Data Science Methods for Digital Learning Platforms

The program is online and asynchronous, with one optional synchronous and virtual “ask me anything” 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. 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. Twenty participants will be selected for the first cohort based on a competitive application process.

There is no cost to participate in this program, though final confirmation of funding is still pending. 

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.