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.
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:
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:
HUDK 4015 Psychology of thinking (3)
HUDK 4029 Human Cognition and Learning (3)
HUDK 4080 Educational psychology (3)
HUDK 5023 Cognitive development (3)
HUDK 5042 Motivation in education (3)
HUDK 5125 Cross-Cultural psychology (3)
HUDK 5100 Supervised Research and Practice (1-6)
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.
For the M.S. degree, no transfer credit is granted for work completed at other universities.
Students are expected to make satisfactory progress toward the completion of degree requirements. If satisfactory progress is not maintained, a student may be dismissed from the program. Program faculty annually review each student’s progress. Where there are concerns about satisfactory progress, students will be informed by the program faculty. If a student is performing below expectations, remedial work within an appropriate timeline may be required. If satisfactory progress is not maintained, a student may be dismissed from the program. Further policy details can be found in the Teachers College Student Handbook: https://www.tc.columbia.edu/student-handbook/
Students can apply for and be admitted to the full-time program in the fall semester only. This program takes up to 3 semesters of study.
For International Students on Visas: Each semester you need to maintain 9 points for full time status. In your last semester, you will need a “Reduced Course load” form signed by the Program Director.
For all students: In your last semester, you will need to submit an “Intent to Graduate” form early in the semester.