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A Recommender System Architecture for Instructional Engineering

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5288)

Abstract

In recent years, Recommender System’s models and techniques, have been applied in e-Learning and main efforts are been centered in learners, their guide and their success when using Learning Management Systems and other Web-based and virtual artifacts. Several techniques were adapted or even developed for this purposes. Our current project is concerned to the need to develop a Recommender System Architecture that may assist teachers in their e-Learning design practices. Instructional Design Methods, Learning Theories, Efficient Searching Methods and Tools and Meta-data Managing, are examples of the necessary knowledge and abilities. In this document we are centered in presenting the overall system architecture and describing their main parts. We are also reporting actual results. Due to it´s wide conception, this project involves Knowledge Engineering, Software Engineering, Machine Learning, Semantic Web Searching, and Data Mining models and tools.

Keywords

  • Recommender System
  • Instructional Design
  • Query Expansion
  • Learn Management System
  • Data Mining Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2008 Springer-Verlag Berlin Heidelberg

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Prieto, M.E., Menéndez, V.H., Segura, A.A., Vidal, C.L. (2008). A Recommender System Architecture for Instructional Engineering. In: Lytras, M.D., Carroll, J.M., Damiani, E., Tennyson, R.D. (eds) Emerging Technologies and Information Systems for the Knowledge Society. WSKS 2008. Lecture Notes in Computer Science(), vol 5288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87781-3_35

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  • DOI: https://doi.org/10.1007/978-3-540-87781-3_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87780-6

  • Online ISBN: 978-3-540-87781-3

  • eBook Packages: Computer ScienceComputer Science (R0)