Situating Computational Learning Opportunities in the Digital Lives of Students

A research project exploring ways to introduce high-school students to the computational foundations of data science

About the Project

API Can Code is a project aimed to situate data science in the lived experiences of today's students

The API Can Code project explores ways to introduce learners to foundational ideas about computer science by having them explore the data that shapes their lives. In particular, the learning experiences will center on application programming interfaces (APIs) for popular platforms and services, allowing students to better understand the digital worlds surrounding them. This approach situates foundational computer science and data science learning in interest-driven contexts that draw on learners’ prior knowledge and is authentic to professional practice.

API Can Code is part of a research-practice partnership between the University of Maryland and Washington Leadership Academy. This project will work with students and teachers to design an interest- and data-driven computer science curriculum and iteratively refine and study the curriculum in classrooms over 3 years. This research will contribute to our understanding of the role of interest and data as a means to impact students’ perceptions of computer science and understanding of the impacts of computing and data on their lives.

The API Can Code Curriculum

The API Can Code curriculum will include three units (Figure 1) that will introduce students to the computational foundations of data science.

Unit 1 - "Data in Learners' Lives" aims to help students better understand the data that surrounds them and how it affects their lives.

Unit 2 - "Computational Foundations of Data Science" will help students gain the essential skills to programmatically retrieve and manipulate publicly available data from diverse Application Programming Interfaces (API's), using block-based environments.

Unit 3 - "Data Science Practices" will focus on acquiring skills to analyze and visualize data to extract meaningful insights from them.

Collectively, this three-unit data science curriculum is designed to provide learners with a well-rounded understanding of data science, starting from its foundational concepts and ethical applications, advancing to essential computational skills, and culminating in applying data analysis and visualization techniques to draw meaningful conclusions and effectively communicate their insights. By the end of the curriculum, learners will be equipped with the knowledge and practical skills necessary to tackle real-world data science challenges.

An overview of the API Can Code Curriculum can be found here.

Figure 1 - The API Can Code curricular units, aligning with three emerging technologies.

Intro to Data Science

What is Data Science all about and why does it matter?

Data science is an independent discipline that lives at the intersection of statistics, computer science, and an applied domain

Data scientists uses algorithms, computational technologies, and programmed processes to draw insights from data. Datasets are often quite large, and the process of analysis may include cleaning or re-structuring the data.

Understanding the role of data in our lives, and having the knowledge and skills to effectively use that data, is essential for all students to be informed citizens and to succeed in an increasingly digital world. This is especially true for those from minoritized groups historically excluded from computing, as these individuals are disproportionately affected by systematic inequities that exist and are being perpetuated through technology. To address the growing importance and impact of data in our world, this project is exploring ways to bring data science into classrooms where all students can learn it.

Frequently Asked Questions

API stands for Application Programming Interface. This is a mechanism that enables two software components to communicate with each other using a set of functions and protocols. APIs can be used by data scientists to automatically gather data from a web service or database.

Data is shaping the way we experience the world in visible and invisible ways. Understanding the role of data in our lives, and having the knowledge and skills to effectively use that data, is essential for all students to be informed citizens and to succeed in an increasingly digital world.

Everyone! Given the role that data is playing in shaping our world, it is essential that all students have a foundational understanding of data science.

David Weintrop is the Principal Investigator leading the project. Dr. Weintrop is an Associate Professor at the University of Maryland with a joint appointment in the Teaching & Learning, Policy & Leadership department in the College of Education and the College of Information Studies (iSchool).

This project is a research practice partnership between the University of Maryland and Washington Leadership Academy. By partnering, the project reflects the goals and priorities of both researchers and educators to ensure the result is impactful and reaches the audience it is designed for.

While the API Can Code curriculum is still being developed, teachers and administrators looking for data science curricula should consider the options listed in the Resources section below.


The Team

David Weintrop

David Weintrop

Principal Investigator

David Weintrop is an Associate Professor in the Department of Teaching & Learning, Policy & Leadership in the College of Education with a joint appointment in the College of Information Studies at the University of Maryland. His research focuses on the design, implementation, and evaluation of effective, engaging, and equitable computational learning experiences. His work lies at the intersection of design, computer science education, and the learning sciences. David has a Ph.D. in the Learning Sciences from Northwestern University and a B.S. in Computer Science from the University of Michigan.

Rotem Israel-Fishelson

Rotem Israel-Fishelson

Postdoctoral Researcher

Rotem Israel-Fishelson is a postdoctoral researcher in the Department of Teaching & Learning, Policy & Leadership in the College of Education at the University of Maryland. Her research focuses on exploring ways to introduce learners to data science using engaging computational learning experiences. She is also interested in assessing computational thinking and creativity skills in game-based learning environments using learning analytics methods. Rotem holds a Ph.D. in Science Education from Tel Aviv University, M.Sc. in Media Technology from Linnaeus University, and a B.A in Instructional Design from Holon Institute of Technology.

Peter F Moon

Peter F Moon

Doctoral Student

Peter Moon is a PhD student in the Center for Math Education at the University of Maryland. His research interests include expressions of algebraic and pre-algebraic thinking, integrating computational thinking into teacher preparation programs, and learning through games; at Maryland, he also works in the NOTICE lab and teaches statistics courses for math teachers. Peter is a Connecticut native with degrees in Psychology from the University of Pennsylvania and Teaching (Secondary Mathematics) from Loyola University Maryland; previously, he taught math and computer science and coached swimming at a high school in Baltimore.

Xiaoxue Zhou

Xiaoxue Zhou

Doctoral Student

Xiaoxue is a Ph.D student in the TLL (Technology, Learning, and Leadership) Program at the University of Maryland. Her research interests include equity issues in K–12 computer science education, data science education, and student achievement assessment. Before starting her doctoral journey in the University of Maryland, she worked as a senior research data analyst at Johns Hopkins University for about four years. She has two master's degrees in Science and a bachelor's degree in Engineering.


Former Team Members

Rachel Tabak

Doctoral Student

Daniel Pauw

Doctoral Student


Here are some cool and useful resources

  • All
  • K-12 Curricula
  • Tools
  • Web


YouCubed offers a high school data science curriculum, K-12 lesson plans and comprehensive professional development. Materials introduce data science concepts and support data collection projects led by students.


CODAP is a data analysis platform offered by the Concord Consortium and available for use by teachers and students.


CodeHS provides curricula for Data Science through our online learning platform.


Bootstrap offers a course curriculum, lesson modules and professional development workshops focused on learning the fundamentals of coding and data analysis.


The UCLA Introduction to Data Science offers a complete data science curriculum for high school students and several professional development opportunities.


The world's first evidence-oriented programming language


Tableau is a leading data visualization tool used for data analysis and business intelligence.


EduBlocks is a visual block-based programming tool that helps teachers to introduce text-based programming languages, like Python and HTML.


NetsBlox is a visual programming language and cloud-based environment that enables novice programmers to create networked programs.


Data Science for Everyone (DS4E) is a growing coalition of individuals and organizations elevating the importance of data literacy and expanding access to K-12 data science education - for every student.


Tuva is a platform that enables students to easily explore, manipulate, and analyze data.

Rapid API

The world's largest API marketplace, used to discover and connect to thousands of APIs.


CourseKata Statistics and Data Science is an innovative interactive online textbook for teaching introductory statistics and data science.

K-12 Tools Comparison

Comparison of Data Science Tools For K-12, created by API Can Code team.


Academic Research from the Project