INFO 4100 - Learning Analytics

Fall. 3 credits. Student option grading.

Prerequisite: INFO 2950 or equivalent, AEM 2110, CS 1110. Demonstrated knowledge of R expected.

R. Kizilcec.

How is technology transforming the ways we learn and teach? How can all of the interaction and performance data that are generated from online courses, learning management systems, and student discussion forums be used effectively? This introductory course on Learning Analytics provides a survey of learning science theories (active learning, modalities, Bloom’s taxonomy, metacognition, self-regulated learning) and educational data science methods (predictive modeling, classification, regression, natural language processing, causal inference). Students will 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.