Building Tech Curriculums For Marginalized Learners

A growing number of tech educational programs don’t center our lived experiences or the things that motivate us as learners.

by Tiffany Mikell on August 17th, 2016

Recently, I volunteered as a guest instructor for a community based code school. Most of the students were adult, low-income and primarily people of color with limited previous computing experiences. After the class, I was approached by several students who were so excited that I came, and expressed that despite weeks of formal instruction on web development from full-time paid instructors, this was the first time that they really “got it.”

It felt good, but confirmed what I already knew to be true: learning complex concepts is only harder when the learner has to do additional work such as battle microaggressions and biased instructional systems that don’t reflect their context and experiences.

This is an essay about how to use cognitive science to inform tech education for learners in systems of oppression. More specifically, how can we use what we know about the brain to design tech educational programs that are effective for people of color? I argue that when curriculum and learning environments are centered in whiteness, the brains of non-white people have to work harder to organize the new information. I’m frustrated with the growing number of educational programs designed specifically for learners of color in the technology space, but that don’t center our lived experiences or the things that motivate us as learners.

A black woman reading a book on Java programming, next to a stack of other technical books including "Compiler Design."

Photo CC-BY WOCinTech Chat.

Teaching marginalized learners without relevant context is a waste of time.

Throughout my career I’ve helped design many alternatives spaces for learning technology, and although my current day-to-day gig is building software for other educators, I make time to share what I know with others when allowed. So, I recently delivered a workshop on the introductory concepts of data modeling for web applications: an onsite, four hour deep-dive on data and SQL.

Teaching beginners how to organize relational data is something I love, particularly because the concepts are evergreen, super actionable and often transferrable across the tech stack. There’s also a strong relationship between how relational databases store and retrieve information and how our brains learn. In a flat file model of storage, data records are simply added in chronological order to one extremely large list of records. As flat file systems grow in size, they notoriously require monumental resources and effort to maintain. Most traditional educational environments treat our brains like flat file storage systems: we cram down facts and definitions and formulas, hoping they are committed and available for retrieval when needed. In reality, this is a highly inefficient way for our brains to process data.

Unlike the flat file model, the relational approach to databases organizes information in separate sets that shows clear relationships between them, making the addition, extension and analysis of complex data sets more efficient. In other words, relational databases depend on linkages of information. Similarly, the act of learning requires that short term memory be processed into long-term memory, a cognitive task that only occurs when learners can relate this new information to our existing mental maps: “The successful retrieval (output) of knowledge to solve problems depends on the way information is initially encoded. For this new knowledge to be useful and permanent, it must be encoded with the students’ prior knowledge” (Gillani, Bijan B., Learning Theories and the Design of E-Learning Environments, 2003).

To help create these “aha” moments for my SQL class, consciously and subconsciously I employed practical applications of effective curriculum delivery based on learning theory insights. I was intentional about how I presented the workshop material, creating opportunities to have conversations about concepts in plain english without tech jargon, and understanding a small bit about each of the learner’s backgrounds so I could use analogies and references that were most familiar. I integrated examples based in commonly used applications, such as Facebook and, as well as analyzing particularly resonant data sets such as police misconduct rates and public school funding trends.

Meaningful and relevant context looks different for learners with different lived experiences. If I’m learning a topic and only presented with work, research, problem sets, etc that don’t speak to any of my own lived experiences, my brain works harder to organize the new information. Therefore, it’s essential that curriculum design, and ideas presented in workshops, classes, or books create deliberate references to the learners’ prior knowledge. Offering content is just the start of the revolution: we must also offer opportunities for the application of the information and create the conversations that make that content relevant. An intentional focus on such will not only increase retention in STEM academic settings, but will also increase retention in STEM careers long-term.

We also need to recognize the role of relationship-based learning in educational environments for marginalized people.

Collaborators of color gather around a laptop in a conference room.

Photo CC-BY WOCinTech Chat.

