Model to Support Synchronous and Asynchronous in the Learning Process with An Adaptive Hypermedia System
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.
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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
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de Bibliotecas Digitales, JBIDI 2000, Valladolid, Espa├▒a. 2000.
[4] McQueen, J. 2007. Some methods for classification and analysis of
multivariate observations, 5-th Berkeley Symposium on mathematics,
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[5] Garre, M., Cuadrado, J., Sicilia, M., Rodríguez, D., Rejas, R. 2007.
Comparaci├│n de diferentes algoritmos de clustering en la estimaci├│n de
coste en el desarrollo de software, REICIS, Revista Espa├▒ola de
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[6] Ayaquica, I., Martínez., 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 Educaci├│n 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.
[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 espa├▒ol: algunos resultados experimentales. I Jornadas
de Bibliotecas Digitales, JBIDI 2000, Valladolid, Espa├▒a. 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.
Comparaci├│n de diferentes algoritmos de clustering en la estimaci├│n de
coste en el desarrollo de software, REICIS, Revista Espa├▒ola de
Innovación, Calidad e Ingeniería del Software, Vol. 3 (1), 1885-4486.
[6] Ayaquica, I., Martínez., 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 Educaci├│n 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.
@article{"International Journal of Information, Control and Computer Sciences:59191", author = "Francisca Grimón and Marylin Giugni and Josep Monguet F. and Joaquín Fernández and Luis León G.", title = "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.
", keywords = "Blended Learning, System Adaptive, Model,Clustering Algorithms.", volume = "4", number = "8", pages = "1281-6", }
