@Concord Winter 2025

The Concord Consortium has just celebrated its thirtieth anniversary. As I flip through the first issue of our @ Concord newsletter, I feel both nostalgic and proud. That original issue paints a picture of an idealistic and scrappy era. Across its articles, I see innovators grappling with technical hurdles—that perennial hallmark of cutting-edge work—as they struggle to ensure broad access to technology-based resources in an era when computers were still hard to come by. I also see remarkable examples of ingenious ideas becoming reality, in the form of theories that would come to enter the common vernacular and early-stage software concepts that would ultimately transform into entire industries. From online learning to sensors and simulations, the Concord Consortium has been pioneering educational innovations from its inception.

Data science and artificial intelligence (AI) are poised to upend the way healthcare is delivered. But what happens when AI is trained on medical data that doesn’t reflect the population? A new semester- long course teaches high school students how embedded bias in machine learning affects healthcare discussions and decisions. The goals of the Data Science, AI, and You in Healthcare project include fostering community connections between educators, researchers, clinicians, and local stakeholders to prepare underrepresented students for STEM jobs—ultimately in fields where their background and experience can help mitigate bias embedded within AI healthcare models and improve outcomes for all.

Middle school students typically study the immigration experience of the tumultuous 20th century and its impact on American policy in their social studies class, often using photos and written accounts as primary sources. Data can also serve as an engaging primary source. Data science is an inherently interdisciplinary field, allowing students to make connections between humanities, math, and issues of community interest.

Consider for a moment how you think about yourself in relation to science, technology, engineering, or math. If you’re a STEM teacher, instructional designer, or researcher, you may identify as someone who knows about, cares about, and contributes to STEM pursuits. You may have developed these feelings as a child who was encouraged to question how things worked, just as engineers do. On the other hand, you might have originally shied away from math and science, thinking that those subjects were for other people, but not for you.

The use of sound to communicate nonverbally predates written records and is deeply embedded in human history. The simple rhythmic sounds from early drums, bells, whistles, and horns evolved to becomes complex communication data. Today, graphs are the most common way information from data is conveyed. The COVID-Inspired Data Science Education through Epidemiology (CIDSEE) project, a collaboration between Tumblehome, Inc. and the Concord Consortium, is developing and researching new tools that add sound to graphs to help students explore and make sense of data.

Beaches are dynamic landscapes that are always changing. However, due to human development and climate change, the speed and intensity of change has been increasing. Our Precipitating Change with Alaskan Schools project has developed curriculum activities focused on the coast and coastline so students can engage in locally relevant scientific practices.

Suppose you’re modeling the weather, and in your model the weather can be in three states: sunny, cloudy, or rainy. If it’s sunny today, there are different probabilities that tomorrow it will remain sunny, become cloudy, or turn rainy. In this model, tomorrow’s weather depends only on today’s conditions, not on what the weather was like in the past. Implementing this simple three-state model using a “Markov chain” enables you to generate a large number of sequences of daily weather conditions. And those sequences can help you look for patterns, which is the beginning of machine learning, used in many artificial intelligence applications.

As a child growing up in Silicon Valley, Christina Chin was no stranger to technology. Her father, an “old IBMer who brought home all kinds of gadgets,” gave Christina her first laptop, a bulky briefcase-like device. She still remembers the screen’s distinct monochromatic orange pixels. These days, she’s using considerably more modern technology with her middle school students to get them excited about data science.

The Concord Consortium is happy to announce the following new grants from the National Science Foundation.