 1 below).

I primarily analyzed specific questions from students’ transcribed interviews3 about perceptions on teaching, learning, and educational technology. I brought students’ interviews into conversation with field notes that highlighted students’ participation with educational and learning technologies (hardware and software) across both years of the study, as well as memos I had written while I was in the field. Ultimately, I utilized integrative and cross-conceptual memos (Emerson et al., 2011) to understand how educational and academic lives were shaped by technologies they were required to use in the name of learning and academic advancement. This paper hones in on students’ experiences with Edgenuity, which was implemented in their second year. These analyses reveal both challenges and concerns as well as the affordances educational web applications can have in shaping youth’s lives.

Table 1. Students




(at start of study)


Youth-Led Space

Out-of-School/Outside of Academic Interests and Passions




Youth empowerment

*group leader, focal student

Writing novels, graphic design, video design, music



African American

Film club

Writing and performing music, football team



African American

Dance team

*captain, focal student

Writing novels, singing, poetry club



African American

Dance team

Dance, cheerleading, doing hair



Latinx, Dominican

Youth empowerment

*group leader, focal student




Youth empowerment club

Cambodian culture, cooking




Youth empowerment club

Music, anime


The Edgenuity platform posed several challenges. A number of students did not feel like they were learning anything – the experience felt like answer-getting and work completion., Some students felt bored and demotivated by the monotony of such a system, and others were frustrated that their actual knowledge and understanding was not reflected by their online program of study. There were students for whom asynchronous and online learning was effective because it reinforced the independence they craved, but even then the actual system did not deliver an adequate learning experience. In the subsequent sections below, I draw from conversations with students to offer insight into these challenges related to personalized learning technologies.

Work Completion Over Learning

Tighe, a student who I worked with through the youth-led film club, was a student for whom Edgenuity felt totally disconnected from his vision or expectation for school. He was passionate about music, about football, and fitness. He was very close to his family and maintained a handful of strong relationships, including some at the Design School. As a tall, quiet, Black boy who would often physically locate himself on the edges of the classroom, he appeared often to be too cool for school, but as time went on, he revealed himself to be a student who derived deep value from active and embodied learning experiences. Early on he was skeptical of the Design School’s approach to learning: “this whole computer thing, working on computer and laptops is new for me still, even though I’ve been here for two years, I still can’t – I can’t do it; I can’t work with them.” When we started unraveling what specifically was challenging about using technology Tighe offered, “coz it’s like I’m cheating myself when maybe don’t test my mind… I can just – they give us 60 minutes to do a whole 10 question test - in 60 minutes, and all I gotta do is go on Google and check, it is gonna be there [the answers] and that’s not right. Anybody could pass like that.” Tighe was referring to the multiple-choice responses on the quizzes or assessments peppered throughout the digital curriculum in Edgenuity. He explained further, “Yeah, it’s the fact that I’m cheating myself out of my own education.” Tighe was resistant to learning with computers. He didn’t see himself or claim the identity as a ‘computer’ person, and learning with tools like Edgenuity felt disingenuous to what he thought learning should and could be.

During the same conversation, Tighe explained how his digital humanities lab class was a place where his use of technology was rewarding, explaining that his teacher, Mr. Caulfield, “was teaching me things; I never knew about a camera, or I never knew nothing about iMovie, I never knew how to work it, so it was just like- it was fascinating how you can do these things with a computer.” In contrast to how he felt about the Edgenuity experience, using technology to produce new content and to engage in exploration and creation was rewarding. His inclination toward the creative and performative was evident from the first time I saw his spiral notebook of raps and rhymes, and would later be evident during our work together in film club and via the student-led school talent show. Tighe embraced opportunities to take action, to try things out, and to participate in embodied learning experiences.

Charles shared a similar sentiment to Tighe, explaining that “I think only thing is just to do this and do that Edgenuity, and then I don’t feel like I’m learning anything. All I’m doing is just taking notes and taking fake quizzes.” Charles reflected that he wished high school was like the movies he watched where “teachers tell us to do the work instead of just assigning it, like force us to do it.” As we chatted, he said of the online learning experiences, “just like- just teach us instead of just having the computer as teachers.” In the early stages of learning to work with Edgenuity, Charles and many other students expressed frustration that the teachers and the school weren’t teaching. Charles did not have any issues navigating online applications or using a computer to complete assignments, but he did lament what felt like a negative shift– from learning with and from people, to learning, as he suggested, from the computer.

