Learning Analytics
Department of - Human Development
Contact Information
212-678-4150
(212) 678-3837
Gary Natriello
Program Description
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 massive open online courses (MOOCs) and other educational domains. The degree requires 32 credits of coursework on learning analytics methods, tools, and theory, as well as key background in related areas such as cognition, educational theory, and statistics and measurement. Students are also required to complete an integrative project in which they apply data analytics to real-world large-scale educational data sets.
Degree Summary
Master of Science (32-Credits)
For a complete listing of degree requirements, please click the "Degrees" tab above
For a complete listing of degree requirements, please continue on to this program's "Degrees" section in this document
Degree Requirements
Masters of Science (32-credits)
Required Program Courses:
- HUDK 4050: Core Methods in Educational Data Mining
- HUDK 4051: Learning Analytics: Process and Theory
- HUDK 4052: Normative Perspectives on the Analysis of Learners and Learning
- HUDK 4054: Managing Educational Data
- HUDK 5053: Feature Engineering Studio
Additional Program Courses:
- HUDK 4029: Human Cognition and Learning
- HUDK 4080: Educational Psychology
- HUDK 5030: Visual Explanations
- HUDK 5035: Psychology of Media
- HUDK 5100: Supervised Research and Practice
Capstone Project:
Students will complete an integrative capstone project, involving conducting analytics on real-world educational data to solve a real-world problem or answer a real-world question.
College Breadth Requirement:
In addition, a minimum of six points in Teachers College courses oustide of HUDK are selected in consultation with an advisor. Some potential courses include:
- HUDM 4122 Probability and statistical inference
- HUDM 4125 Statistical inference
- HUDM 5026 Introduction to data analysis in R
- HUDM 5122 Applied regression analysis
- HUDM 5123 Linear models and experimental design
- HUDM 5124 Multidimensional scaling and clustering
- HUDM 5133 Causal inference for program evaluation
- ORLA 6641 Advanced topics in research methods and design
- HUDM 4050 Introduction to measurement
- HUDM 5059 Psychological measurement
- 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
Faculty List
Faculty
Lecturers
Visiting Faculty
Adjunct
Full-Time Instructors
Instructors
Cleveland E. Dodge Professor of Telecommunications & Ed.
Professor of Statistics and Education
Visiting Assistant Professor
Ruth L. Gottesman Prof. in Educ. Research
Professor of Psychology and Education
For up to date information about course offerings including faculty information, please visit the online course schedule.
Course List
HUDK 4029 Human cognition and learning
Cognitive and information-processing approaches to attention, learning, language, memory, and reasoning. Fee: $20.
HUDK 4050 Core methods in educational data mining
Prerequisite: HUDM 5122 or equivalent, or approved statistics or computer science data mining course. Methods of educational data mining focused on automated discovery of patterns in large-scale educational data, execution of methods in standard software packages, limitations of existing implementations of methods, and when and why to use these methods. Discussion of how EDM uses some of the same mathematical frameworks as traditional statistics and how their use in EDM differs.
HUDK 4051 Learning analytics: Process and theory
Prerequisite: Course in statistics is recommended. Framework for understanding the emerging field of learning analytics. Examines primary perspectives on what the field should be, including educational data mining, learning analytics and big data perspectives, and relationships to related and existing fields. Includes perspectives on philosophy and theory of design and inquiry, validity of a learning analytics analysis or model, and challenges to its effectiveness and appropriate use.
HUDK 4052 Normative perspectives on the analysis of learning and learners
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
Prerequisite: HUDK 4050. Design studio-style course covers the process of feature engineering and distillation, including brainstorming features, deciding what features to create, and criteria for selecting features for data mining. Students learn how to create features in Excel, Java, Google Refine, EDM Workbench, and other relevant tools.