The Masters Program in Learning Analytics prepares students to understand and use emerging quantitative methods, drawn from computer science, statistics, and cognitive science, for handling the vast amounts of data generated by online and digital learning environments.
Students complete coursework in learning analytics and educational data mining methods, tools, and theory over the course of a year of full-time study beginning in the fall semester and concluding in the summer. Part-time study for those working in related fields is also available.
In addition to learning about relevant policy, legal, and ethical issues involved in conducting analytics on educational data, students will be challenged to use learning analytics methods to improve learning opportunities for a range of student populations.
Studying with a faculty of internationally recognized experts, students in the Master of Science in Learning Analytics program work with real-world data collected from online and digital learning environments in the K-12 and post-secondary sectors.
The program includes face-to-face and online components and opportunities for individual instruction and advice. The program has strong industry connections, which can result in internship opportunities and other experiential learning opportunities.
Entry Terms: Fall Only
Required Program Core Courses: (minimum of 5 courses for 15 points/credits)
HUDK 4050: Core Methods in Educational Data Mining
HUDK 4051: Learning Analytics: Process and Theory
HUDK 4052: Data, Learning, and Society OR HUDK 4011 Networked and Online Learning
HUDK 4054: Managing Educational Data OR HUDK 4031 Evaluation: Individuals, Groups, Institutions
HUDK 5053: Feature Engineering Studio OR HUDK 5324 Research Work Practicum
Additional Courses in Learning (HUDK): (minimum of 3 courses for 9 points/credits)
Three courses with the HUDK prefix selected in consultation with your advisor.
Courses in Statistics (minimum of 2 courses for 6 points/credits) Also satisfies the College Breadth Requirement
HUDM 4122 Probability and statistical inference OR HUDM 4125 Statistical inference
HUDM 5122 Applied regression analysis
Students with prior coursework in statistics may place out of one or more statistics courses and consider these additional options:
HUDM 5026 Introduction to data analysis in R
HUDM 5123 Linear models and experimental design
HUDM 5124 Multidimensional scaling and clustering
HUDM 5133 Causal inference for program evaluation
Students will complete an integrative capstone project, involving analysis with educational data to address a real-world problem or question.
A&HF 4090 Philosophies of education
A&HF 4192 Ethics and education
ITSF 4010 Cultural and social bases of education
ITSF 5003 Communication and culture
MSTU 4001 Technology and school change
MSTU 5001 Assessing the impact of technology in our schools
MSTU 4037 Computers and the uses of information in education
MSTU 4083 Instructional design of educational technology
MSTU 4085 New technologies for learning
MSTU 4133 Cognition and computers
MSTU 5035 Technology and metacognition
MSTU 4022 Telecommunications, distance learning, and collaborative interchange
MSTU 4039 Video games in education
MSTU 4052 Computers, problem-solving, and cooperative learning
MSTU 5005 Case-based teaching and learning in electronic environments
MSTU 5030 Intelligent computer-assisted instruction