Charles was an attentive student. He had up and down days but generally kept plodding along because he felt a sense of responsibility to graduate and do well. His aspirations to become a chef and his love for classic Cambodian oldies did not factor into his everyday work at school, but they certainly peppered his technology use. A quick scan of his Instagram live or his feed revealed all of the food he sampled and classic musicians he reminisced about in his free time. Charles’ admission that he wished teachers would just teach is one that many students expressed in the first couple years at the Design School. This student discontent revealed a tension between the school principal’s vision for cultivating independence and the students’ re-learning of what it meant to engage in a process of learning at the school. In this case however, Edgenuity as a resource to facilitate the vision for independent, asynchronous, and task-driven learning did not deliver.

Charles, Tighe, and many other students felt like they were cheating, because it was too easy to look up answers and complete formative assessments like quizzes. While the performance tasks that culminated at the end of units were not things students could Google (in theory,) it’s clear that the processes of learning were facilitated by a system that made students feel like they were simply going through the motions. The implicit design of technologies like Edgenuity suggest that knowledge acquisition or ‘answer getting’ is the ultimate result. This is the banking model of education (Freire, 1993) with a different affective experience – online quizzes instead of a sheaf of worksheets that require completion. Tighe, Charles, and others completed their work but only for the sake of finishing, not because they were challenged, inspired, or interested in where the content journey was meant to take them. Moreover, the Edgenuity design feature that Tighe critiques, keyword grading, directly contributed to their feeling that they were doing busy work, not real learning.

Where they and other students were able to shine and make the work their own was in the performance tasks: Tighe wrote about music, and Charles focused on the Cambodian revolution for a culminating essay in humanities, allowing him to explore his cultural history. However, the system’s delivery of the ‘standards-aligned’ content did not facilitate this creativity or customization, and it did not always feel ‘real.’

Style Eclipses Connection in Student-Centered Learning

There were many students at the Design School who embraced the asynchronous and student-centered pedagogical approach to learning. A decentralized approach to teaching and learning was effective for these students because it gave them a sense of personal responsibility.

Ruby started attending the Design School three weeks into the first school year. She had been on the waitlist but as the school experienced attrition in the early weeks, the principal had called her and recruited her from the waitlist by, as she recounted it, speaking to her “egotistical side” by conveying that the Design School was a place she could pursue any dream she had. Inspired by his vision she came to visit, and immediately felt connected to the space and to the school’s novel pedagogical approach.

Ruby described her previous educational experiences as frustrating, especially when she felt that teachers were ‘hovering over’ her. She appreciated that the Design School’s model of asynchronous learning and leveraging of technology gave her “a lot of responsibility”.  However, she also offered, “the thing about Edgenuity is it’s very dull.” She went on to say that she wished there was dialogue or real-time opportunities to make a connection with the content providers:

I wish they [the people in the online lessons] were actually there, like you know there’s just videos of them, and like they put them on there and like they are talking and explaining and stuff. But like what if they were like actually like just Skyping and we actually have our conversation, like we all have our individual teacher which sounds crazy like there’s a lot of teachers. But it like if we were like to do that and be able to actually talk to them like, “Hey, I don’t get this; could you probably explain this again?” And he be like – and like the dude or the girl would be like, “Yeah, I could explain this again, or do you want me to just say like –” you know stuff like that I think that would be like way better. But you know you can’t always do what you want.

Ruby is describing the dialogue that happens when you can be with educators in real-time. She wanted to be able to ask a question, pursue a line of inquiry, or just clarify things in-the-moment. In other words, what was missing were opportunities to engage in dialogic learning (Freire, 1993). When opportunities to engage in critical conversations that help students explicate their understanding and practice critical thinking are unavailable, even carefully curated content can be reduced to answer-getting.

Ruby was fiercely independent. She embraced the culture of the Design School that emphasized students’ agency and appreciated being left to do her own thing. At times that model worked for her but other times it allowed her, and many other students who were seen as leaders at the school, to stray far away. There were long periods of time where these students weren’t turning in lots of work or even showing up in class. Even though the wrap-around curriculum was essential in rooting students, they were still getting lost in an environment of click-through text and videos.

