Master the fundamentals of data analytics and AI to optimize teaching, learning, and assessment.

Learning Analytics Online Master’s Degree will empower you to leverage data analytics and AI to drive high-quality decisions within the educational context. The program prepares data scientists to develop advanced skills in measurement, analysis, and predictive modeling,  leveraging state-of-the-art methodologies such as machine learning, generative AI, and deep learning. Students will also develop skills in real-time data analysis and visualization, personalized learning recommendation generation, and data management, equipping them to develop and enhance data-driven educational environments.

What Sets Us Apart

  Penn GSE is a top-ranked Graduate School of Education by U.S. News & World Report
laptop_mac Learn the latest technical methods grounded in the rich history of educational thought
groups Be part of a vibrant Ivy League community of scholars and practitioners learning together

About the Program

This fully online program prepares graduates to work as data scientists in research and development in areas such as at-risk prediction, intelligent tutoring systems, and educational recommender systems. You will emerge understanding when and why to use different methods for a range of applications in order to make a real-world impact. The program teaches you both the latest learning analytics algorithms and tools as well as how to engineer data streams to turn raw data into interpretable and valuable features. Your use of contemporary data analytics methods will be grounded in the rich history of educational thought, with an understanding of how this grounding can support efforts to address challenges such as algorithmic bias to improve educational outcomes at scale.

Application deadline
Closed for Spring 2024. Fall 2024: rolling admission.
Entry term(s)
Fall, Spring
Course requirements
10 courses (7 required and 3 concentration courses).
Typical course load

Fall: 3 courses; Spring: 3 courses; Summer: 2 courses; Fall: 2 courses and capstone.

Duration of program
16 months (full-time); 2 years (part-time)

Culminating experienceMaster’s capstone project

  • Full-Time
  • Part-Time
  • Online
  • Online
Programs for Working Professionals

Learning Analytics uses educational data and modern algorithms to develop learning technologies that enhance student outcomes, support teachers and school leaders, and help identify and address gaps. Successful use of learning analytics depends on understanding both algorithms and learning, including the social context that surrounds learning and the use of learning analytics.

This program can be completed within 16 months by following the recommended schedule (Fall, Spring,  Summer, Fall), or students can enroll part-time and complete the program within two years. The program consists of coursework and a capstone project where students will develop projects with real-world relevance and of a quality that can be submitted as a demo or short papers to international conferences. The program is fully online (no in-person component) and will have a mixture of synchronous and asynchronous activities, with multiple sections/time slots for synchronous activities to accommodate students around the world.


The program will have 7 required classes, and 3 concentration courses, culminating in a capstone, where students will demonstrate their readiness to work in industry and/or produce competitive submissions to demo and short paper categories at top international conferences.

  • Core Methods in Educational Data Mining (Baker)
    Teaches essential methods and algorithms used in practice
  • Big Data, Education, and Society (Baker)
    Prepares students for opportunities, challenges, and constraints in real-world usage of learning analytics, including challenges around algorithmic bias. Covers key applications in practice and factors accelerating and impeding their usage.
  • Dashboards and Analytics for Reporting (Ocumpaugh)
    Dashboards are a type of report for teachers, school leaders, school counselors, and other stakeholders. This class will cover design principles and current use cases.
  • Databases and Data Management (Li)
    This course teaches the use of databases within analytics -- i.e. how to set up a database, extract data from it, and analyze data.
  • Foundations of Teaching and Learning (Gouveia)
    The course covers key thinkers and ideas in education and the application of their ideas around the world.
  • Feature Engineering (Li)
    Feature engineering is the practice of transforming complex data streams into interpretable variables that can be used within algorithms to generate models that can be validated and used.
  • Learning Analytics Masters Capstone Seminar (Li)

Concentration Courses will include multiple options, such as Adaptive Learning Systems, Design of Learning Environments, Applied Network Analysis in Learning, Deep Learning and Transformer Models, and Quantitative Ethnography.

Our Faculty

Penn GSE Faculty Ryan S. Baker
Ph.D., Carnegie Mellon University
Penn GSE Faculty Bodong Chen
Associate Professor
Ph.D., University of Toronto
Penn GSE Faculty Yasmin B. Kafai
Lori and Michael Milken President’s Distinguished Professor
Ed.D., Harvard University
Penn GSE Faculty Susan A. Yoon
Graduate School of Education Presidential Professor
Ph.D., University of Toronto

Affiliated Faculty

Our affiliated faculty members are valued as part of our intellectual community, and students are encouraged to take their courses and to connect on research matters and for mentorship.

Haiying Li
Program Manager
Ph.D., University of Memphis

Jaclyn Ocumpaugh
Associate Director, Penn Center for Learning Analytics
Ph.D., Michigan State University

Maciej Pankiewicz
Senior Research Investigator, Penn Center for Learning Analytics
Ph.D., University of Bonn

Our Graduates

The Learning Analytics program prepares students for careers in industry, government, non-profits, higher education, and K-12 school systems, as well as to go on to pursue doctoral programs. The program prepares graduates to work in research and development in areas such as at-risk prediction, intelligent tutoring systems, and educational recommender systems. 

Admissions & Financial Aid

Please visit our Admissions and Financial Aid pages for specific information on the application requirements, as well as information on tuition, fees, financial aid, scholarships, and fellowships.

Contact Information

Contact us if you have any questions about the program.

Office of Admissions and
Financial Aid

Graduate School of Education
University of Pennsylvania
3700 Walnut Street
Philadelphia, PA 19104
(215) 898-6415

Program Contact

Haiying Li, Ph.D.
Program Manager

Please view information from our Admissions and Financial Aid Office for specific information on the cost of this program.

Most students in this program are anticipated to fund their degree through a combination of personal resources, employer benefits, and student loans.