İlhami ÇOLAK, Şeref SAĞIROĞLU, H. Tolga KAHRAMAN

Geleneksel web tabanlı öğretim sistemlerinden uyarlanır öğretim sistemine geçiş: UHÖS için tasarım yaklaşımlarının incelenmesi

Geleneksel bilgisayar destekli öğretim sistemleri kullanıcıların bireysel farklılıklarını dikkate almaksızın tüm kullanıcılar için aynı öğretim stratejisini kullanarak statik sayfalardan oluşan öğretim materyalini öğrencilere sunmaktadırlar. Bu durum, bilgisayar destekli öğretim sistemlerinin sınıf ortamında gerçekleştirilen yüz-yüze öğretim yöntemine alternatif olamadığı gibi, öğretim etkinliği açısından kabul edilebilir bir kazanımın sağlanamamasına sebep olmaktadır. Bu makalede, bilgisayar destekli öğretim sistemlerinde öğrenim verimliliğini üst düzeye çıkartan, geleneksel sistemlerden tamamen farklı bir mimari yapıya ve tasarım yaklaşımına sahip Uyarlanır Hipermedya Öğretim Sistemi (UHÖS) incelenmektedir. Çalışmada genel bir UHÖS mimarisi verilmiştir. UHÖS’ün merkezi bileşeni olan öğrenci modelinin oluşturulması ve bu model içerisinde kullanıcının bilgi alanı hakkındaki bilgi düzeyinin tespit edilmesi örnek bir uygulama ile açıklanmıştır. UHÖS'ün geleneksel sistemlere göre üstünlükleri sunulmuş ve mevcut çalışmalar değerlendirilmiştir.

Transition to adaptive education hypermedia systems from web based educational systems: Review of design approaches for the AEHS

Traditional computer-aided tutoring systems provide an educational material that includes static pages for students, but they do not consider the differences among students. So computer-aided education systems might not be alternative for traditional education in the classroom as well as obtain acceptable profit in point of the instructional quality in education. In this paper, Adaptive Educational Hypermedia System (AEHS) has been observed because of having different architecture and design approach, and making superior the instructional efficiency on the computer aided tutoring systems. General architecture of AEHS is also presented. Building up the student model that is a central component of the AEHS, and determining the knowledge level of user about the domain are explained with an application. Moreover the superiorities of AEHS according to computer-aided tutoring systems are presented and the existing studies about the AEHS are evaluated.