Systemic Inequalities are Exacerbated in STEM Learning

Math Interrupted. At the Design School, mathematics went through some very substantive challenges. During the school’s first year, their first math teacher quit three months into the school year, leaving 90 students with no math instructor. He was unhappy with the school’s approach and felt that he was engaged more as a disciplinarian than an instructor. A second teacher, with no prior teaching experience, was hired in January 2015 and quit after just two weeks. Finally, they found a suitable replacement in mid-March, Ms. Capshaw. She stayed on until the fall of the following school year, only to leave mid-year due to personal injury and her own desire to pursue a career in leadership. Finally, in the school’s third year (Fall 2016), they found a mathematics teacher who was embraced by students and was instructionally strong. However, until this point, especially for students who were part of the inaugural class, math instruction was completely disrupted. Students were forced to rely on other teachers to mediate the online instruction while the school scrambled to keep the position staffed.

Many students’ anxieties stemmed from their disrupted math education, with equal frustration arising from learning math via online modules and not working with teachers to deepen their understanding. Aliya, a student who swung from being very engaged in class to losing focus and forward progress, was one of many students who lamented the challenges with Edgenuity, explaining that she missed instruction and being part of a larger class discussion. When we specifically discussed mathematics, she expressed understandable frustration:

Oh, we don’t have a math teacher, nobody have a math teacher in 9th grade or 10th grade. So when Mr. T left it’s just like everything just start goin’ behind, we doin’ this keystone thing or Edgenuity, and I feel like it don’t help nobody. Like Edgenuity is like everybody don’t do it and then is like – I don’t know. I just feel like we need a real teacher here for people to do math.

Aliya’s frustration is completely understandable – clicking through an application without any structure or guidance is not an optimal learning experience. Watching videos or watching others solve problems is not adequate math education. The study of mathematics requires dialogue with ideas, opportunities for critical thinking, decomposing problems, and specifically examining and engaging with concepts through practice. Moreover, Aliya was also generally frustrated with a system that made her feel isolated in a subject that was so central to high school success.

I offer a second illustration of the challenges online learning can create when students’ experiences do not line up with their expectations.. As a young Dominican woman determined to dream big, Anya’s decision to attend the Design School was driven by her hope that it would be a place of possibility. Inspired by the school’s vision to embrace students’ interests and passions, Anya started her freshman year with enthusiasm and openness, willing to try new things and taking steps to be integral to the school community. Early on, her diligence and commitment to her schoolwork and her willingness to participate in school activities caught the attention of the school leader and her teachers. By her sophomore year, she fully embraced the identity of one of the leading students in her class, which was reinforced when friends and acquaintances asked her for help and when teachers asked her to mentor younger or less experienced students. However, Anya’s anxieties around the future ran high. In her sophomore year, I was on my way out of Anya’s advisory class when I noticed she seemed a little sad. In an excerpt from a field note, her concerns about the future are intertwined with frustrations about the current learning technologies:

I walk over to Anya and ask if she’s okay. She responds, dragging out her words, “yeahhhh, why, miss?” I mention she seems a little off. She says,“I’m just stressed!” and explains she is thinking about college. “Miss, I have a 3.78 GPA - is that good?” I reassure her that it is. She insists,“no Miss I want your EXPEEERRRT TECH opinion.” I tell her that she should keep her grades up and that extra-curriculars matter. As we chat, it occurs to me students have just received their progress reports and so anxieties are high. She turns to her advisory teacher and asks her, “Miss Oswald what’d you get on your SAT?” Miss Oswald nonchalantly replies, “well uh let’s see I got a 690 in math and 6-something on my verbal, so whatever that is.” Then, Anya turns to me, “Miss what did you get?” I tell her my score and then explain “it’s different for you guys they are changing the expectations and the scores are different as well.” “Ohhh okay. Mi-iiisss- we have to take the PSATs NEXT year! NEXT YEAR! and I only answered FOUR questions on this math exam FOUR! How am I going to learn all of that by next year? And in Miss Santini’s class, we are going over problem solving but we are supposed to be doing algebra. Instead, Edgenuity makes you start from integers, adding/subtracting decimals. Like I know most of that, and while I’m doing it I learn some new things bu—tt, we are supposed to be doing algebra!”

Vignette, October 2015

Anya and many other students were nervous about so many unknowns that high school poses: Will I get into college? What is the process? How do I prepare? Will I be able to keep up with the mathematics required? Even though she felt comfortable with certain mathematical ideas and content, the pre-designed curricula dictated that she go through all of these concepts again. Misalignments between how personalized technologies interpret students’ learning and understanding can cause anxieties and frustration. In this case, Anya’s MAP placement tests had brought her back to pre-algebra, instead of where she felt she ought to be, in algebra.

