(306) 262-4986
Applications: Computer Science, Education Methodologies: Classification, Computational Tools for Data Science, Data Management, Data Mining, Database Systems and Infrastructure, Human-Computer Interaction, Machine Learning, Predictive Modeling Relevant Projects:

NSF

Connections:

Society for Learning Analytics Research (SOLAR); International Artificial Intelligence in Education Society (IAIED); International Educational Data Mining Society (IEDMS)

Christopher Brooks

Research Assistant Professor, School of Information

Affiliation(s):

Director, Learning Analytics and Research in the Office of Digital Education & Innovation

The basis of my work is to make the often invisible traces created by interactions students have with learning technologies available to instructors, technology solutions, and students themselves. This often requires the creation of new novel educational technologies which are designed from the beginning with detailed tracking of user activities. Coupled with machine learning and data mining techniques (e.g. classification, regression, and clustering methods), clickstream data from these technologies is used to build predictive models of student success and to better understand how technology affords benefits in teaching and learning. I’m interested in broadly scaled teaching and learning through Massive Open Online Courses (MOOCs), how predictive models can be used to understand student success, and the analysis of educational discourse and student writing.