PDF

___

1. Brusilovsky, P., “Adaptive navigation support in educational hypermedia: the role of student knowledge level and the case for metaadaptation”, British Journal of Educational Technology”, Vol. 34, No 4, 487–497, (2003).
2. Anderson, J. R., “The Expert Module”, Foundations of Intelligent Tutoring Systems, 21-53, (1988).
3. Kavcic, A., "Fuzzy User Modeling for Adaptation in Educational Hypermedia", IEEE Transactions on Systems, Applications and Reviews, Vol. 34, No. 4, (2004).
4. Cao, L., “Designing Web-Based Adaptive Learning Environment: Distils as an Example”, Ph. D. Thesis Faculty of New Jersey Institute of Technology, (2001).
5. Rubim de Assis, A. S. F., “Inferring Dynamic Learner Behavior for User Modeling in Continuously Adapting Hypermedia”, Ph. D. Thesis Faculty of Rensselaer Polytechnic Institute Troy, New York, (2006).
6. Brusilovsky, P., “Developing adaptive educational hypermedia systems: from design models to authoring tools”, School of Information Sciences University of Pittsburgh Pittsburgh PA 15260, (2003).
7. Kahraman, H. T., Colak, I., Sagiroglu, S., “A Web Based Adaptive Educational System”, Sixth International Conference on Machine Learning and Applications, IEEE computer society, 286- 291, (2007).
8. Brusilovsky, P., “Hypadapter: An Adaptive Hypertext System for Exploratory Learning and Programming”, Adaptive Hypertext and Hypermedia, Kluwer Academic Publishers, 117- 140, (1996).
9. Ihsak, Z., Arshad M. M. R., Sumari P., “Adaptive Hypermedia System in Education (review of available technologies)”, IEEE ICICS-PCM Singapore, 15-18, (2003).
10. Fan, J. P., Macredie. R. D., “Gender Differences and Hypermedia Navigation: Principles for Adaptive Hypermedia Learning Systems”, Advances in Web-Based Education: Personalized Learning Environments Chapter 1, Hershey, PA, USA: Information Science Publishing, 1-21, (2005).
11. De Bra, P., Houben, G. J., and Wu, H., “AHAM: A Dexter-based reference model for adaptive hypermedia applications”, Proceedings of the 10th ACM Conference on Hypertext and Hypermedia New York: ACM Press, 147–156, (1999).
12. Halasz, F. G., Schwartz, M., “The Dexter Hypertext Reference Model”, NIST Hypertext Standardization Workshop Gaithersburg, 1-40, (1990).
13. Brusilovsky, P., “Methods and techniques of adaptive hypermedia” User Modeling and User Adapted Interaction, 87-129, (1996).
14. Brusilovsky, P., “User Modeling and User-Adapted Interaction”, Adaptive Hypermedia, 11, (2001).
15. Karampiperis, P., Sampson, D., “Adaptive Resources Sequencing in Educational Hypermedia Systems”, Educational Technology and Society, 128-147, (2005).
16. Çolak, İ., Sagiroglu, S., Kahraman, H. T., “Architecture of Web Based Adaptive Educational Hypermedia System”, Second International Conference on Innovations in Learning for the Future 2008 e-Learning, 358- 365, (2008).
17. Armani. J., Botturi. L., “Bridging the Gap with MAID: A Method for Adaptive Instructional Design”, Advances in Web-Based Education: Personalized Learning Environments, Hershey, PA, USA: Information Science Publishing, 147-178, (2005).
18. Magoulas, G. D., Papanikolaou, K. A., Grigoriadou, M., “Neuro-fuzzy Synergism for PlNNing the Content in a Web-based Course”, Informatics 25, 39-48, (2001).
19. Papanikolaou, K. A., Grigoriadou, M., Kornilakis, H., Magoulas, G. D., “Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE”, User Modeling and User Adapted Interaction, Kluwer Academic Publishers, 213-267, (2003).
20. Castillo, G., Gama, J., Breda, A. M., “An Adaptive Predictive Model for Student Modelling”,Advances in Web-Based Education: Personalized Learning Environments, Hershey, PA, USA: Information Science Publishing, 70- 93, (2005).
21. Henze, N., Naceur, K., Nejdl, W., Wolpers, M., “Adaptive hyperbooks for constructivist teaching”, Künstliche Intelligenz, (4), (1999).
22. Hockemeyer, C., Held, T., & Albert, D., RATH - "A relational adaptive tutoring hypertext WWW-environment based on knowledge space theory”, Proceedings of CALISCE'98, 4th International conference on Computer Aided Learning and Instruction in Science and Engineering, June 15-17, (1998).
23. Neuman, G., & Zirvas, J., SKILL - A scalable internet-based teaching and learning system. In H. Maurer, & R. G. Olson (Eds.), Proceedings of WebNet'98, World Conference of the WWW, Internet, and Intranet, November 7-12, (1998).
24. Weber, G., Kuhl, H.-C., & Weibelzahl, S., “Developing adaptive internet based courses with the authoring system NetCoach” Proceedings of Third workshop on Adaptive Hypertext and Hypermedia, July 35-48, (2001).
25. Pilar da Silva, D., Durm, R. V., Duval, E., & Olivié, H., “Concepts and documents for adaptive educational hypermedia: a model and a prototype”, Computing Science Reports, Eindhoven University of Technology, 35-43. (1998).