The Limits of Learning Technologies in Science Teaching. Examining students’ feedback about their learning experiences in relation to science was eye-opening. It clearly illustrated that ‘innovative technologies’ that are intended to alleviate substantive educational inequities often miss the mark. Students at the Design School had a consistent science educator for the years I was there, Ms. Oswald. However, she did not have access to the budgets or resources for robust labs that required science technologies and materials. This created tensions that are made visible in the feedback from two students, Denise and Aria, who offered critical insight into what was missing from their science learning experiences.

Denise, a young Latinx woman, was independent, strong-headed, and embraced challenges. She loved Ms. Oswald, her advisory and science teacher, and loved science. Her out-of-school life was full of creative pursuits – designing book covers and writing novels on Wattpad, obsessing over online tutorials for makeup and hair, editing tribute videos to her favorite band, 21 Pilots. She started a youth empowerment group, spearheaded the school’s first bake sale, and worked closely with me in managing the logistics of school events. Denise offered this about her science learning:

I like what I’m learning I just don’t like DOING it... I don’t know how to explain it - I see it, I like it, okay - but then how can I just sit there on that thing and just sit and just watch, and watch and watch. I like HANDS-ON! BAM! I like if it’s worth doing it. Like doing something. Not just sitting there watching. Like the LAB- we didn’t get to do the lab! We had to freakin click a BUTTON on Edgenuity and that was us doing the lab - like are you KIDDING ME? You lose interest in it. So you want the hands-on: the fun stuff! let me cut open something dude! Let’s DO this.

Denise's creative and agentive practices outside of her academics were in stark contrast to how she had to pursue learning via an online platform: limited to watching someone else navigate the lab versus doing it herself.

Another student, Aria, who secretly harbored a passion to be a pediatrician, mentioned that “instead of doing everything like online, like I want more of that the hands-on approach…” and also listing off experiments that conjured images of ‘real’ or authentic science.

Embedded in both Denise and Aria’s characterizations of ‘hands-on’ learning is really a cry for authentic, embodied, and active learning experiences which are often unavailable to students in under-resourced schools (Darling-Hammond, 2010). Denise’s frustration that the ‘labs’ they conducted were just a click of a button illustrate the limitations of personalized educational technologies, particularly in subjects like earth science and biology. It is through practicing or doing science that students can explore personally meaningful phenomena, interface with domain specific vocabulary, and grasp conceptual knowledge (Furtak & Penuel, 2019).


The Design School students’ experiences highlighted above illuminate key issues in how adopting personalized educational technologies can both reproduce and remedy educational inequities.

Personalized educational technologies like Edgenuity can remedy educational inequities because they are self-contained platforms that deliver standards-aligned content and assessments, freeing teachers up to have more personal time with students. Moreover, using these platforms also gives students more autonomy, because they are self-paced and evaluate students on mastery of content (Basham et al., 2016). This can relax pressure on educators to ensure that every child in the class is understanding and engaging with the same sets of ideas simultaneously. Personalized technologies also create opportunities for students to take more ownership of their learning. As Ruby shared above, while the content and delivery mechanisms were not always appealing, she derived value from being able to make her own decisions about how she spent her time.

However, Platforms like Edgenuity can also reproduce long standing educational inequities. First, these platforms tend to be reductive: they position students as consumers of knowledge and liken learning to simply content acquisition. Second, these platforms replace social interaction with computer interaction. Third, they use limited data inputs to align students to the curriculum.

Students’ experiences in using Edgenuity were frustrating because the learning process was flattened to content mastery, and the system was easily manipulated. Keyword tracking was one feature that contributed to the students’ sense that they were not learning anything. Even the much maligned ditto worksheet requires some teacher feedback, whereas the assessment feature of Edgenuity did not require any human interaction–it was automatically graded based on which keywords a student entered (Edgenuity, n.d.). True learning cannot be reduced to simple answer-getting, but with regard to technologies like Edgenuity, this is effectively the outcome. This then positions students who are required to use these tools for their academic achievement as mere consumers and recipients of content, reifying persistent inequities in the education system that relegate youth of color to less dynamic and critically engaged educational experiences (Warschauer & Tate, 2018).

