International Science Index


10129

Model to Support Synchronous and Asynchronous in the Learning Process with An Adaptive Hypermedia System

Abstract:

In blended learning environments, the Internet can be combined with other technologies. The aim of this research was to design, introduce and validate a model to support synchronous and asynchronous activities by managing content domains in an Adaptive Hypermedia System (AHS). The application is based on information recovery techniques, clustering algorithms and adaptation rules to adjust the user's model to contents and objects of study. This system was applied to blended learning in higher education. The research strategy used was the case study method. Empirical studies were carried out on courses at two universities to validate the model. The results of this research show that the model had a positive effect on the learning process. The students indicated that the synchronous and asynchronous scenario is a good option, as it involves a combination of work with the lecturer and the AHS. In addition, they gave positive ratings to the system and stated that the contents were adapted to each user profile.

References:
[1] Alsabti, K., Ranka, S., Singh, V. An Efficient K-Means Clustering Algorithm. Proceedings of IPPS/SPDP Workshop on High Performance Data Mining. 1988
[2] Giugni, M., Lugo, E., Malpica, O. Perfiles de usuario en un ambiente adaptativo y colaborativo, XI Congreso Internacional EDUTEC 2008. Santiago de Compostela. Espa├▒a. 2008
[3] Figuerola, C., Zazo, A., Alonso, J. Categorización automática de documentos en español: algunos resultados experimentales. I Jornadas de Bibliotecas Digitales, JBIDI 2000, Valladolid, España. 2000.
[4] McQueen, J. 2007. Some methods for classification and analysis of multivariate observations, 5-th Berkeley Symposium on mathematics, Statistics and Probability, 1, 281-297. 2007.
[5] Garre, M., Cuadrado, J., Sicilia, M., Rodríguez, D., Rejas, R. 2007. Comparación de diferentes algoritmos de clustering en la estimación de coste en el desarrollo de software, REICIS, Revista Española de Innovación, Calidad e Ingeniería del Software, Vol. 3 (1), 1885-4486.
[6] Ayaquica, I., Mart├¡nez., J., Carrasco, J. 2007. Restricted Conceptual Clustering Algorithms based on Seeds, Computaci├│n y Sistemas, Vol.11 (2), México.
[7] Jain, A., Murty, M., Flynn, P. 1999. Data clustering: a review, ACM Computing Surveys 31(3), 264-323.
[8] Legendre, P., Legendre, L.1998. Numerical Ecology, Second English Edition, Elsevier, Amsterdam.
[9] Wilson, D., Martinez, T. 1997. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, Vol. 6, 1-34.
[10] Salton, G., Buckley, C. 1988. Term-weighting approaches in automatic text retrieval. Information Processing and Management. Vol. 24 (5), 513-523.
[11] Salton, G.1991. Developments in automatic text retrieval. Science, Vol. 253, 974-979.
[12] Joachims, T. 1997. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In Proceedings of ICML-97, 14th International Conference on Machine Learning , Nashville, US, 143- 151.
[13] Rossel, G. 2006. Usando IRT y Agentes para Educación Distancia Adaptativa. I Congreso de Tecnología en Educación y Educación en Tecnología. Argentina.
[14] Yin, R. 2002. Case Study Research, Design and Methods. 3rd ed. Newbury Park, Sage Publications.
[15] Fábregas,J., M. Ferruzca , F. Grimón, J. M. Monguet, M. Sampieri. 2005. Assessing Real Time Evaluation Practices in Different Learning Environments. Recent Research Developments in Learning Technologies, pp 54-58.
[16] Gómez, M. 2000. Análisis de contenido cualitativo y cuantitativo: Definición, clasificación y metodología Revista de Ciencias Humanas, vol 20, pp. 103-113.
[17] Mendoza, R., Sagrera, I., Vega, A. 1978. Bases Psicol├│gicas y Pedag├│gicas de la prevenci├│n del abuso de las drogas a través de la educaci├│n. Universidad de Barcelona.
[18] Davis, F. 1989. Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, Vol. 13 (3), 319- 339.
[19] Seddon, P. 1997. A Respecification and Extension of theDeLone and McLean Model of IS Success. Information Systems Research, Vol. 8 (3), 240-253.
[20] Liu, Ch., Arnett, K. 2000. Exploring the Factors Associated with Web Site Success in the Context of Electronic Commerce, Information and Management, Vol. 38, 23-33.
[21] Lee, Y., Kozar, K., Larsen, K. 2003. The Technology Acceptance Model: Past, Present and Future. Communications of the Association for Information Systems, 12(50), 752-780.
[22] Liaw, S., Huang, H. 2003. An investigation of user attitudes toward search engines as an information retrieval tool, Computers in Human Behaviour, Vol. 19 (6), 751-765.
[23] Rong-An, S., Yu-Chen, C., Lysander, S. 2005. Extrinsic versus intrinsic motivations for consumers to shop on-line, Information & Management, Vol. 42 (3), 401-413.
[24] Shih. H. 2004. An empirical study on predicting user acceptance of eshopping on the web. Information and Management, Vol. 41, 351-368.
[25] Brusilovsky, P. 2000. Adaptive Hypermedia. User Modeling and User- Adapted Interaction, Kluwer Academic Publisher, Netherlands, Vol. 11, 87-110.
[26] Brusilovsky, P. 2000. Adaptive Hypermedia: From Intelligent Tutoring Systems to Web-Based Education. Springer-Verlag Berlin Heidelberg, 1-7.