Automated Discovery, Categorization and Retrieval of Personalized Semantically Enriched E-learning Resources

In this paper, we describe an integrated and working Elearning search system for retrieving personalized semantically enriched learning resources. Within this context, this work proposes an architecture divided into four layers: (1) Semantic Representation (knowledge representation), (2) Algorithms, which are the core engine of this study, (3) Personalization Interface to deal with information filtering, and (4) Dual representation of the semantic user profile. We use Cluster Analysis in support of an adaptive personalized search for E-learning. This work ends with an experimental evaluation of the results and an overview of future research. Evidence is found that both ersonalization and semantic enrichment are potential elements for improving an E-learning Information Retrieval System.

[1]  Lora Aroyo,et al.  Interoperability in Personalized Adaptive Learning , 2006, J. Educ. Technol. Soc..

[2]  English Speaking,et al.  The College of Engineering , 2011 .

[3]  Nicholas J. Belkin,et al.  Some(what) grand challenges for information retrieval , 2008, SIGF.

[4]  Bamshad Mobasher,et al.  Ontological User Profiles for Representing Context in Web Search , 2007, 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops.

[5]  George Karypis,et al.  Soft clustering criterion functions for partitional document clustering: a summary of results , 2004, CIKM '04.

[6]  C. Lee Giles,et al.  Discovering Relevant Scientific Literature on the Web , 2000, IEEE Intell. Syst..

[7]  Alexander Pretschner,et al.  Ontology-Based User Profiles for Search and Browsing , 2002 .

[8]  Miguel-Ángel Sicilia.  Metadata, semantics, and ontology: providing meaning to information resources , 2006, Int. J. Metadata Semant. Ontologies.

[9]  Olfa Nasraoui,et al.  Metadata domain-knowledge driven search engine in "HyperManyMedia" E-learning resources , 2008, CSTST.

[10]  Michael J. Pazzani,et al.  A hybrid user model for news story classification , 1999 .

[11]  George Karypis,et al.  Evaluation of hierarchical clustering algorithms for document datasets , 2002, CIKM '02.

[12]  Peretz Shoval,et al.  Information Filtering: Overview of Issues, Research and Systems , 2001, User Modeling and User-Adapted Interaction.

[13]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[14]  Jim Haynes,et al.  July, 2009 , 2009, The Lancet Neurology.

[15]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[16]  Amit P. Sheth,et al.  Managing Semantic Content for the Web , 2002, IEEE Internet Comput..

[17]  Ivan Koychev,et al.  Adaptation to Drifting User's Interests , 2000 .

[18]  Alexander Pretschner,et al.  Ontology based personalized search , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[19]  Tom M. Mitchell,et al.  Experience with a learning personal assistant , 1994, CACM.

[20]  I. Barry Crabtree,et al.  Identifying and tracking changing interests , 1998, International Journal on Digital Libraries.

[21]  Olfa Nasraoui,et al.  Semantic Information Retrieval for Personalized E-Learning , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.