Design of a Course Recommender System as an Application of Collecting Graduating Attributes

  • Author / Creator
    Bakhshinategh, Behdad
  • In educational research, the term of Graduating Attributes has been used for the qualities, skills and understandings a university community agrees its students would develop. Having a description of Graduating Attributes is one of the ways through which universities can display the outcomes of higher education. But can Graduating Attributes be used also to enhance the process of learning? In this thesis, we discuss how graduating attributes can be used in data mining applications to improve the learning process. An example of a data mining application can be a course recommender system which helps students to choose the courses they would participate in. In our work we have implemented this recommender system as an example of possible applications which Graduating Attributes can provide. In order to achieve such a goal we first needed to implement a tool for assessing Graduating Attributes and gather data. In spite of implementing this tool, we were not able to gather sufficient amount of data. As a result, based on the structure of data in our assessment tool, we have generated synthetic data which we have used for the evaluation of the course recommender system. The results of the recommendation improve over time as a result of having more data. The mean squared error decreases from 0.32 in second semester to 0.08 in the tenth semester.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • 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.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • ElAtia, Samira (Education)
    • ZAIANE, Osmar (Computing Science)
  • Examining committee members and their departments
    • Kanuka, Heather (Education)
    • Buro, Michael (Computing Science)