Procedimiento para determinar las Tendencias Estadísticas del Desarrollo de la Competencia Investigativa del Ingeniero en Ciencias Informáticas

  1. Estrada Molina, Odiel
  2. Alfonso Pulido, Misleydi
  3. Hidalgo Iglesia, Leonardo
  4. Blanco Hernández, Sahara María
  5. Ciudad Ricardo, Febe Ángel
Journal:
GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología

ISSN: 2255-5684

Year of publication: 2014

Issue Title: ESPECIAL UCIENCIA 2014

Volume: 2

Issue: 2

Type: Article

More publications in: GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología

Abstract

At the University of Informatics Sciences, Cuba, some students in their fourth academic year of an engineering in computer science degree are incorporated into the Software Development Centers and are part of the development team of a real production project. The tasks to be oriented and evaluated for by the same Project Management System having the software project. This system has didactic limitations the module orientation tasks lack evidence to enable it to tutor or specialists (professionals caring for the student) assess students according to the indicators comprising the research competence, and allows knowing the statistical trends of student learning in a range of time. Due to the above-mentioned limitations, set out to develop an application that would guide the tutors in evaluating research competence and in turn that this system could integrate the Project Management System college determining statistical trends current students learning about the development of research competence for timely decision making. To develop software based on a procedure that would determine time series statistics of student learning trends according to the development of research competence associated with software development, which is in turn the result to be presented in this papers was developed.

Bibliographic References

  • Arnau, J. (1981) Uso de los modelos de series temporales como técnica de análisis de los diseños conductuales. Departamento de Psicología Experimental Facultad de Filosofía y Ciencias de la Educación. Universidad de Barcelona, p. 100-125.
  • Brillinger, D. (1976) Times Series. Data Analysis y theory. Mc-Graw Hill. Inc, p. 50-75.
  • Brockwell, P. y Davis, R. (2006) Time Series: Theory and Methods. Second Edition. Springer Science +Business Media, LLC, p.95-105.
  • Cáceres, J. y Martín, G. (2008) Introducción al análisis univariante de series temporales económicas. Madrid: Delta Publicaciones Universitarias, p.75-100.
  • Colectivo de autores (2010) Preparación pedagógica para profesores universitarios. Centro de Referencia para la Educación de Avanza. Centro Universitario José Antonio Echeverría de Cuba (CUJAE), p. 175.
  • Farrat, M. y Wong, T. (2010) Procedimiento para el análisis de los resultados de las evaluaciones de software en la UCI. Tesis en opción del grado de Ingeniero en Ciencias Informáticas. Universidad de las Ciencias Informáticas de Cuba.
  • Hernández, J. (2009) Análisis de series temporales económicas II. Madrid: ESIC Editorial, p. 80- 105.
  • Díaz, J., Martín, J., Vilches, Á., Puerto, M., Patón, J. y Varo, C. (2012) Evaluación de modelos de series temporales para la previsión de la demanda de emergencias sanitarias. Revista Emergencias, 24: 181-188.
  • Millan, D., Pacheco, J., Hidalgo J. y Vélez, J. (2010) Forecasting in a Multiskill Call Centre.
  • ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part II. Heidelberg: Springer-Verlag Berlín, p.15-75.
  • Muñoz, D. (2010) Manual de Estadística. Profesor Departamento Economía y Empresa. [En línea] Universidad Pablo de Olavide. [Fecha de consulta: 08 de marzo de 2014.] Disponible en: http://www.eumed.net/cursecon/libreria/drm/drm-estad.pdf .
  • Peña, D. (2010) Análisis de series temporales. Madrid: Alianza S.A., p.75.
  • Channouf, N., L’ecuyer, P., Ingolfsson, A. y Avramidis, A. (2007) The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta. Health Care Manage Sci. 10, p. 25-45.
  • Ruey, T. (2002) Analysis of Financial Time Series. Financial Econometrics. A WileyInterscience Publication JOHN WILEY & SONS, INC. University of Chicago. p. 22- 35.
  • Setzler H., Saydam, C. y Park, S. (2009) EMS Call Volume Predictions: A Comparative Study. Comput Oper Res. 36: 1843-1851.
  • Zhu, Z., Mcknew, M.A. y Lee, J. (1992) Effects of time-varied arrival rates: an investigation in emergency ambulance service systems. En: Proceedings of the 1992 winter simulation conference. Piscataway: IEEE Press; p. 1180.