You are here

KULLANICI TANIMLI ÜSTVERİ TANIMLAYICILARININ ZEKİ ÖĞRENME NESNELERİ ÜSTVERİ SİSTEMLERİNDE KULLANILMASI

USING OF USER-DEFINED METADATA DESCRIPTORS IN INTELLIGENT LEARNING OBJECT METADATA SYSTEMS

Journal Name:

Publication Year:

Abstract (2. Language): 
In the existing Learning Object Metadata Systems, information about user profile is determined by the authors or creators of the learning materials. This information is very important for choosing the learning materials corresponding to the knowledge level of the learners. However, the authors and the users’ opinions about the same learning object may not sometimes match. Consequently, the retrieving learning objects from the repository may not always be appropriate to the user’s knowledge level. In this paper, we propose new metadata descriptors, taking into account a user profile. Namely, we propose the descriptors “user’s knowledge level” and “relevance degree”. In this study, the method improved for determination of dependence between the users and authors’ opinions about the learning objects by using the learning object metadata descriptors is explained. Moreover, the overall description of the Intelligent Learning Object Metadata System is given.
Abstract (Original Language): 
Mevcut Öğrenme Nesneleri Üstveri Sistemlerinde kullanıcı profili hakkındaki bilgiler, genellikle öğrenme nesnelerinin geliştiricileri tarafından belirlenmektedir. Bu bilgiler, öğrencinin kendi bilgi seviyesine uygun nesneleri öğrenme nesnelerini bulması açısından oldukça önemlidir. Fakat aynı nesnenin nitelikleri hakkındaki görüşler, geliştirici ve kullanıcı (öğrencinin) açısından farklılıklar gösterebilir. Bu sebepten dolayı öğrenme nesnelerinin, yalnız “geliştirici tanımlı” üstveri değerleri doğrultusunda seçilmesi çoğu zaman istenen sonuçları vermeyebilir. Bu makalede öğrenme nesnelerinin seçilmesi için kullanıcı görüşlerini dikkate alan yeni üstveri tanımlayıcıları (“kullanıcının bilgi seviyesi” ve “uygunluk derecesi”) önerilmiştir. Bu tanımlayıcılarının kullanıldığı “Öğrenme Nesneleri Zeki Üstveri Sistemi”nin genel yapısı açıklanmış, aynı zamanda, geliştirici ve kullanıcının görüşleri arasındaki ilişkiyi belirleyen bir yöntem verilmiştir.
364-374

REFERENCES

References: 

Alami, M.E., Casel, N. & Zampunieris, D. (2008). An architecture for e-learning system with computational intelligence, The Electronic Library, 26 (3):318-28.
Brasher, A. & McAndrew, P. (2004). Human-generated learning object metadata. Springer-Verlag Berlin Heidelberg 2004.
Christos E. Alexakos, Konstantinos C. Giotopoulos, Eleni J. Thermogianni, Grigorios N. Beligiannis, Spiridon D. Likothanassis. (2006). Integrating e-learning environments with computational intelligence assessment agents, International Conference on Computer Science (ICCS 2006), Budapest, Hungary, May 26-28, 2006, 13: 233-238.
IEEE LTSC (2002). IEEE 1484.12.1-2002, Draft standard for learning object metadata, IEEE Learning Technology Standards Committee (LTSC), 15 July 2002. Retrieved April 9, 2010 from http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf
Kosba, E., Dimitrova, V. & Boyle, R. (2003). Fuzzy student modeling to advise teachers in web-based distance courses. International Journal of Artificial Intelligence Tools, Special Issue on AI Techniques in Web-Based Educational Systems, World Scientific Net, 13(2): 279-297.
Muñoz, L.S. & Oliveira, J.P.M. (2004). Adaptive web-based courseware development using metadata standards and ontologies, International Conference On Advanced Information Systems Engineering (Caise 2004).
Mosby's Medical Dictionary (2009), Elsevier, 8th edition, retrived: http://www.thefreedictionary.com/Mosby%27s%20Medical%20Dictionary%208th%...
Olševičová, K. & Mikulecký, P. (2008). Learning management system as ambient ıntelligence playground. International Journal of Web Based Communities (IJWBC), 4(3): 348-358.
Pedrazzoli, A. & Dall'acqua, L. (2009), OPUS One. An artificial intelligence - multi agent based intelligent adaptive learning environment (IALE), Learning in the Synergy of Multiple Disciplines, Vol.5794 Berlin/Heidelberg: Springer, Oct 2009 (ISBN: 978-3-642-04635-3)
Riley, S.A., Miller, L.D., Son, L.-K., Samal, A. & Nugent G. (2009). Intelligent learning object guide (iLOG): A Framework for Automatic Empirically-Based Metadata Generation , Retrieved January 10,2010, from http://cse.unl.edu/agents/ilog/downloads/ Rileyetal_ IntelligentLearningObjectGuide_final_4_4_09.pdf
Rossi, P.G. (2009). Learning environment with elements of artifi cial intelligence. Journal of e-Learning and Knowledge Society, 5(1): 191–199.
Salahli, M.A., Gasimzade, T. & Guliyev, A. ( 2010). The development of learning object metadata system: fuzzy representation of the metadata, ninth International Conference on Application of Fuzzy Systems and Soft Computing, Prague, Czech Republic, August 26-27, 2010
Salahli, M.A. & Yaşar, C. (2010). The development of learning object metadata system using ontology knowledge on computer education, 2nd International Congress Of Educational Researchs, Antalya, 29 April- 02 May, 2010.
Schaverien, L. (2003). Re-conceiving “intelligence” in learning management systems: tuning learning to theory, University of Sydney, Sydney, Australia. Retrieved 20 June, 2010 from http://sydney.edu.au/engineering/it/~aied/vol4/vol4_Schaverien.pdf
Tu, L.Y., Hsu, W.L. & Wu, S.H. (2002). A cognitive student model – an ontological approach. International Conference on Computers in Education, December 3-6, 2002, Auckland, New Zealand.

Thank you for copying data from http://www.arastirmax.com