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Sequence-Based Approaches to Course Recommender Systems

  • Ren Wang
  • Osmar R. Zaïane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)

Abstract

The scope and order of courses to take to graduate are typically defined, but liberal programs encourage flexibility and may generate many possible paths to graduation. Students and course counselors 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 techniques, yet failed to take a student’s sequence of past courses into consideration. In this paper, 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. The result shows that all can improve the performance of students while the approach based on dependency graph contributes most.

Keywords

Recommender systems Dependency graph Process mining 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of AlbertaEdmontonCanada

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