Abstract
The present paper introduces an original approach for the validation of learning objects (LOs) within an online Community of Practice (CoP). A social validation has been proposed based on two features: (1) the members’ assessments, which we have formalized semantically, and (2) an expertise-based learning approach, applying a machine learning technique. As a first step, we have chosen Neural Networks because of their efficiency in complex problem solving. An experimental study of the developed prototype has been conducted and preliminary tests and experimentations show that the results are significant.
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© 2013 Springer International Publishing Switzerland
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Berkani, L., Driff, L.N., Guessoum, A. (2013). Social Validation of Learning Objects in Online Communities of Practice Using Semantic and Machine Learning Techniques. In: Amine, A., Otmane, A., Bellatreche, L. (eds) Modeling Approaches and Algorithms for Advanced Computer Applications. Studies in Computational Intelligence, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-00560-7_28
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DOI: https://doi.org/10.1007/978-3-319-00560-7_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00559-1
Online ISBN: 978-3-319-00560-7
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