Personalized learning technologies also tend to rely on algorithms and automation to replace the work teachers traditionally do in classrooms (Basham et al., 2016). Opportunities for dialogue, for unraveling problems out loud and together, and to ask questions in real tim, are all features of ‘in-person’ learning that aren’t easily replicated with personalized technologies. Even with available technologies the quality of students’ experiences vary tremendously based on students’ race, class, and socioeconomic status and the culture of technology and learning in their schools (Rafalow, 2020). The use of personalized learning technologies in already under-resourced schools threatens to increase this divide.

The third issue relates to the dubious nature of what ‘personal’ really means. As mentioned above, Edgenuity, like similar platforms, suggests that it is a personalized educational technology that customizes learning experiences to students’ needs. At the Design School, students’ MAP test led to a system-generated individualized learning plan, which then informed what curriculum choices they had available to them. All the customizations were predicated on one data input, which limits how customizable or adaptive the learning can really be (Bulger, 2017). This is why Anya was frustrated with starting at pre-algebra again – she felt ready to move onto algebra but the data inputs suggested she was not. Even with sophisticated algorithms that have the potential to customize content to learners’ needs, if the quality of the inputs are limited, then the learning experiences will be bounded by what the system is able to produce. The structures of Edgenuity limited how effective it could be in nurturing students’ learning. In real time the best educators in classrooms are aware of where students are developmentally by cultivating personal connections, learning about students, and through an ethic of care and compassion. Systems that don’t have authentic and continual feedback loops cannot be characterized as “personalized’ as a specific one-time input cannot ‘know’ the whole person (Basham et al., 2016). Knowing young people means you know about their ride to school that day or that their parents were going through a divorce, and understand how that might impact their performance on a test or a quiz that day.

At the Design School did not have a culture of low expectations. Conversely, teachers and staff were advocates for students and held them to high expectations. The school wanted students to become independent thinkers and owners of their learning journeys. Adopting personal learning technologies was one way to foster independence and ensure their student-centered model could be realized. The Design School designed around the technology, offering pedagogical solutions like mini-lessons and individual conferences for students, but the technology failed to inspire students and also was frustrating and at times demoralizing for educators. While many students navigated their way through the content, there were many others who struggled, often skipping or ignoring assignments until the end of term, when they then scrambled to finish a host of incomplete work.This rendered the whole experience as work completion and not as carefully curated and personalized learning experiences that were tailored to students’ needs.

Algorithms are increasingly a significant part of our everyday lives but we have little opportunity to push back or question the ways in which they can have an outsized influence (Wilson, 2017). By adopting automated and algorithm driven educational solutions to facilitate students’ learning we implicitly suggest that technology solutions are superior and that education as a discipline and the nature of teaching and learning is simply something technology can solve (Roberts-Mohoney et al., 2016).

In the nation’s best schools, we expect students to be engaged in experiential or embodied learning experiences –they are not the exception but rather the rule. These schools possess well-resourced science classrooms where students have real equipment and machinery for scientific exploration, design classes with sophisticated software for graphic art and peer collaboration, powerful computers and robotics equipment to support students’ interests in computer programming and engineering, and more. Many urban schools, like the Design School, are under-resourced, and forced to compete for every dollar and opportunity, despite the heroic efforts of teachers and staff. Technocratic solutions, like, Edgenuity, are designed for credit recovery and content ‘acquisition.’ These approaches to learning harken back to the duality of public education that has persisted for over a hundred years, where youth of color, who are now often located in urban contexts, receive education that positions them as secondary, as consumers versus producers, preparing for jobs that do not demand their intellectual engagement but rather their complacency, obedience, and silence (Anderson, 1988; Anyon, 1981; Fine, 1991).


I write this at an unexpected time. We are in the midst of a global pandemic. There are a slough of articles and reports lamenting the ‘learning loss’ and the ‘summer slide’ that we will encounter because children aren’t in school (Wall & Franko, 2020; Dorn et al., 2020). Parents with children as young as pre-kindergarten are trying to negotiate online environments where children are held ‘accountable’ for seat-time for up to eight hours a day. Op-eds and research are abounding on the toll the lack of standardized testing will take on children who will fall further behind – especially the poorest and most vulnerable (Kuhfield & Tarasawa, 2020; Reilly, 2020; Sparks, 2020). A real fear is that policymakers will come out of this experience with the feeling that we should automate more educational functions and cede control to technologies that do not engage students in creative and critical production. Even in an environment like the Design School in their early years, where many students felt safe and connected to their peers and educators, the introduction of technologies as a replacement for meaningful teaching and learning relationships is a slippery slope. They belie the humanity and care that is central to real learning. To know children, to understand who they are, what matters to them, and how different disciplinary content and skills might animate and elevate their aspirations is a very different goal than what so ultimately occurs in much of urban public education. Instead, starved for resources in an otherwise wealthy nation, the most vulnerable children’s and youth’s learning experiences remain – even in the age of algorithms and sophisticated technologies–limited by a lack of understanding for what teaching and learning can and should be.

