Machine Learning in Education

What insights about student learning can be revealed from data, and how can those insights be used to improve the efficacy of educational technology? This course will cover computational approaches to the task of modeling learning and improving outcomes in Intelligent Tutoring Systems (ITS) and Massive Open Online Courses (MOOCs). We will cover theories and methodologies underpinning current approaches to knowledge discovery and data mining in education and survey the latest developments in the broad field of human learning research.

This course will be project based, where teams will be introduced to online learning platforms and their datasets with the objective of pairing data analysis with theory or implementation. Literature review will serve to add context and grounding to projects.

Suggested background includes one programming course and familiarity with one statistical/computational software package.

The study of learning in online environments is an interdisciplinary pursuit, and therefore all majors are welcomed and encouraged to bring complimentary backgrounds.

Undergraduates with the appropriate background and motivation are encouraged to enroll but must contact Associate Director of Student Affairs Catherine Cronquist Browning for enrollment permissions.

NOTE: This course is cross-listed as Education 290A. Formative Assessment in Virtual Learning Environments.

(Currently offered as Info C260F.)