Hierarchical Cores Applied to an Analysis of Use of Technologies Level among Higher Education Students in Mexico

Francisco Casanova-del-Angel

doi:10.4236/ojs.2014.410079

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Open Journal of Statistics > Vol.4 No.10, December 2014

Hierarchical Cores Applied to an Analysis of Use of Technologies Level among Higher Education Students in Mexico ()

Francisco Casanova-del-Angel

SEPI-ESIA, Unit ALM of the Polytechnic Institute National, Mexico City, Mexico.

DOI: 10.4236/ojs.2014.410079 PDF HTML XML 4,283 Downloads 4,673 Views Citations

Abstract

Using the theory shown, Cores Optimal Criterion, three factors from which hierarchical aggregation of variables under study was built, as well as hierarchical cores showing the level of use of pocket computing technologies by students. The principal factors influencing the level of use of pocket computing technologies among higher education students are analyzed from a theoretical aggregation development based on hierarchical cores. The theoretical part includes the development of an algorithm used to obtain an interesting class or partition from a hierarchy. The experimental work carried out included design, preparation and application of a questionnaire to higher education students in Mexico. A pilot test was carried out to check timing and repetition of questions. Data was recorded, validated, and mathematically and statistically analyzed.

Keywords

Use of Technologies, Higher Education, Questionnaire, Pocket Calculators, Hierarchical Cores

Share and Cite:

Casanova-del-Angel, F. (2014) Hierarchical Cores Applied to an Analysis of Use of Technologies Level among Higher Education Students in Mexico. Open Journal of Statistics, 4, 837-850. doi: 10.4236/ojs.2014.410079.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	González Meneses, M.E. (2007) Estudio de impacto en el uso de calculadoras con sistemas algebraicos (Tecnologías CAS) en instituciones de educación superior. Instituto Tecnológico de Apizaco. Dirección de Educación Superior Tecnológica. Secretaría de Educación Pública, Mexico.
[2]	Lagrange, J.B., Artigue, M., Laborde, C. and Trouche, L. (2003) Technology and Mathematics Education: Multidimensional Overview of Recent Research and Innovation. In: Bishop, A.J., Clements, M.A., Keitel, C., Kill Patrick, J. and Leung, F.K.S., Eds., Second International Handbook of Mathematics Education, Vol. 1, Kluwer Academic Publishers, Dordrecht, 237-270. http://dx.doi.org/10.1007/978-94-010-0273-8_9
[3]	Rodríguez, J.R. and Flores López, L.F. (2005) Propuesta didáctica para el cálculo usando calculadoras Texas Instru- ments. InstitutoTecnológico de los Mochis, Sonora, Mexico.
[4]	Buteau, C. and Muller, E. (2006). Evolving Technologies Integrated into Undergraduate Mathematics Education. In: Son, L.H., Sinclair, N., Lagrange, J.B. and Hoyles, C., Eds., Proceedings of the ICMI 17 Study Conference: Background Papers for the ICMI 17 Study, Hanoi University of Technology, Hanoi.
[5]	OECD (2004) Learning for Tomorrow’s World—First Results from PISA 2003. OECD—Programme for International Student Assessment. http://www.pisa.oecd.org/dataoecd/1/60/34002216.pdf
[6]	Marion, M. and Signonello, S. (2011) From Histogram Data to Model Data Analysis. In: Fichet, B., et al., Eds., Classification and Multivariate Analysis for Complex Data Structures, Studies in Classification Data Analysis, and Knowledge Organization, Springer-Verlag, Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-13312-1_38
[7]	Diday, E. and Noirhomme, M. (2008) Symbolic Data Analysis and the SODAS Software. Wiley, Hoboken, 457 p.
[8]	Diday, E. (2008) Spatial Classification. DAM (Discrete Applied Mathematics). Vol. 156.
[9]	Gonzáles, P., Guzmán, J.C., Partelow, L., Pahlke, E., Jocely, L., Kastberg, D. and Williams, T. (2004) Highlights from the Trends in International Mathematics and Science Study: TIMSS 2003. National Center for Education Statistics Institute of Education Sciences, U.S. Department of Education. http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2005005
[10]	Benzécri, J.P. and Benzécri F. (1980) Pratique de L′Analyse des Données. 1. Analyse des Correspondances. ExposéÉlémentaire. Dunod. Bordas, Paris.
[11]	Ruthven, K. and Hennessy, S. (2002) A Practitioner Model of the Use of Computer-Based Tools and Resources to Support Mathematics Teaching and Learning. Educational Studies in Mathematics, 49, 47-88. http://dx.doi.org/10.1023/A:1016052130572
[12]	Casanova-del-Angel, F. (2001) Análisis multidimensional de datos. Editorial Logiciels, Mexico.
[13]	Everitt, B.S., Landau, S., Leese, M. and Stahl, D. (2011) Cluster Analysis. 5th Edition, Wiley Series in Probability and Statistics. http://dx.doi.org/10.1002/9780470977811