Exploratory Analysis of Spatial Data on the levels of learning achievement in mathematics and reading of second grade secondary students in the Census Assessment
DOI:
https://doi.org/10.17162/au.v11i4.760Keywords:
Learning assessment, census assessment of students, second grade high school students, exploratory analysis of spatial data, principal component analysis, mathematics, reading.Abstract
The objective of this research was to identify the levels of learning achievement in mathematics and reading obtained by second grade high school students, in the Student Census Assessment (SCA) in 2018, using the exploratory analysis of spatial data and analysis of main components and to determine if there is spatial autocorrelation between the levels of achievement of learning in mathematics and reading obtained by the students of the second grade of secondary school in the SCA in the regions of Peru. This study was developed within a quantitative approach which is based on the hypothetical deductive, non-experimental, cross-sectional design method because it aimed at a defined time and time, the year 2018. The data collection method that was used was that of secondary data because the SCA was used in second-grade secondary school students in Peru, in 2018.The results allowed determining the level of learning achievement prior to starting in mathematics and reading, obtained by the second grade of secondary school, the most important, corresponding to the regions of: Loreto, Amazonas, Ucayali, Huánuco, Huancavelica and Apurímac; with the level of achievement of learning at the beginning in mathematics, were identified: Tumbes, Piura, La Libertad, Ica y Callao; with the level of learning achievement at the beginning of reading were identified: Tumbes, Piura, Cajamarca, San Martín, Ucayali and Madre de Dios.; It was also shown that there is spatial autocorrelation between the levels of learning achievement in mathematics and reading obtained by second year students in SCA. (p < 0.05).Downloads
References
Acevedo, I. & Velásquez, E. (2008). Algunos conceptos de la econometría espacial y el análisis exploratorio de datos espaciales. Ecos de Economía, 12 (27), 2-25. https://dialnet.unirioja.es/servlet/articulo?codigo=4024478.
Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27 (2),
-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Buzai, G. & Baxendale, C. (2009). Análisis Exploratorio de Datos Espaciales. Revista digital del Grupo de Estudios sobre Geografía y Análisis Espacial con Sistemas de Información Geográfica (GESIG), Sección Software y Metodología, (1), 1-11. https://ri.unlu.edu.ar/xmlui/bitstream/handle/rediunlu/702/Buzai_An%C3%A1lisis%20Exploratorio%20de%20Datos%20Espaciales.pdf?sequence=1&isAllowed=y
Celemín, J. P. (2009). Autocorrelación especial e indicadores locales de asociación especial, importancia, estructura y aplicación. Revista Universitaria de Geografía, 18 (1), 11-31.
https://www.redalyc.org/pdf/3832/383239099001.pdf
Cervera, L. E., Lizárraga, G. M. & Sánchez, C. P. (2008). Estudio georreferencial de la Evaluación Nacional de Logro Académico en Centros Escolares (ENLACE) en el Municipio de Juárez, Chihuahua: análisis espacial. Revista electrónica de investigación educativa, 10 (1), 1-23. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1607-40412008000100005.
Chasco, C. (2003). Métodos gráficos del análisis exploratorio de datos espaciales. Anales de economía aplicada. Departamento de Economía Aplicada, Universidad Autónoma de Madrid. Madrid. https://www.asepelt.org/ficheros/File/Anales/2003%20-%20Almeria/asepeltPDF/93.PDF
Chasco, C. (2006). Análisis estadístico de datos geográficos en geomarketing: el programa GeoDa. Distribución y Consumo, 2, 34-45.
