CMPUT 664 - e-Learning, Adaptation, & Analytics (formerly Computational Theory Building for Education)

Overview

This course focuses on how educational data can be analyzed and used to support adaptation. It will address how methods from artifical intelligence, machine learning, and information visualization can be combined with what we know about how people learn. Topics will include learner modelling, educational data mining, learning analytics and dashboards, computer-supported collaborative learning, intelligent tutoring systems, and the use of sensors and other algorithms for processing learner artefacts and behavioural data.

This is a project-based course where students will

  • Apply computational methods to educational data to describe or predict student learning
  • Use data to design a visualization to support common teaching tasks, or
  • Develop an adaptive feature that can be integrated into systems like eClass.

Objectives

Students will

  1. Learn to integrate sociological, psychological, or educational theories into their analysis of user data;
  2. Demonstrate an understanding of the intricacies of working with user data in learning settings through their review of the literature;
  3. Demonstrate their ability to assess and use previous research through their review of the literature and their identification of a research project;
  4. Demonstrate a deep understanding of at least one theory and one computational technique through their ability to explain these for generalist audiences; and
  5. Design and conduct research.

Course Work

  • assignments
  • projects
  • participation
  • presentations