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Sistema automatizado de reportes académicos que optimiza la gestión educativa.

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Title: Sistema automatizado de reportes académicos que optimiza la gestión educativa.
Alternate Title: Automated Academic Reporting System that Optimizes Educational Management.
Authors: Londoño-Gallego, Jennifer Andrea1 jealondonog@sena.edu.co, Benavides-Sánchez, Daniel David2 dbenavidess@sena.edu.co, Castro-Maldonado, John Jairo3 jcastrom@sena.edu.co, García-Quintero, Oscar Eduardo4 oegarcia@sena.edu.co
Source: Comunicar. Apr2026, Vol. 34 Issue 85, p35-55. 21p.
Subject Terms: *Artificial intelligence, *School administration, *Digital transformation, *Vocational education, *Bibliometrics, Nonrelational databases
Abstract (English): In the context of digital transformation, vocational education institutions face gaps in academic management related to scheduling, learner monitoring, and the generation of reports for decision-making. This article introduces the Automated Academic Reporting System (SARA), whose objective is to describe its technical development and implementation of artificial intelligence, as well as to assess its contribution to academic management through bibliometric analysis and instructor perceptions. The research is applied in nature, with a mixed-methods approach and a descriptive--exploratory scope. SARA's architectural framework is based on the Model--View--Controller (MVC) pattern and a NoSQL database, ensuring modular scalability and the efficient integration of more than 140,000 heterogeneous records (Excel, SOFIA Plus, JSON). For the bibliometric analysis, the Scopus database was consulted, identifying 4,922 documents processed with VOSviewer® using co-authorship analysis. In addition, semistructured interviews were conducted with seven instructors, whose responses were analyzed using natural language processing techniques to systematize perceptions. The findings highlight in the literature the relevance of frameworks for interoperability, data governance, and digital sustainability, while also revealing a positive perception of SARA regarding ease of use, reduction of administrative workload, and utility in decision-making. In conclusion, SARA is presented as a scalable and replicable tool that strengthens academic management and generates pedagogical and organizational implications from an interdisciplinary perspective. [ABSTRACT FROM AUTHOR]
Abstract (Spanish): En el contexto de transformación digital, las instituciones de formación profesional enfrentan vacíos en la gestión educativa relacionados con la programación, el seguimiento académico y la generación de reportes para la toma de decisiones. Este artículo presenta el Sistema Automatizado de Reportes Académicos (SARA), cuyo objetivo es describir su desarrollo técnico e implementación de inteligencia artificial, así como evaluar su aporte a la gestión académica mediante análisis bibliométrico y la percepción de instructores. La investigación es de tipo aplicada, con enfoque mixto y alcance descriptivo--exploratorio. El marco arquitectónico de SARA se fundamenta en el patrón Modelo--Vista--Controlador (MVC) y en una base de datos NoSQL, lo que asegura escalabilidad modular y la integración eficiente de más de 140.000 registros heterogéneos (Excel, SOFIA Plus, JSON). Para el análisis bibliométrico se consultó la base de datos Scopus, identificando 4.922 documentos procesados con VOSviewer® aplicando análisis de coautoría. Adicionalmente, se aplicaron entrevistas semiestructuradas a 7 instructores, cuyo análisis se apoyó en técnicas de procesamiento de lenguaje natural para sistematizar percepciones. Los resultados destacan en la literatura la relevancia de marcos de interoperabilidad, gobernanza de datos y sostenibilidad digital, además de evidenciar una percepción positiva respecto a la facilidad de uso, la reducción de carga administrativa y la utilidad de SARA en la toma de decisiones. En conclusión, SARA se plantea como una herramienta escalable y replicable que fortalece la gestión académica y genera implicaciones pedagógicas y organizativas desde un enfoque interdisciplinario. [ABSTRACT FROM AUTHOR]
Copyright of Comunicar is the property of Oxbridge Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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PubType: Academic Journal
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  Label: Title
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  Data: Sistema automatizado de reportes académicos que optimiza la gestión educativa.
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  Data: Automated Academic Reporting System that Optimizes Educational Management.
