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Reseach Article

Proposed Approach to Build Semantic Learner Model in Adaptive E-Learning

by Khaled M. Fouad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 17
Year of Publication: 2012
Authors: Khaled M. Fouad
10.5120/9377-3859

Khaled M. Fouad . Proposed Approach to Build Semantic Learner Model in Adaptive E-Learning. International Journal of Computer Applications. 58, 17 ( November 2012), 40-47. DOI=10.5120/9377-3859

@article{ 10.5120/9377-3859,
author = { Khaled M. Fouad },
title = { Proposed Approach to Build Semantic Learner Model in Adaptive E-Learning },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 17 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 40-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number17/9377-3859/ },
doi = { 10.5120/9377-3859 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:47.579973+05:30
%A Khaled M. Fouad
%T Proposed Approach to Build Semantic Learner Model in Adaptive E-Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 17
%P 40-47
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the context of E-learning, adaptive systems are more specialized and focus on the adaptation of learning content and the presentation of this content. The adaptive E-learning system focuses on how the profile data is learned by the learner and pays attention to learning activities, cognitive structures and the context of the learning material. The system controls the process of collecting data about the learner, the process of acquiring the learner profile and during the adaptation process. The Semantic Web adds structured meaning and organization to the navigational data of the current web, based on formalized ontologies and controlled vocabularies with semantic links to each other. The semantic web-based educational systems need to interoperate, collaborate and exchange content or re-use functionality. In this paper, the proposed approach aims at improving representation of a learner model during acquiring leaner profile, which is based on learner interest and learning style, in content-based approaches by performing the next steps. First step is domain concept filtering in which concepts and items of interests are compared to the domain ontology to check the relevant items to the selected learning domain using ontology based semantic similarity. Second step is incorporating semantic content into the term vectors. Term definitions and relations are used, provided by WordNet ontology, to perform domain-specific concepts as category labels for the semantic learner models. The Learning style of the learner can be acquired by using the learner behavior during utilizing the E-learning system.

