Data Literacy - Making Sense of the Systems That Shape Us by Kyan

Data Literacy - Making Sense of the Systems That Shape Us by Kyan - June 2025 Scholarship Essay

Today, data drives how we live, learn, work, and connect. In such a world, learning to interpret data isn't just a technical skill; it’s a foundational literacy.

If I could make one elective a required course, it would be Data Literacy. Not because everyone should become a data scientist, but because everyone is already a data subject, monitored by algorithms, generating data that shapes our choices, identities, and interactions. In such a world, it’s essential that all individuals are equipped to understand the systems that shape them, recognize their influence, manage their effects, and defend their cognitive and creative agency from being quietly overwritten. A high school course in Data Literacy will teach students to identify how algorithmic systems influence their choices, decode how their data is collected and used, and develop strategies to retain control over their time, attention, and creativity in increasingly automated environments.

"Data Literacy" will be a hybrid course combining foundational Statistics and Computer Science, and will raise urgent and empowering questions: How do systems built to predict our behavior end up reshaping it? What does it mean to protect your agency when platforms know your next click better than you do? And how can we use data not just to extract insights, but to resist manipulation and reclaim our attention?

In the statistics component, students will learn to analyze patterns in the data-driven environments they engage with every day. They’ll explore how platforms like TikTok or Spotify tailor content based on user behavior, and how those feedback loops shape habits, preferences, and perceptions. Rather than approaching this through abstract theory, students will complete hands-on exercises that apply key statistical tools, such as tracking their own digital activity and mapping trends in how algorithms respond to them. They will also examine examples of persuasive data presentation, learning to identify when graphs, trends, or figures are being used to subtly steer attention or belief. These activities will not only ground students in foundational statistical reasoning but also give them practical tools for noticing when they are being guided and how to respond with awareness rather than complacency.

The computer science component will give students a basic understanding of how algorithmic systems work, helping them grasp how platforms like YouTube, Amazon, and Instagram make decisions that shape their experiences. Students will build simple versions of recommendation engines, not to become developers, but to understand how platforms respond to their behavior, guiding choices while prioritizing certain actions and values over others. By adjusting inputs and watching the results, students will gain an intuitive grasp of how algorithmic systems adapt and sometimes amplify unintended effects, revealing the trade-offs in everyday design decisions. A classroom activity like "Design Your Own Attention Trap" will prompt students to examine their digital habits and uncover persuasive design tactics, while broader exercises will build their ability to recognize and understand how algorithmic systems shape behavior and attention. These skills will help them make digital choices that reflect their own values rather than the priorities of a platform, and stay more aware of when their attention or preferences are being subtly influenced.

The personal impact of this course will be deeply empowering. Students will begin to distinguish between their own autonomous choices and subtle forms of algorithmic influence. A student who once scrolled aimlessly might now pause to ask, “Why was I shown this?” or “Whose interest does this serve?” These small moments of awareness accumulate into cognitive resilience: the ability to resist manipulation, make intentional choices, and preserve focus in an attention economy designed to erode it. Students may start setting boundaries with devices, resist autoplay defaults, or diversify their information diets; not because they were told to, but because they’ve developed the agency and insight to take ownership of their digital behavior.

The societal benefits are just as profound. A citizenry fluent in data systems will be harder to mislead, less vulnerable to propaganda, and more capable of demanding accountability from the technologies that govern their lives. Imagine a generation that can challenge biased AI in hiring tools, intuitively notice how generative tools reinforce stereotypes, or articulate why algorithmic transparency should be a civil right. Teaching Data Literacy is not just an educational upgrade; it’s democratic infrastructure. It equips students not just to succeed in a data-driven world, but to question and improve it.

My own journey with data began in high school when I took a Juni Learning course and analyzed World Happiness Report data. Discovering how walkable urban planning correlated with happiness sparked my fascination with the human stories that data can reveal. Since then, I’ve explored algorithmic bias in music platforms, mis-recycling habits through the recycling tracker I created for my school, and how school orientation experiences can be improved by creating sentiment models and tracking systems. These projects showed me how non-human systems can shape and sometimes distort human behavior. But with understanding, intention, and limitation, these tools can be used to maximize human fulfillment. Making Data Literacy a required course will ensure all students gain that same clarity: the ability to set boundaries & question systems, while also using data to create better outcomes for themselves and their communities.