Chue, J. (2016). Análisis exploratorio espacial del ingreso de los egresados universitarios del Perú. Anales Científicos, 77 (2), 329-337. http://revistas.lamolina.edu.pe/index.php/acu/article/view/810/pdf_4
De Corso, G. B., Pinilla, M. & Gallego, J. (2017). Métodos gráficos de análisis exploratorio de datos espaciales con variables espacialmente distribuidas. Cuadernos latinoamericanos de Administración, 13 (25), 92-104. https://doi.org/10.18270/cuaderlam.v13i25.2417
García, J. (2011). Análisis exploratorio de datos espaciales de la segregación urbana en ciudad Juárez. St. Louis: Federal Reserve Bank of St Louis. https://search.proquest.com/working-papers/análisis-exploratorio-de-datos-espaciales-la/docview/1698181312/se-2?accountid=12268
Getis, A. & Ord, J. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24 (3), 189-206. doi: 10.1111/j.1538-4632.1992.tb00261.x
Griffith, D. A. (2009). Spatial autocorrelation. University of Texas at Dallas, Richardson, TX, Elsevier Inc. http://www.elsevierdirect.com/brochures/hugy/SampleContent/Spatial-Autocorrelation
Gordziejczuk, M. A., & Lucero, P. I. (2019). Turismo y calidad de vida: un estudio de autocorrelación espacial aplicado a la ciudad de Mar del Plata, provincia de Buenos Aires, Argentina. Cuadernos de Geografía: Revista Colombiana de Geografía, 28 (1), 23-42. https://doi.org/10.15446/rcdg.v28n1.67275
Hernández-Sampieri, R. & Mendoza, C. P. (2018). Metodología de la investigación. Las rutas cuantitativa, cualitativa y mixta. México: McGraw Hill.
INEI (2020). Resultados de la pobreza monetario 2019. https://www.inei.gob.pe/media/MenuRecursivo/boletines/presentacion-del-jefe-del-inei.pdf
Johnson, R. A., & Wichern, D. (2007). W. Applied multivariate statistical analysis. New Jersey: Prentice Hall Inc.
Levy, J. & Varela, J. (2003). Análisis multivariable para las ciencias sociales. Madrid: Pearson Educación.
MINEDU (2020). Evaluaciones nacionales de logro de aprendizaje 2019. http://umc.minedu.gob.pe/wp-content/uploads/2020/06/Reporte-Nacional-2019.pdf
MINEDU (2019). Reporte técnico de las evaluaciones censales y muestrales de estudiantes 2018. http://umc.minedu.gob.pe/wp-content/uploads/2019/08/RT-Evaluaciones-2018.pdf.
MINEDU (2018). Sistema de consulta de resultados de evaluaciones (SICRECE). https://sistemas15.minedu.gob.pe:8888/evaluacion_censal_publico
MINEDU (2016). Resultados de la Evaluación Censal de Estudiantes 2016 (ECE 2016). http://umc.minedu.gob.pe/wp-content/uploads/2017/04/ECE-2016-presentaci%C3%B3n-de-resultados-web.pdf
MINEDU (2015). Resultados de la Evaluación Censal de Estudiantes 2015. http://umc.minedu.gob.pe/wpcontent/uploads/2016/03/Resultados-ECE-2015.pdf
Oyana, T., & Margai, F. (2015). Spatial analysis. Statistics, visualization, and computatonal methods. New York: CRC Press.
Perez, C. (2014). Análisis multivariante de datos. Madrid: Ibergarceta Publicaciones.
Rodríguez, F. de J., Pompa-García, M., Hernández-Díaz , C. & Juárez-Reyes, A. (2010). Patrón de distribución espacial de la pérdida, degradación y recuperación vegetal en Durango, México. Avances en investigación agropecuaria, 14 (1), 53-65. Recuperado de: http://ww.ucol.mx/revaia/anteriores/PDF%20DE%20REVISTA/2010/enero/4.pdf
Rodríguez, R. (2017). Software de análisis geoestadístico GEODA. https://risharkygis.wordpress.com/2017/03/23/software-de-analisis-geoestadistico-geoda/.
Serrano, R. M. & Valcárcel, E. V. (2002). Econometría espacial: Nuevas técnicas para el análisis regional. una aplicación a las regiones europeas. Investigaciones Regionales, (1), 83-106. https://search.proquest.com/scholarly-journals/econometría-espacial-nuevas-técnicas-para-el/docview/1459697470/se-2?accountid=12268
Uriel E. & Aldás, J. (2005). Análisis multivariante aplicado. Aplicaciones al marketing, Investigación de mercados, Economía, Dirección de empresas y turismo. Madrid: Thomson editores.
Vilalta, C. J. (2005). Cómo enseñar autocorrelación espacial, Economía, Sociedad y Territorio, México, 5 (18), 323-333. https://doi.org/10.22136/est002005307
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Copyright (c) 2021 Olga Solano Dávila, Doris Gómez Ticerán, Grabiela Montes Quintana, Gregoria Ramón Quispe, Nelly Pillhuaman Caña, Daniel Bolaños Solano
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