References
  1. Yu, Z. , Nakamura, Y. , Jang, S. , Kajita, S. , & Mase, K. (2007), Ontology-Based Semantic Recommendation for Context-Aware E-Learning, UIC 2007, LNCS 4611, pp. 898–907, 2007, Springer-Verlag Berlin Heidelberg.
  2. Christoph, F. (2005) User Modeling and User Profiling in Adaptive E-learning Systems, Master's Thesis At Graz University of Technology.
  3. Modritscher, F. (2004). Victor Manuel Garcia-Barrios, and Christian G¨utl. The Past, the Present and the future of adaptive E-Learning. In Proceedings of the International Conference Interactive Computer Aided Learning (ICL2004), 2004. http://www. iicm. edu/iicm_papers/icl2004/adaptive_e-learning/adaptiv_e-learning. pdf.
  4. Brusilovsky, P. & Maybury, M. (2002). From Adaptive Hypermedia to the Adaptive Web. Communications of the ACM, Volume 45 Issue 5.
  5. Kritikou,Y. , Demestichas, P. , Adamopoulou. E. , Demestichas, K. , Theologou, M. & Paradia, M. (2008). User Profile Modeling in the context of web-based learning management systems. Journal of Network and Computer Applications 31 (2008) 603–627. Elsevier Ltd.
  6. Dicheva, D. (2008). Ontologies and Semantic Web for E-Learning, In : "Handbook on Information Technologies for Education and Training", 978-3-540-74155-8, Springer Berlin Heidelberg.
  7. Ghaleb, F. , Daoud, S. , Hasna, A. , ALJa'am, J. , El-Seoud, S. & El-Sofany, H. (2006). E-Learning Model Based On Semantic Web Technology, International Journal of Computing &Information Sciences Vol. 4, No. 2, August 2006, On-Line. Pages 63 – 71.
  8. Zschocke, T. & deLeon, J. (2010). Towards an Ontology for the Description of Learning Resources on Disaster Risk Reduction. WSKS 2010, Part I, CCIS 111, pp. 60–74, Springer-Verlag Berlin Heidelberg.
  9. Lee, M. , Tsai, K. & Wang, T. (2008). A practical ontology query expansion algorithm for semantic-aware learning objects retrieval. Computers & Education 50 (2008) 1240–1257. Elsevier Ltd.
  10. Ding, L. , Liu, B. & Tao, Q. (2010). Hybrid Filtering Recommendation in E-Learning Environment. 2010 Second International Workshop on Education Technology and Computer Science. 978-0-7695-3987-4/10, IEEE.
  11. Reformat, M. & Koosha, S. (2009), Updating User Profile using Ontology-based Semantic Similarity, FUZZ_IEEE 2009, Korea, August 20-24, 978-1-4244-3597-5, IEEE.
  12. So, C. Lai, C. , & Lau, R. (2009). Ontological User Profiling and Language Modeling for Personalized Information Services, 2009 IEEE International Conference on e-Business Engineering, 978-0-7695-3842-6/09, IEEE.
  13. Harb, H. & Fouad. Kh. (2010). Semantic web based Approach to learn and update Learner Profile in Adaptive E-Learning . Al-Azhar Engineering Eleventh International Conference, December PP: 23-26.
  14. Fouad, K. , Harb, H. & Nagdy, N. (2011). Semantic Web supporting Adaptive E-Learning to build and represent Learner Model. The Second International Conference of E-learning and Distance Education (eLi 2011) – Riyadh.
  15. Baishuang, Q. & Wei, Z. (2009). Student Model in Adaptive Learning System based on Semantic Web, 2009 First International Workshop on Education Technology and Computer Science, 978-0-7695-3557-9/09, IEEE, DOI 10. 1109/ETCS,466.
  16. Pan, J. , Zhang, B. , Wang, S. , Wu, G. , & Wei, D. (2007). Ontology Based User Profiling in Personalized Information Service Agent, Seventh International Conference on Computer and Information Technology, 0-7695-2983-6/07, IEEE.
  17. Pan, J. , Zhang, B. , Wang, S. & Wu, G. (2007), A Personalized Semantic Search Method for Intelligent e-Learning, 2007 International Conference on Intelligent Pervasive Computing, 0-7695-3006-0/07, IEEE , DOI 10. 1109/IPC. 2007. 48.
  18. Fouad, K. , Hogo, M. , Gamalel-Din, S. & Nagdy, N. (2010). Adaptive E-Learning System based on Semantic Web and Fuzzy Clustering. (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, December 2010.
  19. Rodr?guez, M. & Egenhofer, M. (2003), Determining Semantic Similarity among Entity Classes from Different Ontologies, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 15, NO. 2, MARCH/APRIL, 1041-4347/03, IEEE.
  20. Dragoni, M. , Pereira, C. & Tettamanzi, A. (2010). An Ontological Representation of Documents and Queries for Information Retrieval Systems, IEA/AIE 2010, Part II, LNAI 6097, pp. 555–564, Springer-Verlag Berlin Heidelberg.
  21. Pereira, C. & Tettamanzi, A. (2006). An Ontology-Based Method for User Model Acquisition. In: Ma, Z. (ed. ) Soft computing in ontologies and semantic Web. Studies in fuzziness and soft computing, pp. 211–227. , Springer-Verlag Berlin Heidelberg.
  22. Nada, Y. & Fouad, K. (2011). An Approach to improve the Representation of the User Model in the Web-Based Systems. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, P: 152 – 160, 2011.
  23. Fellbaum, C. (2010). WordNet. In R. Poli et al. Theory and Applications of Ontology: Computer Applications, (pp. 231-243). 231-243, DOI: 10. 1007/978-90-481-8847-5_10, Springer Science+Business Media B. V.
