Big school data: Strategies of the psycho-pedagogical department of an educational institution to cope with the tsunami of psychometric data concerning students

• Autores: Pereyra Quiñones, José Luis, Cuya Chamilco, Paolo, Terrones Flores, César
• Formato: artículo
• Fecha de Publicación: 2017
• Institución: Universidad César Vallejo
• Repositorio: Revistas - Universidad César Vallejo
• Lenguaje: español
• OAI Identifier: oai:oai.revistas.ucv.edu.pe:article/166
• Enlace del recurso: http://revistas.ucv.edu.pe/index.php/psiquemag/article/view/166
• Nivel de acceso: acceso abierto
• Materia: big data educativo; archivos educativos; información psicoeducativa; biblioteca virtual psicológica; almacenamiento de datos educativos; datos psicoeducativos; archivos digitales escolares; educational big data; educational files; psychoeducational information; virtual psychological library; educational data storage; psychoeducational data; school digital files

From the early 20th century, the Psychopedagogical Department of educational institutions has overseen the application of tests, scales or inventories to kinder, elementary and secondary education students, in other words, psychologists have been collecting psychometric information from students for more than 10 years. In that time, the filed data was printed in paper and ink complicating the filing process;therefore, large physical spaces were needed to preserve this psychometric data library and, indeed this was one of the factors that slowed the location process of a particular student’s file.In the 70’s, the arrival of computers allowed to store the files in digital and electronic format, but even with this technology the data filing process was complicated and the location of a student’s file remained slow. The question that arises is: why digital systems are still slow and complicate? This article proposes an explanation about the origin of this enigma. One of the side effects of the digital age is the one called Big Data, which refers to the groups of data whose size, complexity and speed of growth difficult their capture, management, processing or analysis through conventional technologies and tools, such as relational databases and conventional statistics or visualization packages, that is to say, it looks for the utility of the Big Data (Dan’s, 2011).