A Learning Design Recommendation System Based on Markov Decision Processes

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Proceedings titleKDD-2011: 17th ACM SIGKDD conference on knowledge discovery and data mining
Conference17th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD) Workshop on Knowledge Discovery in Educational Data, San Diego, CA, August 21-24, 2011, August 21-24, 2011, San Diego, CA
Pages# of pages: 8
SubjectLearning design; recommendation system; learning style; Markov decision processes
AbstractAs learning environments are gaining in features and in complexity, the e-learning industry is more and more interested in features easing teachers’ work. Learning design being a critical and time consuming task could be facilitated by intelligent components helping teachers build their learning activities. The Intelligent Learning Design Recommendation System (ILD-RS) is such a software component, designed to recommend learning paths during the learning design phase in a Learning Management System (LMS). Although ILD-RS exploits several parameters which are sometimes subject to controversy, such as learning styles and teaching styles, the main interest of the component lies on its algorithm based on Markov decision processes that takes into account the teacher’s use to refine its accuracy.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number19291028
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Record identifier012e755d-1319-464d-87d0-68809bc56f2c
Record created2012-01-24
Record modified2016-05-09
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