This paper presents the results of an efficiency study of Colombian public universities in 2012, conducted using the methodology of Data Envelopment Analysis (DEA) and the models CCR, BCC and SBM under output orientation. The main objective is to determine technical, pure technical, scale and mix efficiencies using data acquired from the Ministry of National Education. An analysis of the results shows the extent to which outputs of inefficient Higher Education Institutions (HEIs) could be improved and the possible cause of this inefficiency. The universities were also ranked using a Pareto efficient cross-efficiency model and a study was made of changes to overall productivity between 2011 and 2012. The results showed Tolima, Caldas and UNAD to be the best-performing universities, with Universidad del Pacífico as the worst performer. Malmquist index was applied to analyze the change in productivity from 2011 to 2012. The Universidad de La Guajira showed great improvement in technical efficiency between 2011 and 2012.

This paper shows the results of a research of the application of data envelopment analysis (DEA) together with artificial neural networks (ANN) of higher education institutions in Colombia during the years 2011-2013, for the purpose of evaluating the technical efficiency of Colombian higher education institutions and subsequently carry out predictions, based on a group of management indicators. Information provided by the Ministry of National Education was used as data source. The results show that this two-stage approach provides the DEA with the predictive potential that it otherwise lacks, enhancing its evaluative qualities; this is also evident in the various research papers consulted. The results also show that 50% of the models built have correct classification rates of 64.58% and 58.33% for training and validation datasets, respectively.

Higher Education in Colombia is experiencing a period of shortfalls in funding which has obliged Universities to seek resources from non-governmental sources, in addition to this, the Ministry of National Education (MEN) has implemented a scheme where funds are assigned year on year but this is not considered part of the budgetary base. This study seeks to create a characterisation of the model and proposes the creation of a synthetic performance index based on Multiple Factorial Analysis (MFA). This index enabled us to rank the Higher Education Institutions (HEI) according to their performance and analyse the trajectory of Colombian Universities at a given moment, which meant their evolution over time could be reviewed, moreover within the complexity of the model of indices of results in Colombian higher education, the majority of HEI have a similar, yet, to some degree, distinct structure and display scores which show that within a certain degree of homogeneity, each institution has its strengths in some of the variables considered and its weaknesses in others.

Background: Growing disparities in development, governance, and logistics performance across countries pose challenges for global policymaking and Sustainable Development Goal (SDG) monitoring. This study proposes a classification of 137 countries based on multiple structural dimensions. The dataset for 2023 includes six components of the Logistics Performance Index (LPI), six dimensions of the Worldwide Governance Indicators (WGIs), and four proxies of the Human Development Index (HDI). Methods: The Uniform Manifold Approximation and Projection (UMAP) technique was used to reduce dimensionality and allow for meaningful clustering. Based on the reduced space, the K-means algorithm was employed to group countries with similar development characteristics. Results: The classification process allowed the identification of three distinct groups of countries, supported by a Hopkins statistic of 0.984 and an explained variance ratio of 87.3%. These groups exhibit structural differences in the quality of governance, logistics capacity, and social development conditions. Internal consistency checks and multivariate statistical analyses (ANOVA and MANOVA) confirmed the robustness and statistical significance of the clustering. Conclusions: The resulting classification offers a practical analytical tool for policymakers to design differentiated strategies aligned with national contexts. Furthermore, it provides a data-driven approach for comparative monitoring of the SDGs from an integrated and empirical perspective.

This study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean countries, taking into account the following indicators for the period 2017–2022: education expenditure (% of GDP), health expenditure (% of GDP), GDP per capita (constant USD), CO₂ emissions per capita (metric tons), energy consumption per capita (kWh), internet users (% of population), mobile phone subscriptions (per 100 inhabitants), and the Global Innovation Index (GII). Initially, through the application of principal component analysis (PCA), the objective was to reduce the complexity of the data set and reveal the main structural dimensions. Subsequently, cluster analysis was used to classify countries according to shared development patterns. To achieve this, the average of the indicators for the 2017–2022 period was used as a basis, which enabled the reduction in short-term distortions and the capture of structural trends. The results reveal the existence of distinct groups, with countries with higher levels of digital connectivity, investment in human capital, and economic dynamism experiencing more favorable development conditions.

Eficiencia en las instituciones educativas