Learning Sciences & Technologies (MS)

The MS in Learning Sciences & Technologies program at WPI is for people who are passionate about improving education. In our highly regarded and well-funded program, you’ll be collecting data in real learning environments, discovering, testing, and implementing groundbreaking advances in how students learn using technology in and out of the classroom.

We’re persistent in our quest to improve learning—how students learn best and how teachers can implement those practices into their classrooms. From software design to classroom environments to curriculum organization and shifting expectations, you'll consider many influences to make learning practices successful and efficient. If you think education needs to keep up with the advances in technology, you can make big changes with your work here.

Graduate Admissions

Curriculum

Our flexible, personalized curriculum offers highly motivated students the latest courses, technology, and resources that support extensive research, and strong relationships with schools worldwide that help inform our scientific and social approach.

The master’s program combines computer science; cognitive, developmental, and educational psychology; and statistics classes to give you a solid interdisciplinary foundation of the social, emotional, cultural, and scientific influences on learning.

Whether you take classes part-time or full-time, you’ll be working with faculty and on project-based teams to see how students and teachers put the methods to use in authentic learning environments.

Find complete course descriptions and degree requirements in the Course Catalog.

Graduate Catalog

Learn Graspable Math

Similar Degrees

Master's in Computer Science

Master's in Data Science

data science

Society, Technology & Policy (BS)

Research

The extensive research experiences provided in the Learning Sciences & Technologies program encourage one-on-one mentoring relationships with our expert faculty. Our faculty projects are well-funded and highly interdisciplinary; they use a range of mixed methodologies including classroom observations, survey data, advanced quantitative methods, and educational data mining.

No matter your interests, you’ll make discoveries and implement changes that improve teaching and learning. Some recent research projects:

Using the ASSISTments tool, an adaptive online tutoring system to conduct randomized controlled trials
Using probabilistic modeling, neural networks, and other data-mining and machine learning methods to understand how students progress through wrong answers to right answers
Creating games and instructional activities with wearable embodied technologies
Using Graspable Math, a dynamic algebra system, to analyze student strategies
Examining student affect, motivation, engagement, and learning within intelligent tutoring systems

The educational technology industry needs the expertise a degree in learning sciences and technologies delivers, and the STEM-focused program at WPI offers expertise in varied areas such as intelligent tutoring, cognitive psychology, and artificial intelligence.

Graduate students in WPI’s well-funded learning sciences and technologies program work with real students and teachers in their classrooms to discover the nuances of real-world needs so they can tailor their research to help solve them.

Varied backgrounds are essential to this field—computer scientists, educators, and social scientists who are passionate about ensuring that educational practices keep pace with technological advances have an opportunity to immediately impact thousands of students with their research.

With a location in the regional hub of educational technology, WPI is in close proximity to more than 150 start-ups that aim to understand and improve the educational process with their technologies.

Although you’ll work in labs to collect, analyze, and mine data and shape platform changes, you’ll also spend time in real K-12 STEM classroom environments, talking with teachers and students and observing how your research projects and technologies can be used to advance teaching and learning.

Research & Facilities

Faculty Profiles

Neil Heffernan

Professor

, Computer Science

Neil T. Heffernan enjoys doing educational data mining and running the ASSISTments system. ASSISTments helps schools teach better. It’s a web service hosted at WPI that allows teachers to assign nightly homework or daily class work. Students get instant feedback while teachers get live reports. Professor Heffernan enjoys supervising WPI students in creating ASSISTments content and features. He has 6 dozens paper in educational data mining, and 20+ papers in comparing different ways to optimize student learning.

Erin Ottmar

Assistant Professor

, Social Science & Policy Studies

Erin Ottmar is an assistant professor of psychology and learning sciences at WPI. She received her BA in psychology and elementary education from the University of Richmond. After college, she spent several years teaching in Ecuador and Japan. In 2011, she received her PhD in Educational Psychology: Applied Developmental Science from the University of Virginia. After graduate school, spent 3 years as a post-doctoral research scientist at the University of Richmond.

Jacob Richard Whitehill

Assistant Professor

, Computer Science

My research interests are in applied machine learning, computer vision, data science and their applications to education, affective computing, and human behavior recognition. My work is highly interdisciplinary and frequently intersects cognitive science, psychology, and education. Before joining WPI, I was a research scientist at the Office of the Vice Provost for Advances in Learning at Harvard University. In 2012, I co-founded Emotient, a San Diego-based startup company for automatic emotion and facial expression recognition.

Joseph Beck

Associate Professor

, Computer Science

Joseph Beck, assistant professor of Computer Science, has been at WPI since 2007. His research focuses on educational data mining, a new discipline that develops techniques for analyzing large educational data sets to make discoveries that will improve teaching and learning. His work centers on estimating how computer tutors impact learning. He established the first workshop in the field and in 2008 was program co-chair of the first International Conference on Educational Data Mining.

Affiliations:

Social Science & Policy Studies