A rule-based recommender system to suggest learning tasks

Hazra Imran, Mohammad Belghis-Zadeh, Ting Wen Chang, Kinshuk, Sabine Graf

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

4 Citations (Scopus)


Learner-centered learning can be defined as an approach to learning in which learners choose the topic to study and learning tasks. Because of available choices, learners can find it difficult to make a decision about which of the topics/tasks would be more appropriate for them. Identifying other learners with similar characteristics and then considering the tasks that worked well, makes it possible to suggest appropriate tasks to a learner. Based on this concept, we introduce a rule-based recommender system that supports learner-centered learning and helps learners to select learning tasks that are most suitable for them, with the focus on maximizing their learning.

Original languageEnglish
Title of host publicationIntelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings
Number of pages2
Publication statusPublished - 2014
Event12th International Conference on Intelligent Tutoring Systems, ITS 2014 - Honolulu, HI, United States
Duration: 5 Jun. 20149 Jun. 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8474 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Intelligent Tutoring Systems, ITS 2014
Country/TerritoryUnited States
CityHonolulu, HI


  • Learning Management Systems
  • Personalization
  • Recommender System


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