  24. Amine, A. , Elberrichi, Z. , & Simonet, M. (2010). Evaluation of Text Clustering Methods Using WordNet. The International Arab Journal of Information Technology, Vol. 7, No. 4.
  25. Boubekeur, F. , Boughanem, M. , Tamine, L. , Daoud, M. (2010). Using WordNet for Concept-Based Document Indexing in Information Retrieval, SEMAPRO: The Fourth International Conference on Advances in Semantic Processing, Pages: 151 to 157, IARIA.
  26. AMINE, A. , ELBERRICHI, Z. , SIMONET, M. & MALKI, M. (2008). WordNet-based and N-Grams-based Document Clustering: A Comparative Study, Third International Conference on Broadband Communications, Information Technology & Biomedical Applications, 978-0-7695-3453-4/08, IEEE.
  27. Brusilovsky, P. & Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems. The Adaptive Web, LNCS 4321, pp. 3 – 53, Springer-Verlag Berlin Heidelberg.
  28. Sangineto, E. (2008). An Adaptive E-Learning Platform for Personalized Course Generation". In Claus Pahl. (ed) Architecture Solutions for E-Learning Systems. IGI Publishing.
  29. Chen, S. & Zhang, J. (2008). The Adaptive Learning System based on Learning Style and Cognitive State. 2008 International Symposium on Knowledge Acquisition and Modeling. 978-0-7695-3488-6/08, IEEE.
  30. Popescu, E. , Trigano, P. & Badica, C. (2007). Adaptive Educational Hypermedia Systems: A Focus on Learning Styles. EUROCON 2007 The International Conference on "Computer as a Tool". 1-4244-0813-X/07, IEEE.
  31. Fouad, K. (2012). Semantic Retrieval and Recommendation in Adaptive E-Learning System. 1st Taibah University International Conference on Computing and Information Technology Al-Madinah Al-Munawwarah, Saudi Arabia, 19-21 Rabi II 1433 Hijri (12-14 March 2012) (ICCIT 2012).
  32. Latham, A. , Crockett, K. , McLean, D. Edmonds, B. & O'Shea, K. (2010). Oscar: An Intelligent Conversational Agent Tutor to Estimate Learning Styles. 978-1-4244-8126-2/10, IEEE.
  33. Using, S. , Ahmad, R. , Taib, S. (2010). Ontology of Programming Resources for Semantic Searching of Programming Related Materials on the Web. 978-1-4244-6716-7110, IEEE.
  34. Hong-yan, Y. , Jian-liang, X. , Mo-ji, W. & Jing, X. (2009). Development of Domain Ontology for E-learning Course, 978-1-4244-3930-0/09, IEEE.
  35. Noy N. , Sintek, M. , Decker, S. , Crubézy, M. , Fergerson, R. & Musen, M. (2001). Creating Semantic Web Contents with Protégé-2000, IEEE INTELLIGENT SYSTEMS, 1094-7167/01, IEEE.
  36. Hogeboom, M. , Lin, F. , Esmahi, L. & Yang, C. (2005). Constructing Knowledge Bases for E-Learning Using Protégé 2000 and Web Services, Proceedings of the 19th International Conference on Advanced Information Networking and Applications (AINA'05), 1550-445X/05, IEEE.
  37. G. PAOLO, F. SILVIA. (2009). Applied Data Mining for Business and Industry Second Edition, Chapter 6: Describing website visitors, ISBN: 978-0-470-05887-9 (Pbk), John Wiley.
  38. http://www. w3. org/TR/WD-logfile. html.
  39. K. Natheer, C. Chien-Chung. (2005). Web Usage Mining Using Rough Sets, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society, 0-7803-9187-X/05, IEEE.
  40. Vesin, B. , Ivanovi, M. , Klašnja-Milicevic, A. & Budimac,Z. (2012). Protus 2. 0: Ontology-based semantic recommendation in programming tutoring system. Expert Systems with Applications 39 (2012) 12229–12246. Elsevier Ltd.
  41. Sánchez-Alonso, S. , Sicilia, M. & García-Barriocanal, E. (2006). Ontologies and Contracts in the Automation of Learning Object Management Systems. In: Web-Based Intelligent E-Learning Systems: Technologies and Applications, 216-234 pp, DOI: 10. 4018/978-1-59140-729-4. ch011, IGI Global.
  42. Zatarain-Cabada, R. , Barrón-Estrada, M. , Zepeda-Sanchez, L. & Vega-Juárez, F. (2008). Authoring Learning Objects for Web-Based Intelligent Tutoring Systems. ICWL 2007, LNCS 4823, pp. 66 – 77, Springer-Verlag Berlin Heidelberg.
  43. Wu, H. (2008). Research of Internet Education System Based on Ontology. Fifth International Conference on Fuzzy Systems and Knowledge Discovery. 978-0-7695-3305-6/08, IEEE.
  44. Gkatzidou, V. & Gkatzidou, E. (2010). Exploring the development of adaptable learning objects. A practical approach. 10th IEEE International Conference on Advanced Learning Technologies. 978-0-7695-4055-9/10, IEEE.
  45. Silveira, R. & Silva, J. (2008). Building Intelligent Learning Environments Using Intelligent Learning Objects. In: Agent-Based Tutoring Systems by Cognitive and Affective Modeling. 19-42 pp, DOI: 10. 4018/978-1-59904-768-3. ch002. IGI Global.
  46. Mota, J. & Fernandes, A. (2010). Adaptivity and Interoperability in e-Learning Using Ontologies. IBERAMIA 2010, LNAI 6433, pp. 592–601, Springer-Verlag Berlin Heidelberg.
Index Terms

Computer Science
Information Sciences

Keywords

Learner Model Learner Interest Learning Style Ontology Domain Concept Filtering Semantic Similarity Semantic Learner Model