Learning Analytics | Human Development

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Learning Analytics

Department of Human Development

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Program Description

The 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. 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. Students are encouraged to develop industry connections, which can result in internships and other experiential learning opportunities.

Degrees

  • Master of Science

    • Points/Credits: 32

      Entry Terms: Fall

      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 Data, Testing, and Meritocracy

      • 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

        • HUDM 5199 Programming for data science

        (Note that the two courses in statistics (HUDM) also satisfy the college breadth requirement.)

         

        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 international students  must maintain 9 points for full time status. In the last semester, they will need a “Reduced Course load” form signed by the Program Director.

        For all students: In their last semester, students will need to submit an “Intent to Graduate” form early in the semester.

         

    Faculty

    • Faculty

      • 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

    • Visiting Faculty

      • Yasemin Gulbahar Guven Visiting Assistant or Associate Professor - Learning Analytics Program

    • Emeriti

      • John B Black Cleveland E. Dodge Professor Emeritus of Telecommunications & Ed.
      • 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 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 5053 - No Title Found in Banner
      Learning Analytics Practicum is a core course of the M.S. in Learning Analytics Program and a gateway for students to transition from their education to a professional career. The course introduces principles and procedures in real-world educational data problems, provides support for students’ capstone projects with external organizations, and helps students access resources and develop skills necessary for a career in education and data science.
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