Data, Learning, and Society | Human Development

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Data, Learning, and Society

Department of Human Development

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Program Description

The online Masters of Science in Data, Learning, and Society Program offers students an opportunity to acquire basic techniques in statistics and data science as they apply to the study of learning in the context of the growing reliance on data as a foundation for the operation of the learning sector. 

The program aims to equip students with the skills, techniques, and perspectives for the socially responsible application of data to understand and support learning in contemporary society. The program prepares students for positions as analysts of learning programs and activities in public and private institutions. Graduates will be able to approach their work with a solid grounding in contemporary data techniques, an understanding of the unique aspects of empirical work on learning, and an appreciation of the social and ethical issues presented by the growing availability of data on learners and learning. 

The program: 1) introduces students to statistical and data science fundamentals, 2) provides a solid understanding of the study of learning in its various forms, and 3) allows students to consider both the possibilities and the problems attendant to the growing use of learning data. 

The program is particularly attractive to students who are recent college graduates, early career professionals, and established educators who wish to develop a more sophisticated understanding of the potential of the growing availability of data on learning.

Degrees

  • Master of Science

    • Points/Credits: 32

      Entry Terms: Fall

      Degree Requirements

      Program Requirements Master of Science: 32 Points Total

      Core Requirements (12 credits): 

      • HUD 4120 - Methods of Empirical Research (3 credits)

      • HUDM 4122 - Probability and Statistical Inference (3 credits)

      • HUDK 4029 - Human Cognition and Learning (3 credits)

      • HUDK 4052 - Data, Learning, and Society (3 credits)

      Two Additional Courses in Data (6 credits): 

      • HUDK 4050 - Core Methods in Educational Data Mining (3 credits)

      • HUDK 4053 - Managing Educational Data (3 credits)

      • HUDM 5026 - Intro to Data Analysis in R (3 credits)

      • HUDM 5059 - Psychological Measurement (3 credits)

      • HUDM 5122 - Applied Regression Analysis (3 credits)

      One Additional Course in Learning (3 credits):

      • HUDK 4011 - Networked and Online Learning (3 credits)

      • HUDK 5053 - Cognitive Development (3 credits)

      • HUDK 5037 - The Psychology of E-Learning in Business and Industry (3 credits)

      One Additional Course in Social Impact (3 credits):

      • HUDK 4031 - Data, Testing, and Meritocracy (3 credits)

      • HUDK 5025 - Cross-Cultural Psychology (3 credits) 

      • HUDK 5035 - Poverty, Inequality, and Child Development (3 credits)

      • HUDK 5040 - Development and Psychopathology: Atypical Contexts and Populations

       

      Breadth Requirement (6 credits): 

      Two courses approved by your program advisor draw from other online courses across the College.

       

      Masters Culminating Requirement: Integrated Project 

      • HUDK 5324 - Research Practicum (2 credits)

      • Students will complete a capstone project drawing on their studies in the program.

       

      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/.

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

  • HUD 4120 - Methods of empirical research
    An introduction to the methods of scientific inquiry, research planning, and techniques of making observations and analyzing and presenting data.
  • HUDK 4029 - Human cognition and learning
    Cognitive and information-processing approaches to attention, learning, language, memory, and reasoning.
  • HUDK 4031 - Data, Testing, and Meritocracy
    Individuals in modern societies live in a world in which evaluation is ubiquitous. More and more aspects of our performances are subject to informal and/or formal assessment. Everything from our health as infants, to our performance in schools as youngsters, our potential to benefit from higher education, and our capacity to contribute in the workplace is evaluated. This course examines the social dimensions of the development and operation of different kinds of evaluation systems in modern societies. Major topics include the social, political, and intellectual contexts for evaluation, the institutional bases of evaluation activities, the social settings in which evaluation takes place, and the effects of evaluations on individuals and groups
  • 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 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 5324 - Research Practicum
    Students learn research skills by participating actively in an ongoing faculty research project.
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