Learning Analytics

Department of Human Development

Program Description

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.

Degrees

Group by Degree Type

Master of Science
- Learning Analytics
  Master of Science
  
  Points/Credits: 32
  
  Entry Terms: Fall Only
  
  Degree Requirements
  Degree Requirements
  
  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
  
  Capstone Project:
  
  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.
  
  Satisfactory Progress
  
  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/
  
  Full-time Program
  
  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.

Faculty

Faculty
- John B Black Cleveland E. Dodge Professor of Telecommunications & Ed.
- James E Corter Professor of Statistics and Education
- Bryan Sean Keller Associate Professor of Practice in Applied Statistics
- Gary J Natriello Ruth L. Gottesman Professor in Educational Research
- Renzhe Yu Assistant Professor, Learning Analytics / Educational Data Mining
Emeriti
- Barbara Tversky Professor Emerita of Psychology and Education

Courses

HUDK 4011 - Networked and Online Learning

The course explores the social dimensions of online learning. The course begins by reviewing the uniquely social dimensions of learning in general and then turns to an examination of the transition to the information age that has made online or networked learning possible. The course next covers how traditional social forms such as classrooms, schools, professions, and libraries have been represented in online learning venues followed by consideration of new and emerging social forms such as digital publishing, social networks and social media, adaptive learning technologies, and immersive and interactive environments. The course concludes by examining macro-level factors that shape the opportunities for online learning.
HUDK 4029 - Human cognition and learning

Cognitive and information-processing approaches to attention, learning, language, memory, and reasoning.
HUDK 4050 - Core methods in Educational Data Mining

The Internet and mobile computing are changing our relationship to data. Data can be collected from more people, across longer periods of time, and a greater number of variables, at a lower cost and with less effort than ever before. This has brought opportunities and challenges to many domains, but the full impact on education is only beginning to be felt. Core Methods in Educational Data Mining provides an overview of the use of new data sources in education with the aim of developing students’ ability to perform analyses and critically evaluate their application in this emerging field. It covers methods and technologies associated with Data Science, Educational Data Mining and Learning Analytics, as well as discusses the opportunities for education that these methods present and the problems that they may create. The overarching goal of this course is for students to acquire the knowledge and skills to be intelligent producers and consumers of data mining in education. By the end of the course students should be able to systematically develop a line of inquiry utilizing data to make an argument about learning and be able to evaluate the implications of data science for educational research, policy, and practice.
HUDK 4051 - Learning Analytics: Process and theory

Learning Analytics, Theory & Practice builds on HUDK 4050 Core Methods in Educational Data Mining to provide advanced techniques in the use of new data sources in education with the aim of developing students’ ability to perform analyses and critically evaluate their application in this emerging field. It covers methods and technologies associated with data science, machine learning and learning analytics, as well as discusses the opportunities for education that these methods present and the problems that they may create.
HUDK 4052 - Data, Learning, and Society

Introduction to multiple perspectives on activities connected to progress in our capacity to examine learning and learners, represented by the rise of learning analytics. Students develop strategies for framing and responding to the ranges of values-laden opportunities and dilemmas presented to research, policy, and practice communities as a result of the increasing capacity to monitor learning and learners.
HUDK 4054 - Managing education data

Attaining, compiling, analyzing, and reporting data for academic research. Includes data definitions, forms, and descriptions; data and the research lifecycle; data and public policies; and data preservation practices, policies, and costs.
HUDK 5030 - Visual explanations

Surveys production and comprehension of visualizations ranging from ancient cave paintings and petroglyphs to diagrams, charts, graphs, comics, picture books, photographs, gesture, and film to extract and apply techniques for conveying objects, actions, forces relations, and emotions, meanings that are both inherently visible and non-visible. Implications for education, art, media, and HCI are drawn.
HUDK 5053 - Feature engineering studio

Feature Engineering Studio is a core course of the Learning Analytics Program and preference is given to students within this course of study. FES is a design studio style course that tackles real world data problems associated with technology use in education. Students will work in groups with outside organizations on data projects pertinent to educational problems. They will be required to respond to briefs supplied by the organizations and perform all parts of the workflow to generate data solutions for those organizations including, data cleaning and access, feature engineering and distillation, visualization, and final deliverables.

Learning Analytics | Human Development