Sequence-based Approaches to Course Recommender Systems

• Author / Creator

  Wang, Ren

• A curriculum is a planned sequence of instructions or a view of the student’s experiences in terms of the educator’s or school’s instructional goals. However, the guidance provided by the curriculum is limited and both student and course counsellor struggle with the question of choosing a suitable course at a proper time. Many researchers have focused on making course recommendations with traditional data mining technologies, yet they failed to take a student’s history path of taking courses into consideration. In this thesis, we study sequence-based approaches for the course recommender system. First, we implement a course recommender system based on three different sequence related approaches: process mining, dependency graph and sequential pattern mining. Then, we evaluate the impact of the recommender system on undergraduate students in higher education. The result shows that all three methods can improve the performance of students in a certain scale while the approach based on dependency graph contributes most.

• Subjects / Keywords

  • Dependency Graph
  • Process Mining
  • Sequential Pattern Mining
  • Recommender Systems

• Graduation date

  Spring 2017

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3Z31P153

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.