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      • HARVEST
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      Individualized selection of learning objects

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      Thesis_Final_JianLiu.pdf (719.8Kb)
      Date
      2009
      Author
      Liu, Jian
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Rapidly evolving Internet and web technologies and international efforts on standardization of learning object metadata enable learners in a web-based educational system ubiquitous access to multiple learning resources. It is becoming more necessary and possible to provide individualized help with selecting learning materials to make the most suitable choice among many alternatives. A framework for individualized learning object selection, called Eliminating and Optimized Selection (EOS), is presented in this thesis. This framework contains a suggestion for extending learning object metadata specifications and presents an approach to selecting a short list of suitable learning objects appropriate for an individual learner in a particular learning context. The key features of the EOS approach are to evaluate the suitability of a learning object in its situated context and to refine the evaluation by using available historical usage information about the learning object. A Learning Preference Survey was conducted to discover and determine the relationships between the importance of learning object attributes and learner characteristics. Two weight models, a Bayesian Network Weight Model and a Naïve Bayes Model, were derived from the data collected in the survey. Given a particular learner, both of these models provide a set of personal weights for learning object features required by the individualized learning object selection. The optimized selection approach was demonstrated and verified using simulated selections. Seventy simulated learning objects were evaluated for three simulated learners within simulated learning contexts. Both the Bayesian Network Weight Model and the Naïve Bayes Model were used in the selection of simulated learning objects. The results produced by the two algorithms were compared, and the two algorithms highly correlated each other in the domain where the testing was conducted. A Learning Object Selection Study was performed to validate the learning object selection algorithms against human experts. By comparing machine selection and human experts’ selection, we found out that the agreement between machine selection and human experts’ selection is higher than agreement among the human experts alone.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Greer, J. E. (Jim)
      Copyright Date
      2009
      URI
      http://hdl.handle.net/10388/etd-05122009-093502
      Subject
      learning object
      selection
      metadata
      Bayesian network
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