Introduction to Linear and Matrix Algebra

Edfinity is supported by the National Science Foundation


This is an online homework companion to Introduction to Linear and Matrix Algebra. It comprises hundreds of algorithmic problems carefully organized into problem sets mapped to textbook sections. Use this course as-is, or customize at any level. You can mix-and-match problems from other catalog courses, add problems from the Edfinity problem repository, or write your own.

How to use this course

  1. Homework: Assign high quality problems with hints and personalized feedback to develop problem-solving skills.
  2. Testing: Create summative secure online quizzes and tests in minutes. 
  3. Supplementary resources: Embed videos, class notes, and applets alongside assignments.
  4. Intervention: Use rich analytics to identify and monitor at-risk students for timely intervention.
  5. Analytics: Drill down into student performance and identify problematic or difficult topics.
  6. Flipped classroom: Assign pre-class assignments. Save precious class time for discussions.
  7. Emporium classes: Use Edfinity for individual/group work for large enrollment sections in labs.
Interactive, algorithmic problems
Algebraic, graphing, open response; randomized variants, hints, and tips
Adaptive learning and personalization
Each student receives personalized support
to fill learning gaps.
Corequisite course structures
Use pre-built corequisite content,
or create your own.
LMS integration and accessibility
Connect to your LMS in minutes. VPAT here.
Import and author WeBWorK problems.


  1. Edfinity Demo
  2. 1 1 Vectors And Vector Operations
  3. 1 2 Lengths, Angles, And The Dot Product
  4. 1 3 Matrices And Matrix Operations
  5. 1 4 Linear Transformations
  6. 1 A Areas, Volumes, And The Cross Product
  7. 1 B Paths In Graphs
  8. 2 1 Systems Of Linear Equations
  9. 2 2 Elementary Matrices And Matrix Inverses
  10. 2 3 Subspaces, Spans, And Linear Independence
  11. 2 4 Bases And Rank
  12. 2 A Linear Algebra Over Finite Fields
  13. 2 B Linear Programming
  14. 2 C More About The Rank
  15. 2 D The Lu Decomposition
  16. 3 1 Coordinate Systems
  17. 3 2 Determinants
  18. 3 3 Eigenvalues And Eigenvectors
  19. 3 4 Diagonalization
  20. 3 A More About Determinants
  21. 3 B Power Iteration
  22. 3 C Complex Eigenvalues Of Real Matrices
  23. 3 D Linear Recurrence Relations