A Rule-based system for Hybrid Search and Delivery of Learning Objects to Learners

Purpose – Presently, searching the internet for learning material relevant to ones own interest continues to be a time‐consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is to present a system of “hybrid search and delivery of learning objects to learners”.Design/methodology/approach – This paper presents a system of “hybrid search and delivery of learning objects to learners” that combines the use of WordNet for semantic query expansion and an approach to personalized learning object delivery by suggesting relevant learning objects based on attributes specified in the learner's profile. The learning objects are related to the learner's attributes using the IEEE LOM and IMS LIP standards. The system includes a web crawler to collect learning objects from existing learning object repositories, such as NEEDS or SMETE.Findings – The p...

[1]  Yevgen Biletskiy,et al.  Semantic Mining Based on the Learner's Preferences , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[2]  Harold Boley,et al.  A match-making system for learners and learning objects , 2005, Interact. Technol. Smart Educ..

[3]  Gwo-Jen Hwang,et al.  A Heuristic Algorithm for planning personalized learning paths for context-aware ubiquitous learning , 2010, Comput. Educ..

[4]  Dragan Gasevic,et al.  Ontologies for Effective Use of Context in e-Learning Settings , 2007, J. Educ. Technol. Soc..

[5]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[6]  Kun Hua Tsai,et al.  Personalized Learning Objects Recommendation based on the Semantic-Aware Discovery and the Learner Preference Pattern , 2007, J. Educ. Technol. Soc..

[7]  Norm Friesen,et al.  Interoperability and Learning Objects: An Overview of E-Learning Standardization , 2005 .

[8]  Steffen Staab,et al.  Supporting application development in the Semantic Web , 2003 .

[9]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[10]  Adam Pease,et al.  Towards a standard upper ontology , 2001, FOIS.

[11]  Gobinda G. Chowdhury,et al.  Introduction to Modern Information Retrieval , 1999 .

[12]  Yevgen Biletskiy,et al.  Focused Crawling for Downloading Learning Objects , 2009 .

[13]  Chih-Ming Chen,et al.  Personalized web-based tutoring system based on fuzzy item response theory , 2008, Expert Syst. Appl..

[14]  Michael Fleming,et al.  An adjustable personalization of search and delivery of learning objects to learners , 2009, Expert Syst. Appl..

[15]  Hele-Mai Haav,et al.  A Survey of Concept-based Information Retrieval Tools on the Web , 2001 .

[16]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[17]  Miaoliang Zhu,et al.  eLORM: learning object relationship mining-based repository , 2008, Online Inf. Rev..

[18]  Yevgen Biletskiy,et al.  Focused Crawling for Downloading Learning Objects - An Architectural Perspective , 2009 .

[19]  Chuni Wu,et al.  An attribute-based ant colony system for adaptive learning object recommendation , 2009, Expert Syst. Appl..

[20]  H. Li,et al.  A blended approach for search of learning objects , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[21]  Kun Hua Tsai,et al.  A practical ontology query expansion algorithm for semantic-aware learning objects retrieval , 2008, Comput. Educ..

[22]  Steffen Staab,et al.  Ontologies improve text document clustering , 2003, Third IEEE International Conference on Data Mining.

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

[24]  Yevgen Biletskiy,et al.  Building ontologies for interoperability among learning objects and learners , 2004 .

[25]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..