26. De Bra, P., & Ruiter, J.-P., “AHA! Adaptive hypermedia for all”, Proceedings of WebNet'2001, World Conference of the WWW and Internet, 262-268, (2001).
27. Vassileva, J., “DCG + GTE: Dynamic Courseware Generation with Teaching Expertise”, Instructional Science, 317-332, (1998).
28. Kelly, D., Tangney, B., “ ‘First Aid for You’: Getting to know your Learning Style using Machine Learning”, Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05), (2005).
29. Martinez, F. E., Chen, S.Y., Liu, X., “Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia”, IEEE Transactions On Systems, Applications And Reviews, Vol. 36, No.6, 734-749, (2006).
30. Martinez, F. E., Magoulas, G., Chen, S., Macredie, R., “Modeling human behavior in user-adapted systems: Recent advances using soft computing techniques”, Expert Systems with Applications ELSEVIER, 320-329, (2005).
31. Lee, C-S., Singh, Y. P., “Student modeling using principal component analysis of SOM clusters”, Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT’04), 480-484, (2004).
32. Nykanen, O., “Inducing Fuzzy Models for Student Classification”, Educational Technology & Society, 223-234, (2006).
33. Wenger, E., "Artificial intelligence and tutoring systems", Computational approaches to the communication of knowledge, ACM SIGCHI Bulletin, 71-73, (1988).
34. Brusilovsky, P., Eklund, J., Schwarz, E., “Webbased Education for All: A Tool for Development Adaptive Courseware”, Computer Networks and ISDN Systems, Proceedings of Seventh International World Wide Web Conference, 291-300, (1998).
35. De Bra, P., Aerts, A., Berden, B., de Lange, B., Rousseau, B., Santic, T., Smits, D., Stash, N., “AHA! The Adaptive Hypermedia Architecture”, HT’03, Nottingham, United Kingdom, ACM 1- 58113-704-4/03/0008, 1-4, (2003).
36. Brusilovsky, P., Anderson, J., "An adaptive system for learning cognitive psychology on the Web", Proceedings of WebNet'98, World Conference of the WWW, Internet, and Intranet, 92-97, (1998).
37. Millan, E., De-La-Cruz, J. L. P., “A Bayesian Diagnostic Algorithm for Modeling and its Evaluation”, User Modeling and User Adapted Interaction, Kluwer Academic Publishers, 281- 330, (2002).
38. Castillo, G., Gama, J., & Medas, P., "Adaptation to drifting concepts", Progress in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Springer Verlag, (2003).
39. Wludyka. P. S., Jacobs. S. L., “Runs Rules and PCharts for Multistream Binomial Processes”, Communications in Statistics -Simulation and Computation, 97–142, (2002).
40. Lucas. J. M., Davis. D. J., Saniga. E. M., “Detecting improvement using Shewhart attribute control charts when the lower control limit is zero”, IEEE Transactions, 38:8, 699– 709, (2007).
41. Wang. Z., Ma. W., “Design of an Optimum Adaptive Control Chart for Attributes”, Proceedings of the 2003 Systems and Information Engineering Design Symposium, 213-219, (2003).
42. Tsiriga, V., Virvou, M., “A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems”, User Modeling and User-Adapted Interaction, Pages: 289-316, (2004).
43. Mitchell. T. M., “Machine Learning”, McGraw- Hill Science/Engineering/Math, 154-184, (1997).
44. Conati. C., Gertner. A., Vanlehn. K., “Using Bayesian Networks Uncertainty in Student Modeling”, www.cs.ubc.ca/~conati/mypapers/ umuai2002.pdf, 1-42, (2002).
45. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K., “The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users”, Adaptive Systems & Interaction Microsoft Research, 1-10, (1998).
46. Conati, C., Gertner, A. S., Van Lehn, K., Druzdzel, M. J., “On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks”, Proceedings of the Sixth International Conference on User Modeling (UM-97), 1-12, (1997).
47. Hibou, M., Labat, J. M., “Embedded Bayesian network student models”, Information Technology Based Higher Education and Training, ITHET 2004. Proceedings of the Fifth International Conference, IEEE, 468-472, (2004).
48. Stern, M. K., "Using Adaptive Hypermedia and Machine Learning to Create Intelligent Web Based Courses", Ph. D. Thesis, University of Massachusetts Amherst, 11-122, (2001).
49. Weber, G., Brusilovsky, P., "An Adaptive Versatile System for Web-based Instruction", International Journal of Artificial Intelligence in Education, 351-384, (2001).
50. Chen, Q., Norcio, A. F., “Modeling Users with Neural Architectures”, Neural Networks, 1992. IJCNN, International Joint Conference, Vol. 1, IEEE, 547-552, (1992).
51. Herder, E., Dijk, B., “Site Structure and User Navigation: Models, Measures, and Methods”, Adaptable and Adaptive Hypermedia Systems, Chapter 2, Hersley PA USA: IRM Press, 19-36, (2005).