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  Data: <searchLink fieldCode="AR" term="%22Londoño-Gallego%2C+Jennifer+Andrea%22">Londoño-Gallego, Jennifer Andrea</searchLink><relatesTo>1</relatesTo><i> jealondonog@sena.edu.co</i><br /><searchLink fieldCode="AR" term="%22Benavides-Sánchez%2C+Daniel+David%22">Benavides-Sánchez, Daniel David</searchLink><relatesTo>2</relatesTo><i> dbenavidess@sena.edu.co</i><br /><searchLink fieldCode="AR" term="%22Castro-Maldonado%2C+John+Jairo%22">Castro-Maldonado, John Jairo</searchLink><relatesTo>3</relatesTo><i> jcastrom@sena.edu.co</i><br /><searchLink fieldCode="AR" term="%22García-Quintero%2C+Oscar+Eduardo%22">García-Quintero, Oscar Eduardo</searchLink><relatesTo>4</relatesTo><i> oegarcia@sena.edu.co</i>
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  Data: <searchLink fieldCode="JN" term="%22Comunicar%22">Comunicar</searchLink>. Apr2026, Vol. 34 Issue 85, p35-55. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22School+administration%22">School administration</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+transformation%22">Digital transformation</searchLink><br />*<searchLink fieldCode="DE" term="%22Vocational+education%22">Vocational education</searchLink><br />*<searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Nonrelational+databases%22">Nonrelational databases</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: In the context of digital transformation, vocational education institutions face gaps in academic management related to scheduling, learner monitoring, and the generation of reports for decision-making. This article introduces the Automated Academic Reporting System (SARA), whose objective is to describe its technical development and implementation of artificial intelligence, as well as to assess its contribution to academic management through bibliometric analysis and instructor perceptions. The research is applied in nature, with a mixed-methods approach and a descriptive--exploratory scope. SARA's architectural framework is based on the Model--View--Controller (MVC) pattern and a NoSQL database, ensuring modular scalability and the efficient integration of more than 140,000 heterogeneous records (Excel, SOFIA Plus, JSON). For the bibliometric analysis, the Scopus database was consulted, identifying 4,922 documents processed with VOSviewer® using co-authorship analysis. In addition, semistructured interviews were conducted with seven instructors, whose responses were analyzed using natural language processing techniques to systematize perceptions. The findings highlight in the literature the relevance of frameworks for interoperability, data governance, and digital sustainability, while also revealing a positive perception of SARA regarding ease of use, reduction of administrative workload, and utility in decision-making. In conclusion, SARA is presented as a scalable and replicable tool that strengthens academic management and generates pedagogical and organizational implications from an interdisciplinary perspective. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Spanish)
  Group: Ab
  Data: En el contexto de transformación digital, las instituciones de formación profesional enfrentan vacíos en la gestión educativa relacionados con la programación, el seguimiento académico y la generación de reportes para la toma de decisiones. Este artículo presenta el Sistema Automatizado de Reportes Académicos (SARA), cuyo objetivo es describir su desarrollo técnico e implementación de inteligencia artificial, así como evaluar su aporte a la gestión académica mediante análisis bibliométrico y la percepción de instructores. La investigación es de tipo aplicada, con enfoque mixto y alcance descriptivo--exploratorio. El marco arquitectónico de SARA se fundamenta en el patrón Modelo--Vista--Controlador (MVC) y en una base de datos NoSQL, lo que asegura escalabilidad modular y la integración eficiente de más de 140.000 registros heterogéneos (Excel, SOFIA Plus, JSON). Para el análisis bibliométrico se consultó la base de datos Scopus, identificando 4.922 documentos procesados con VOSviewer® aplicando análisis de coautoría. Adicionalmente, se aplicaron entrevistas semiestructuradas a 7 instructores, cuyo análisis se apoyó en técnicas de procesamiento de lenguaje natural para sistematizar percepciones. Los resultados destacan en la literatura la relevancia de marcos de interoperabilidad, gobernanza de datos y sostenibilidad digital, además de evidenciar una percepción positiva respecto a la facilidad de uso, la reducción de carga administrativa y la utilidad de SARA en la toma de decisiones. En conclusión, SARA se plantea como una herramienta escalable y replicable que fortalece la gestión académica y genera implicaciones pedagógicas y organizativas desde un enfoque interdisciplinario. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Comunicar is the property of Oxbridge Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=193720775
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        Value: 10.5281/zenodo.19689946
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        Text: Spanish
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              Text: Apr2026
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