Optimization of Learning Performance on Multimedia Items using Machine-learning Methods

Castle Rock Research provides quality curriculum-based educational resources to students, parents and educators, both online and in print. Reinforcement has been well-studied in psychology and proven to be an efficient learning technique. However, learning behavior varies from one individual to another. It is important to understand one’s learning capabilities and response pattern so as to customize the reinforcement process and to optimize the performance of individual learners. Thus, this project applies machine-learning methodology and involves the design of a statistical model to assess student responses with a goal to optimize individual learning performance.

Faculty Supervisor:

Dr. Irene Cheng

Student:

Alexey Badalov

Partner:

Castle Rock Research Corp.

Discipline:

Computer science

Sector:

Education

University:

University of Alberta