Prepare to become an education researcher capable of leveraging large-scale data using data science methods, improving education quality with digital learning platforms. 

Over the last 15 years, the educational data mining and learning analytics communities have developed a range of algorithms tailored to the data and R&D goals of digital learning platforms. The Data Science Methods for Digital Learning Platforms certificate provides learners with a breadth and depth of skills that expand beyond existing courses of its length in data science or education more generally. 

What Sets Us Apart

14 Learn from 14 leading researchers with expertise in different areas of learning analytics.
  Earn your certificate for free, thanks to support from the Institute of Education Sciences.
 A focused 16-week certificate that teaches career-useful skills.

About the Program

Working with the data from digital learning platforms requires a range of data science methods. Do you have the right tools to pursue your education research questions?  

Through the Data Science Methods for Digital Learning Platforms certificate program, you will learn to use both algorithms designed specifically for digital learning platforms and how to effectively apply algorithms developed for more general purposes to digital learning platform data. 

This program is open to US citizens or permanent residents only.

Duration of program
16 weeks; 6–8 hours per week asynchronously required

Application 

  • Application Opens: May 1, 2025
  • Priority Deadline: May 16, 2025
  • Rolling thereafter, until cohort is filled.

Program StartAugust 18, 2025

Certificate offered: Penn GSE certificate in Data Science Methods for Digital Learning Platforms 

Ideal candidates

  • Researchers (or aspiring researchers) with a clear intent to pursue education research questions
  • Researchers (or aspiring researchers) with a diverse range of academic backgrounds, including education, psychology, sociology, economics, and computer science
  • Researchers (or aspiring researchers) with professional experience in academia, industry, non-profits, school districts, and government

Prerequisites 

  • Introductory-level knowledge of statistics and/or quantitative research
  • Beginner knowledge of Python OR sufficient knowledge of R 
  • Must be a U.S. citizen or permanent resident
Modality
  • Online
Overview

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 methods. You will also learn how to move beyond the well-structured use cases often utilized in introductory data science and statistics courses, which 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” 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. 

Application opens May 1, 2025.  
Sign up to receive a notification.

 

Program Schedule

DatesTopics
August 18 - 22, 2025Introduction, Challenges, and Framework
August 25 - 29, 2025Data and Measurement Validity
September 1 - 5, 2025Prediction Modeling and Metrics
September 8 - 12, 2025Feature Extraction and Feature Engineering
September 15 -19, 2025Data Visualization
September 22- 26, 2025Ethics, Equity, and Algorithmic Bias
September 29 - October 3, 2025Data Management and Database Access
October 6 - 10, 2025Knowledge Graphs
October 13 - 17, 2025Knowledge Tracing
October 20 -24, 2025Cluster Analysis
October 27 - 31, 2025Network Analysis
November 3 - 7, 2025Sequential Pattern Mining and Temporal Analysis
November 10 - 14, 2025Causal Reasoning
November 17 - 21, 2025Neural Networks and Deep Learning
December 1 - 5, 2025Natural Language Processing
December 8 - 12, 2025Transformer and Foundation Models

Partners in Collaboration

Partners in Collaboration

 

This program and certificate are made possible through a partnership between Penn GSE, the University of Florida, and Digital Promise. 

Penn GSE Learning Analytics Logo
University of Florida Logo
Digital Promise Logo

 

This program is led by Principal Investigator (PI) Ryan Baker, Penn GSE; along with 5 co-PIs representing the collaborating institutions: Anthony Botelho, University of Florida; Bodong Chen, Penn GSE;  Elizabeth Cloude, Tampere University; Stefani Pautz Stephenson, Digital Promise; and Jeremy Roschelle, Digital Promise.

 

 
IES logo
 

 

This project is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305B230007 to the University of Pennsylvania. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

By IES requirements, only US citizens or permanent residents are eligible for this program. If you are ineligible for this program, you can find other training opportunities at https://seernet.org/research-training-opportunities/.

Our Faculty

Penn GSE Faculty Seth Akonor Adjei
Seth Akonor Adjei
Assistant Professor, Northern Kentucky University
Ph.D., Worcester Polytechnic Institute
Penn GSE Faculty Michael Ashenafi
Michael Ashenafi
Project Scientist, Penn Center for Learning Analytics
Ph.D., University of Trento
Penn GSE Faculty Ryan S. Baker
Ryan S. Baker
Professor
Ph.D., Carnegie Mellon University
Penn GSE Faculty Anthony Botelho
Anthony Botelho
Assistant Professor, University of Florida
Ph.D., Worcester Polytechnic Institute
Penn GSE Faculty Alex J. Bowers
Alex J. Bowers
Professor of Education Leadership, Teachers College, Columbia University
Ph.D., Michigan State University
Penn GSE Faculty Wendy Chan
Wendy Chan
Assistant Professor
Ph.D., Northwestern University
Penn GSE Faculty Bodong Chen
Bodong Chen
Associate Professor
Ph.D., University of Toronto
Penn GSE Faculty Scott Crossley
Scott Crossley
Professor, Vanderbilt University
Ph.D., University of Memphis
Penn GSE Faculty Shamya Karumbaiah
Shamya Karumbaiah
Assistant Professor, University of Wisconsin–Madison
Ph.D., University of Pennsylvania
Penn GSE Faculty Walter Leite
Walter Leite
Professor, University of Florida
Ph.D., University of Texas at Austin
Penn GSE Faculty Haiying Li
Haiying Li
Program Manager
Ph.D., University of Memphis
Penn GSE Faculty Jaclyn Ocumpaugh
Jaclyn Ocumpaugh
Associate Director, Penn Center for Learning Analytics
Ph.D., Michigan State University
Penn GSE Faculty Jinnie Shin
Jinnie Shin
Assistant Professor, University of Florida
Ph.D., University of Alberta

Program Leadership

Ryan Baker  
University of Pennsylvania

Anthony Botelho
University of Florida

Bodong Chen
University of Pennsylvania

Elizabeth Cloude
Tampere University

Jeremy Roschelle
Digital Promise

Stefani Pautz Stephenson
Digital Promise

Contact Us

For more information about the Data Science Methods for Digital Learning Platforms certificate program, contact us at ProfessionalLearning@gse.upenn.edu