New York University Learning Analytics and Educational Data Science Specialization

• In Event: Graduate Programs in Learning Analytics Workshop: Core Competencies, Curriculum, and Instruction

Abstract

The Learning Analytics and Educational Data Science Specialization allows students in the Digital Media Design for Learning Masters to focus their studies on how to harness the power of data to improve learning and teaching. Students learn how to design and create information and decision-making tools for educational settings that are based on data science, machine learning, and artificial intelligence.

Upon completing this specialization students will have the skills to:
Create solutions for educational settings based on the analysis and generation of data from learning tools.
Use data and analytics to better understand current learning processes and outcomes and improve them for the future.
Meaningfully critique and improve the design of learning analytics from technical, practical, social, and ethical perspectives.
Effectively communicate how interconnected infrastructure, policy, and societal factors play a role in learning analytics systems.
To complete this specialization, students take two required courses and one elective from a menu of options of relevant courses across the university.

Required Courses

EDCT-GE 2252 Theories and Principles of Learning Analytics. This course provides an introduction to uses of data in education that will help students develop their ability to evaluate data sources, perform analyses and critically evaluate applications of data use in real world educational situations. The course weaves together three strands of focus: a conceptual concern with selecting and interpreting data-based information, a technical emphasis on working with data and performing analyses, and a societal lens on understanding the opportunities, challenges and concerns that such data use presents.
EDCT- GE 2260 Building Learning Analytics Applications. In this course, students develop the technical and computational skills needed to create learning analytics applications that respond to real educational needs. The course explores the computational approaches needed to design and use algorithms to capture and automatically analyze data produced during online or face-to-face learning activities and to implement applications that provide feedback to the stakeholders of the learning process.
Sample Elective Options
APSTA-GE 2014: Statistical Analysis of Networks
APSTA-GE 2011: Supervised and Unsupervised Machine Learning
DS-GA 1011 Natural Language Processing
CS-GY 6313: Information Visualization
ITPG-GT 2941 Data w/o Borders: Data Science in the Service of Humanity
CEH-GA 3016 Data Rules: How Quantification Shapes Science, Selves, and States

NYU’s Learning Analytics Research Network (NYU-LEARN)
In addition to formal coursework, students in this specialization are encouraged to engage with NYU’s Learning Analytics Research Network through their events and workshops and by engaging in learning analytics research either through an internship on an existing faculty project or through an approved independent study with a faculty supervisor.

Capstone Thesis Project

The Digital Media Design for Learning capstone thesis project provides students with an opportunity to integrate their studies and prior knowledge and experience, while concentrating on a single project of personal-professional interest. Students specializing in Learning Analytics and Educational Data Science should consult with one of the specialization’s faculty as well as with their thesis course advisor to plan their project.

Author

• Alyssa Wise, New York University
  Presenting Author