Data Science Methods for Digital Learning Platforms

In this 16-week program, participants will learn to conduct analysis on real-world data, working directly with authentic student interaction data that has not been cleaned. You’ll compare statistical and psychometric approaches to machine learning and data mining approaches, and learn how to move beyond the well-structured use cases often utilized in introductory data science and statistics courses that are often not representative of the data that comes from digital learning platforms.

The program is online and asynchronous, with one optional synchronous and virtual  “Ask me anything (AMA)” session with the instructors. Each module includes discussion-based interactions with peers and instructors and a project-based assignment for which fellows will be able to apply the skills they learn using authentic tools and datasets. The examples and assignments corresponding with each module will align with real challenges and scenarios common to digital learning platforms, and emphasis is given to identifying the development of relevant research questions and understanding the limitations and affordances that different types of digital learning platform data may provide in addressing these questions.

This program is designed for individuals with a clear intent to pursue education research, and who have some degree of prior quantitative analysis background and either an intermediate-level understanding of statistics or psychometrics or a background in computer science. Fifty participants will be selected based on a competitive application process.

This is the fifth and final cohort of this program. While we encourage applications to be submitted by the priority application deadline, admissions will be reviewed on a rolling basis until the cohort is filled. Admission decisions are expected to be announced by January 12, 2026. There is no cost to participate in this program. 

This program is best suited for researchers or aspiring researchers who are committed to exploring education-focused research questions and bring professional experience from academia, industry, non-profits, school districts, or government. Applicants should have a foundational understanding of statistics or quantitative research methods and beginner-level proficiency in Python or sufficient knowledge of R. A wide range of academic backgrounds is welcomed, including education, psychology, sociology, economics, and computer science. 
 

U.S. citizenship or permanent residency is required.