The Learning Analytics program prepares graduate students to make data-driven decisions about education using quantitative methods drawn from computer science, statistics, and cognitive science. We study the "big data" generated by online and digital learning environments and develop new insights that benefit students, teachers, and administrators. Our students learn analyses methods through coding, statistical model building, and visualization as well as relevant policy, legal, and ethical issues involved in conducting analysis on education data. Graduates of our masters program pursue jobs in educational technology companies and startups, think tanks, and governmental data groups.
Our students complete an integrative capstone project that draws on the perspectives and skills acquired during their studies.
Katherine and Qingying built models of student state test performance to predict which students might benefit from extra help.
Anna and Xixuan investigated how learning design principles can be used to assign difficulty levels within an online educational math game at Teachley.
Yun and Haogang investigated student eye tracking data to identify patterns that might provide useful markers of student reading level at Okimo.
Lena and Mandy developed a personalized learning strategy including data and UX design at Learnabi.
Sihan and Linjin analyzed log files for patterns in student reading performance at Squiggle Park.
Sherry and Victoria designed a system for automated recommendations of project based learning lessons for teachers at Camino Education.
Melissa and Eli identified geographical patterns in student performance data at MarGrady Research.