Our context for learning is affected by environment. We know that the single percentages of people of color working in engineering departments often feel isolated. We also know that this isolation often starts in the halls of academic institutions and code schools. This is particularly important to understand for learners of color in technology specifically, as relationship-based learning assumes that you have enough relationships with teachers, mentors, colleagues and peers that respect you enough to engage in this; for too many people of color in STEM, this is not the case. Yet relationships in learning environments heavily impact performance; for example, “Students are motivated when they believe that teachers treat them like people and care about them personally and educationally. When teachers apply in the classroom their knowledge of human needs, amazing things happen. These teachers treat students with respect; offer meaningful, significant choices; create valuable, fun, or interesting learning opportunities; and foster relationships that help students see teachers as teachers and not as dictators, judges, juries, or enemies” (Rogers & Renard, Relationship-Driven Teaching, 1999). The importance of relationships in learning also extends to our relationships with other students, and larger systems of support and mentorship.

In 2006, I taught myself to code with VB.NET and learned to build web applications with Drupal. That winter, I started my first professional role as a Java developer with Accenture. The first 30 days in that role was one of the most intense learning experiences of my career: all new hires were given exactly 4 weeks to pass an extensive written exam on the Java programming language as well as build a functional web application according to specs. I was the only black woman in my cohort and the only hire without a CS degree. Imposter Syndrome was at its peak and the first few days felt utterly overwhelming for me. I was constantly asking myself if I was smart enough to make it and it seemed so many environmental factors confirmed my incompetence. I was the other. Maybe this engineering thing wasn’t really for people like me.

Thankfully, I quickly realized that those first few days were so difficult for me because I was attempting to learn in isolation; simply cramming down rules and definitions. I had to develop relationships with other learners to be successful. I needed to have those after-hours conversations and lunch-time napkin diagram sessions to really transfer what I absorbed in my short-term memory into something that could be retrieved long-term. Yet, it was incredibly difficult to be my own educational advocate and navigate a system where my presence made many of my peers uncomfortable.

Two women of color pair program at a standing desk in an office environment.

Photo CC-BY WOCinTech Chat.

The following are actions I took that continue to be proven helpful for myself and other learners of color who struggle with experiential learning in environments that don’t embrace our lived experiences:

  • I identified a handful of key internal allies that helped keep me engaged (This can be really hard sometimes, but necessary.)
  • I developed an extensive network of mentors outside of my corporate training environment
  • I applied concepts I was learning immediately to a passion project that I cared enough about to work on at 2am
  • I came to know my individual learning style on an intimate level and advocated for relevant examples and case studies
  • I found & created accountability groups with learners who had similar life experiences, allowing me to debrief when I experienced microaggressions as well as articulate and focus on long-term goals

Designers of tech education spaces who are intentional about creating inclusive environments can incorporate many of these strategies, including mentoring programs with industry professionals of color, allowing time and resources for personal projects and creating space (both physical and energetic) for common interest groups. Unfortunately, these are often seen as supplemental “nice to haves,” program amenities, rather than critical components of design and delivery. Institutions delivering tech education need to collectively recognize and prioritize how the historical homogeneity of computing as both an industry and an academic area of study impacts learner potential.

Call to Action

Understanding the environments and frameworks that help us organize new information most effectively is something we often take for granted as lifelong and continuous learners. Through neuro and learning science, we’ve come to know a great deal about the how the brain works; insight that can be applied directly to our educational models. Too often — particularly with STEM-based education — this knowledge transfer does not occur, often due to lack of informed intentionality when designing programs specifically for learners of color.

Educational justice can be defined as equality of opportunity for achieving essential educational outcomes (Waltenberg, 2006). Resources allocated under this charge have focused narrowly on access in forms of scholarships and visibility (profiles, POC in leadership positions, etc). But it isn’t just about getting more bodies in tech education spaces; missing is a focus on relevant pedagogy rooted in cognitive science. We must also center strategies for shifting those spaces so they are inclusive for all. Because of the nature of technology roles and how they require you to be constantly learning, we must design tech education that not only drives impact and innovation, but also fosters the human support systems that provide the motivations and belief systems our brains need to tackle rigorous topics, thus creating truly equal access to STEM education.