INFO 4100 - Learning Analytics
Fall. 3 credits. Student option grading.
Prerequisite: INFO 2950 or equivalent, AEM 2100 , CS 1110 . Co-meets with INFO 5101 . Demonstrated knowledge of R expected.
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.
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