Personalized learning platforms driven by algorithms are at the forefront of corporate educational initiatives like the Amazon schools and the recently shuttered WeWork schools, as well as a new wave of philanthropy like the Chan Zuckerberg Foundation. We need to push back. Solutions that were marginally effective in the world of business and commerce should not impact millions of children, whose districts and leadership accept funding and parameters not out of choice but out of necessity. We must instead think critically about how we might learn from the creative and agentive ways we can make, create, share, and produce with technologies, and think carefully about how we can shift schools away from emphasizing students’ roles as passive consumers of technology and knowledge, to actively creating, making, and producing with technologies.


Veena Vasudevan is a postdoctoral associate at New York University and currently works on two NSF-funded grant projects focused on STEM education specifically related to citizen science and arts integrated data literacy. Her broader research agenda explores STEM learning, educational equity, and urban education with an emphasis on understanding the lives of children and youth through the lenses of identity, learning, and literacies.


Anderson, J. (1988). The education of Blacks in the South. University of North Carolina Press.

Anyon, J. (1981). Social class and school knowledge. Curriculum Inquiry, 11(1), 3-42.

Basham, J. D., Hall, T. E., Carter Jr, R. A., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126-136.

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.

Darling-Hammond, L. (2010). The flat world and education: How America’s commitment to equity will determine our future. Teachers College Press.

Dolan, J. E. (2016). Splicing the divide: A review of research on the evolving digital divide among K–12 students. Journal of Research on Technology in Education, 48(1), 16-37.

Dorn, E., Hancock, B. Sarakatsannis, J. & Viruleg, E. (2020). Public & social sector practice COVID-19 and learning loss—disparities grow and students need help. McKinsey.

Eddy, C., & Ballenger, J. (2016). The effectiveness of an online credit recovery program on improving the graduation rates of students at risk of school failure. School Leadership Review, 11(7). Retrieved from:

Edgenuity Help Center.

Edgenuity. (2014, May 13). Edgenuity MyPath™ I Edgenuity. [Video]. YouTube.

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

Farmer, M. R. (2016). Steps towards personalized learning using online asynchronous technology: A study of 7th, 10th, and 12th graders at a small rural school in Massachusetts. Educational Studies Dissertations, 3.

Ferguson, A. A. (2020). Bad boys: Public schools in the making of black masculinity. University of Michigan Press.

Fine, M. (1991). Framing dropouts: Notes on the politics of an urban high school. Suny Press.

Furtak, E. M., & Penuel, W. R. (2019). Coming to terms: Addressing the persistence of “hands‐on” and other reform terminology in the era of science as practice. Science Education, 103(1), 167-186.

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

Gadsden, V. L., Smith, R. R., & Jordan, W. J. (1996). The promise of desegregation tendering expectation and reality in achieving quality schooling. Urban Education, 31(4), 381-402.

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

Holt, T. C. (2002). The problem of race in the 21st Century. Harvard University Press.

Honey, M., & Kanter, D. (2013). Design. Make. Play. Growing the next generation of STEM innovators. Routledge.

Hull, G., & Schultz, K. (2001). Literacy and learning out of school: A review of theory and research. Review of Educational Research, 71(4), 575-611.

Ito, M., Baumer, S., Bittanti, M., boyd, d., Cody, R., Herr, B., Horst, H. A., Lange, P. G., Mahendran, D., Martinez, K., Pascoe, C. J., Perkel, D., Robinson, L., Sims, C., & Tripp, L. (2010). Hanging out, messing around, geeking out: Living and learning with new media. Cambridge: MIT Press.

Ito, M., Gutiérrez, K., Livingstone, S., Penuel, B., Rhodes, J., Salen, K., Schor, J., Sefton-Green, J., Watkins, S.G., (2013). Connected learning: An agenda for research and design. Digital Media and Learning Research Hub.

