Analytical Data Mart for the Monitoring of University Accreditation Indicators

Ortiz, L.W.; Hallo, María

doi:10.1109/edunine.2019.8875826

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HEIs, therefore, need tools that enable effective academic analysis, which requires a systematic and balanced process for collecting, synthesizing, and evaluating relevant data. In order to support the accreditation process of HEIs, Ortiz and Hallo [64] present an analytical data mart.…”

Section: Description Of Primary Studiesmentioning

confidence: 99%

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review

Sequeira,

Reis,

Alves

et al. 2024

Information

View full text Add to dashboard Cite

Higher education institutions (HEIs) make decisions in several domains, namely strategic and internal management, without using systematized data that support these decisions, which may jeopardize the success of their actions or even their efficiency. Thus, HEIs must define and monitor strategies and policies essential for decision making in their various areas and levels, in which business intelligence (BI) plays a leading role. This study presents a systematic literature review (SLR) aimed at identifying and analyzing primary studies that propose a roadmap for the implementation of a BI system in HEIs. The objectives of the SLR are to identify and characterize (i) the strategic objectives that underlie decision making, activities, processes, and information in HEIs; (ii) the BI systems used in HEIs; (iii) the methods and techniques applied in the design of a BI architecture in HEIs. The results showed that there is space for developing research in this area since it was possible to identify several studies on the use of BI in HEIs, although a roadmap for its implementation was not identified, making it necessary to define a roadmap for the implementation of BI systems that can serve as a reference for HEIs.

show abstract

Section: Description Of Primary Studiesmentioning

confidence: 99%

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review

Sequeira,

Reis,

Alves

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…This process classified all evaluated institutions into five categories: 11 A; 9B; 13C; 13D; 21 E. Of the total number of institutions, 13 were closed for not meeting the minimum quality standards (Acosta and Acosta, 2016). Subsequently, 54 universities were evaluated during the second round, and 5 remained in category A, 23 in B, 18 in C, and 8 in D (Ortiz and Hallo, 2019;Sánchez et al, 2018). This last evaluation followed the legal framework regulating Higher Education which textually expressed, "All the Universities and Polytechnic Schools of the country must be subject to an institutional evaluation, accreditation, and categorization on a five-yearly basis" (Avilés, 2016;Espinoza Cevallos, 2016).…”

Section: Introductionmentioning

confidence: 99%

Evaluación de la Eficiencia Técnica en la Educación Superior en el Ecuador

et al. 2022

View full text Add to dashboard Cite

Este trabajo pretende aportar conocimiento de la eficiencia, planteándose como objetivo valorar la eficiencia relativa desde una perspectiva del análisis envolvente de datos en las universidades de Ecuador. Llevándose a cabo una propuesta retrospectiva de corte transversal tomándose como referencia el análisis envolvente de datos, técnica no paramétrica que permitió la medición relativa de las unidades, donde el modelo CCR-O reveló que 16 DMUs son eficientes y, en el modelo BCC-O fueron 20; se destacan la Escuela Politécnica del Ejército y la del Litoral. En la escala con bootstrap 16 DMUs del modelo CCR-O y 20 del BCC-O alcanzaron Eficiencia Técnica Pura, encontrándose sobre la frontera eficiente; mientras que, en la puntuación de ineficiencia, en el modelo CCR-O 38 son ineficientes, así como, 34 del modelo BCC-O. Este hecho muestra los valores reales de Docencia-Investigación-Gestión frente a los específicos, donde las DMUs ineficientes obtienen el 33,33% y en la variable de entrada, no se muestra un efecto significativo en las puntuaciones de la eficiencia; mientras que, en Docencia-Investigación las DMUs ineficientes alcanzan el 61,11%, Docencia el 55,55%, Docencia-Gestión el 48,14%. Por tanto, las Unidades están sobre dotadas de parámetros en sus resultados, de acuerdo con sus valores reales. Finalmente, la limitación se configuró según el modelo DEA–BCC de output orientado con rendimiento a escala y, resultó útil para medir el desempeño. No obstante, su alcance global puede distorsionar la realidad de la eficiencia técnica, en función de los recursos de las universidades.

show abstract