Geodesic
Recommending Collaboratively Generated Knowledge
Computer Science and Information Systems, Tome 9 (2012) no. 2.

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With the development and adoption of information technologies in education, learners become active producer of knowledge. There is an increasing amount of content generated by learners in their learning process. These emerging learning objects (ELOs) could potentially be valuable as learning resources as well as for assessment purpose. However, the potentials also give rise to new challenges for indexing, sharing, retrieval and recommendation of such learning objects. In this research we have developed a recommender system for emerging learning objects generated in a collaborative knowledge building process and studied the implications and added values of the recommendations. We conducted two evaluations with learners to assess and improve the system�s design and study the quality and effects of the recommendations. From the evaluations, we received generally positive feedback and the results confirm the added values of the recommendations for the knowledge building process.
Keywords: Recommender systems, collaborative knowledge, knowledge building, emerging learning objects 
@article{CSIS_2012_9_2_a17,
     author = {Weiqin Chen and Richard Persen},
     title = {Recommending {Collaboratively} {Generated} {Knowledge}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {9},
     number = {2},
     year = {2012},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2012_9_2_a17/}
}
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Weiqin Chen; Richard Persen. Recommending Collaboratively Generated Knowledge. Computer Science and Information Systems, Tome 9 (2012) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2012_9_2_a17/