Ito, M., Arum, R., Conley, D., Gutiérrez, K., Kirshner, B., Livingstone, S., ... & Rhodes, J. (2020). The connected learning research network: Reflections on a decade of engaged scholarship. Connected Learning Alliance.

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

Kafai, Y. B., & Peppler, K. A. (2011). Youth, technology, and DIY: Developing participatory competencies in creative media production. Review of Research in Education, 35(1), 89-119.

Kuhfeld, M. & Tarasawa, B. (2020). The COVID-19 slide: What summer learning loss can tell us about the potential impact of school closures on student academic achievement. Northwest Evaluation Association [NWEA].

Llewellyn, T. (2019). A program evaluation of student and teacher perceptions of an online Edgenuity high school course program in an urban high school (Paper 1563898748) [Doctoral dissertation]. Scholarworks.

Moll, L. C., Amanti, C., Neff, D., & Gonzalez, N. (1992). Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. Theory Into Practice, 31(2), 132-141.

Moje, E. (2000). “To be part of the story”: The literacy practices of gangsta adolescents. Teachers College Record, 102(3), 651-690.

Nieto, S. (1999). The light in their eyes: Creating multicultural learning communities. Multicultural Education Series. Teachers College Press.

Omi, M. & Winant, H. (1994). Racial formation in the United States: From the 1960s to the 1990s. Routledge Press.

Peppler, K. A., & Kafai, Y. B. (2007). From SuperGoo to scratch: Exploring creative digital media production in informal learning. Learning, Media and Technology, 32(2), 149-166.

Pinkard, N., Martin, C. K., & Erete, S. (2020). Equitable approaches: opportunities for computational thinking with emphasis on creative production and connections to community. Interactive Learning Environments, 28(3), 347-361.

Papert, S. (1993). Mindstorms: Children, computers, and powerful ideas. Basic Books.

Rafalow, M. H. (2020). Digital divisions: How schools create inequality in the tech era. University of Chicago Press.

Reich, J., Ito, M., & Team, M. S. (2017). From good intentions to real outcomes. Connected Learning Alliance. Retrieved from:

Reilly, K. (2020, December 8). The learning gap is getting worse as schools rely on remote classes, especially for students of color. Time Magazine. Retrieved from:

Resnick, M., & Rosenbaum, E. (2013). Designing for tinkerability. In Honey, M. & Kanter, D. (Eds.), Design, Make, Play (pp. 163-181). Routledge.

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.

Rowsell, J., Morrell, E., & Alvermann, D. E. (2017). Confronting the digital divide: Debunking brave new world discourses. The Reading Teacher, 71(2), 157-165.

Sanchez, G. J. (1993). Becoming Mexican American: Ethnicity, culture, and identity in Chicano Los Angeles, 1900-1945. Oxford University Press.

Smith, A. (2020, September 3). Students uncover method to manipulate grading algorithm. The Independent. Retrieved from:

Sparks, S. (2020, April 9). Academically speaking, the ‘COVID Slide’ could be a lot worse than you think. Education Week. Retrieved from:

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

Thomas, D. E., & Stevenson, H. (2009). Gender risks and education: The particular classroom challenges for urban low-income African American boys. Review of Research in Education, 33(1), 160-180.

Vaughan, A. (2020). Conceptualizing scholarship on adolescent out‐of‐school writing toward more equitable teaching and learning: A literature review. Journal of Adolescent & Adult Literacy, 63(5), 529-537.

Vossoughi, S., & Bevan, B. (2014). Making and tinkering: A review of the literature. National Research Council Committee on Out of School Time STEM, 67, 1-55.

Wall, P. & Franko, K. (2020, October 30). On pandemic ‘learning loss,’ schools look forward, not back. Associated Press. Retrieved from:

Warschauer, M., & Matuchniak, T. (2010). New technology and digital worlds: Analyzing evidence of equity in access, use, and outcomes. Review of Research in Education, 34(1), 179-225.

Warschauer, M. & Tate, T. (2018). Digital divides and social inclusion. In Mills, K., Stornaiuolo, A., Smith, A., Zacher Pandya, J. (Eds.), Handbook of writing, literacies, and education in digital cultures, (Chapter 5). Routledge.

Willson, M. (2017). Algorithms (and the) everyday. Information, Communication & Society, 20(1), 137-150.


No comments here