A new framework of operation research and learning path recommendation for next-generation of e-learning services

AuthorSearch for: ; Search for:
ConferenceEUROXXVII Annual Conference, 12-15 July 2015, University of Strathclyde, Glasgow, United Kingdom
SubjectOR in Distance Learning; OR in Education; Mathematical Programming
AbstractThis work presents the contribution of operational research to education and more particularly to learning design with the implementation of a learning path recommendation system for the next generation of e-learning services. A learning design recommendation system would help learners get appropriate learning objects through an efficient learning path during their self-directed learning journey. The quantity of learning objects available is constantly growing, and millions are now available online. Therefore designing a learning path can be a tedious task that could be eased with the help of software capacities. Moreover, most of the existing recommender solutions proposed by different research communities including educational data mining are not suitable for the very large repositories of learning objects and does not take into account the complexity of the problem in their optimization process. To alleviate this difficulty, we proposed a general approach based on graph theory and mathematical programming to optimize the learning path discovery. The first step of the approach consists in reducing the search space by iteratively building sub-graphs as a succession of cliques form the targeting competencies to competencies reachable by the learner. In a second step, our mathematical model takes into account the prerequisite and gained competencies as constraints and the total competencies needed to reach the learning goal as the objective function to optimize.
Publication date
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedNo
NPARC number23000841
Export citationExport as RIS
Report a correctionReport a correction
Record identifier960ff119-8cad-4a0a-b2e9-4d9a21942f13
Record created2016-10-18
Record modified2016-10-18
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)