Quality Rating and Recommendation of Learning Objects

Vivekanandan Kumar, John Nesbit, Philip Winne, Allyson Hadwin, Dianne Jamieson-Noel, Kate Han

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)


The unceasing growth of the Internet has led to new modes of learning in which learners routinely interact on-line with instructors, other students, and digital resources. Much recent research has focused on building infrastructure for these activities, especially to facilitate searching, filtering, and recommending on-line resources known as learning objects. Although newly defined standards for learning object metadata are expected to greatly improve searching and filtering capabilities, learners, instructors, and instructional developers may still be faced with choosing from many pages of object listings returned from a single learning object query. The listed objects tend to vary widely in quality. With current metadata and search methods, those who search for learning objects waste time and effort groping through overwhelming masses of information, often finding only poorly designed and developed instructional materials. Hence, there is a clear need for quality evaluations prior to making a recommendation that can be communicated in a coherent, standardized format to measure the quality of learning objects.

Original languageEnglish
Title of host publicationAdvanced Information and Knowledge Processing
Number of pages37
Publication statusPublished - 2007

Publication series

NameAdvanced Information and Knowledge Processing
ISSN (Print)1610-3947
ISSN (Electronic)2197-8441


  • Bayesian Belief Network
  • Learning Object
  • Parent Node
  • Quality Rating
  • Quality Review


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