INFO 4100

INFO 4100

Course information provided by the Courses of Study 2020-2021.

Technology has transformed how people teach and learn today. It also offers unprecedented insight into the mechanics of learning by collecting detailed interaction and performance data, such as in online courses and learning management systems like Canvas. At the intersection of education and data science, learning analytics are used to make sense of these data and use them to improve teaching and learning. This course blends learning theories and methodologies covering a wide range of topics with weekly hands-on activities and group projects using real-world educational datasets. You will learn how learning works, major theories in the learning sciences, and data science methods. Students collect and analyze their own learning trace data as part of the course. Learning outcomes: Students will learn to articulate key ideas in the learning sciences; articulate the potential benefits and dangers of learning analytics for students, teachers, and institutions; choose and apply appropriate methods for analyzing different kinds of educational data and be able to articulate why; and interpret the results of basic learning analytics.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: INFO 2950 or equivalent, AEM 2100, CS 1110.

Distribution Category (SBA-AS, SDS-AS)

Comments Demonstrated knowledge of R expected. In Fall 2020, the course is offered asynchronously (no live instruction) and with many office hours to accommodate students in all timezones.

View Enrollment Information

Enrollment Information
Syllabi: none
  •   Regular Academic Session. 

  • 3 Credits Stdnt Opt

  • 11576INFO 4100  LEC 001

  • Instruction Mode: Distance Learning-Asynchronous
    Prerequisites: Must have completed INFO2950 (or equivalent), a statistics course, and a programming course. Prior experience with R is helpful.