Skip to main content

Course Outlines

Course outlines are provided here for your information and reference as they become available and have received the required approvals.

Available BCIT course outlines can be accessed by selecting the term, the course subject and the course number from the dropdown list(s) below. If outlines are available for your selected term and course, they will then be listed. If any of these steps don’t result in what you are looking for, it indicates that no outline is currently available. If you are looking for an outline from a current term please check back closer to the start of the course or consult another outline from a previous term.  Please keep in mind that the content from previous outlines may be different than current offerings of the course. For historical outlines not covered in this system please contact BCIT Library at 604-432-8370 to see if they have the outline you require.

The course outline is a statement of educational intent and direction, providing BCIT students with clear, concise, accurate and readily available information related to course content and administration. BCIT course outlines are governed by Policy 5403 and the creation of course outlines is subject to the procedure described in 5403-PR1.

Computer Systems - COMP


While no outlines currently exist for this course, below are the course learning outcomes/competencies.

Course Learning Outcomes/Competencies

Upon successful completion of this course, the student will be able to:

  • Define adaptive, adaptable and hybrid interfaces.
  • Describe the decision-making tasks that are included in the application's interface.
  • Identify how adaptive interfaces can be used in assistive technologies.
  • Describe user models and intelligent interface agents.
  • Identify the principles of AI driven UX.
  • Analyze and identify user models, user support, socio-organizational issues, and stakeholder requirements of HCI systems.
  • Describe various models for combining human and machine intelligence to solve computational problems.
  • Identify users' beliefs, intentions and goals for an existing system.
  • Design decision models used in user modeling which incorporate users' beliefs, intentions and goals.
  • Apply probabilistic models (e.g., the Bayesian theorem) for computing the likelihood of a certain event to happen.
  • Discuss the differences between machine learning and predictive modeling.
  • Discuss machine learning algorithms used for prediction.
  • Apply machine learning algorithms for design of user models and intelligent interface agents.
  • In preparation for the Major Project, propose and draft the design of an application that includes (a) user models based on the characteristics of users and tasks and (b) intelligent interface agents that can predict the user's future actions based on the current state of the system, the user's profile, and previous actions and beliefs.

Effective as of Fall 2020

More Course Outlines