Master of Science (M.S.) in Learning Analytics
Your graduate studies in our program will train you to understand key learning analytics and educational data-mining (LA/EDM) methodologies and apply them to real-world problems across a variety of learning environments. In addition to learning about relevant policy, legal, and ethical issues involved in conducting analytics on educational data, you will be challenged to use learning analytics methods to change education for the better.
Sample Syllabi:
Careers:
Data analysis represents one of the fastest growing career paths, and employers in the education sector are increasingly looking to hire individuals with the skills to make data-driven decisions. The Master of Science in Learning Analytics prepares researchers and professionals for a range of careers in:
- Education technology companies and startups
- Educational evaluation
- Educational think tanks
- Data groups in city, state, and federal departments of education
Admissions Information
Master of Science
- Points/Credits: 32
- Entry Terms: Fall Only
Application Deadlines
- Spring: N/A
- Summer/Fall (Priority): January 15
- Summer/Fall (Final): Rolling
* Deadline for Fall 2022 is May 1. For details about rolling deadlines, visit our admission deadlines page.
Supplemental Application Requirements/Comments
- Online Degree Application, including Statement of Purpose and Resume
- Transcripts and/or Course-by-Course Evaluations for all Undergraduate/Graduate Coursework Completed
- Results from an accepted English Proficiency Exam (if applicable)
- $65 Application Fee
- Two (2) Letters of Recommendation
Requirements from the TC Catalog
Required Program Core Courses: (minimum of 5 courses for 15 points/credits)
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HUDK 4050: Core Methods in Educational Data Mining
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HUDK 4051: Learning Analytics: Process and Theory
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HUDK 4052: Data, Learning, and Society OR HUDK 4011 Networked and Online Learning
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HUDK 4054: Managing Educational Data OR HUDK 4031 Evaluation: Individuals, Groups, Institutions
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HUDK 5053: Feature Engineering Studio OR HUDK 5324 Research Work Practicum
Additional Courses in Learning (HUDK): (minimum of 3 courses for 9 points/credits)
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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
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HUDM 4122 Probability and statistical inference OR HUDM 4125 Statistical inference
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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:
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HUDM 5026 Introduction to data analysis in R
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HUDM 5123 Linear models and experimental design
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HUDM 5124 Multidimensional scaling and clustering
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HUDM 5133 Causal inference for program evaluation
Capstone Project:
Students will complete an integrative capstone project, involving analysis with educational data to address a real-world problem or question.
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A&HF 4090 Philosophies of education
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A&HF 4192 Ethics and education
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ITSF 4010 Cultural and social bases of education
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ITSF 5003 Communication and culture
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MSTU 4001 Technology and school change
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MSTU 5001 Assessing the impact of technology in our schools
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MSTU 4037 Computers and the uses of information in education
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MSTU 4083 Instructional design of educational technology
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MSTU 4085 New technologies for learning
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MSTU 4133 Cognition and computers
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MSTU 5035 Technology and metacognition
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MSTU 4022 Telecommunications, distance learning, and collaborative interchange
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MSTU 4039 Video games in education
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MSTU 4052 Computers, problem-solving, and cooperative learning
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MSTU 5005 Case-based teaching and learning in electronic environments
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MSTU 5030 Intelligent computer-assisted instruction