Neural longitudinal mapping of multidimensional performance profiles of Latin American universities

Authors

DOI:

https://doi.org/10.47909/ijsmc.92

Keywords:

multidimensional temporal data visualization, self-organizing maps, THE Latin American university ranking, neural longitudinal mapping, SOM neural network, university performance profile

Abstract

Objective. To introduce an artificial intelligence method and to use it to analyze the evolution of the multidimensional performance profiles of the most prominent Latin American universities, according to the Times Higher Education Latin America University Rankings.

 Design/Methodology/Approach. To multidimensionally characterize universities' performance profiles, we use the rankings' sub-scores indicators, which quantify five dimensions of academic endeavor assessed by this ranking. To automatically compare and visually analyze performance profile's evolution our method uses an artificial neural network.

Results/Discussion. The neurocomputational procedure allowed us to discover all the characteristic performance profiles of the 50 best ranked universities (20 institutional profiles in 2019), and to visualize, in a knowledge map, the universities' groups sharing similar profiles. Furthermore, the profile's evolution of this group of universities was analyzed, and visually displayed in a sequence of knowledge maps, covering the four years period 2016-2019. In general, these universities show a remarkable improvement in Teaching, Research and Citation scores during 2016-2019. The profiles diversity of the best ranked universities, and the predominance and homogenization process of Brazilian universities profiles are noteworthy.

Conclusions. Performance profile characterization using multiple indicators is a matter of interest in diverse domains. However, visualization or comparison of multidimensional performance profiles is not an easy task for the human mind. Even more challenging is the visual analysis of multidimensional performance profiles evolution. The neuro-longitudinal technique introduced is a useful tool to analyze and visualize the evolution of multidimensional performance profiles.

Originality/Value. The approach and techniques introduced in the paper have an important degree of generality and can be used to analyze other type of rankings or multidimensional data.

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References

Aguilar GS, Sánchez MVG, Carrillo-Calvet H. (2002). ViBlioSOM: Visualización de información bibliométrica mediante el mapeo autoorganizado. Rev Española Doc Científica; 25(4): 477–84. https://doi.org/10.3989/REDC.2002.V25.I4.281 DOI: https://doi.org/10.3989/redc.2002.v25.i4.281

Arencibia-Jorge R, Villaseñor EA, Lozano-Díaz IA, Carrillo-Calvet H. (2016) Elsevier’s journal metrics for the identification of a mainstream journals core: A case study on mexico. Libres; 26(1): 1–13. https://doi.org/10.32655/LIBRES.2016.1.1 DOI: https://doi.org/10.32655/LIBRES.2016.1.1

Çakır MP, Acartürk C, Alaşehir O, Çilingir C. A comparative analysis of global and national university ranking systems. Scientometrics 2015; 103(3):813–48. https://doi.org/10.1007/S11192-015-1586-6 DOI: https://doi.org/10.1007/s11192-015-1586-6

García, J. A., Rodríguez-Sánchez, R., Fdez-Valdivia, J., Robinson-García, N., & Torres-Salinas, D. (2012). Mapping academic institutions according to their journal publication profile: Spanish universities as a case study. Journal of the American Society for Information Science and Technology, 63(11), 2328–2340. https://doi.org/10.1002/ASI.22735 DOI: https://doi.org/10.1002/asi.22735

Garfield, E. (1994). Scientography: Mapping the tracks of science. Current contents. Social & Behavioural Sciences, 7(45), 5–10. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1609630

Glänzel, W. (2000). Science in Scandinavia: A Bibliometric Approach. Scientometrics 2000 48:2, 48(2), 121–150. https://doi.org/10.1023/A:1005640604267 DOI: https://doi.org/10.1023/A:1005640604267

Victoria Guzman-Sanchez, M., Carrillo-Calvet, H., Jimenez Andrade, J. L., & Villasenor-Garcia, E. A. (2010). Bioinformetric Studies on Tuberculosis Vaccines Research. In Norazmi Mohd. Nor, Armando Acosta, & Maria Elena Sarmiento (Eds.), Art & Science of Tuberculosis Vaccine Development. Oxford Fajar Sdn. Bhd.

Hazelkorn, E., & Hazelkorn, E. (2015). Reshaping Higher Education. In Rankings and the Reshaping of Higher Education (pp. 203–227). Palgrave Macmillan UK. https://doi.org/10.1057/9781137446671_6 DOI: https://doi.org/10.1057/9781137446671_6

Iñiguez, G., Pineda, C., Gershenson, C., & Barabási, A.-L. (2022). Dynamics of ranking. Nature Communications, 13(1), 1–7. https://doi.org/10.1038/s41467-022-29256-x DOI: https://doi.org/10.1038/s41467-022-29256-x

Jiménez-Andrade JL, Villaseñor-García EA, Carrillo-Calvet H. (2019) Self Organizing Maps Laboratory: LabSOM. Pre-release. https://github.com/ldnl-unam/labsom. https://doi.org/10.5281/zenodo.3630581.

Kim, J. (2018). The Functions and Dysfunctions of College Rankings: An Analysis of Institutional Expenditure. Research in Higher Education, 59(1), 54–87. https://doi.org/10.1007/s11162-017-9455-1 DOI: https://doi.org/10.1007/s11162-017-9455-1

Kohonen, T. (2013). Essentials of the self-organizing map. Neural Networks, 37, 52–65. https://doi.org/10.1016/j.neunet.2012.09.018 DOI: https://doi.org/10.1016/j.neunet.2012.09.018

Leiden Ranking (2021) https://www.leidenranking.com/ranking/2021/chart

Luque-Martínez, T., & Faraoni, N. (2020). Meta-ranking to position world universities. Studies in Higher Education, 45(4), 819–833. https://doi.org/10.1080/03075079.2018.1564260 DOI: https://doi.org/10.1080/03075079.2018.1564260

Millot, B. (2015). International rankings: Universities vs. higher education systems. International Journal of Educational Development, 40, 156–165. https://doi.org/10.1016/j.ijedudev.2014.10.004 DOI: https://doi.org/10.1016/j.ijedudev.2014.10.004

Moed, H. F. (2017). A critical comparative analysis of five world university rankings. Scientometrics, 110(2), 967–990. https://doi.org/10.1007/S11192-016-2212-Y/TABLES/10 DOI: https://doi.org/10.1007/s11192-016-2212-y

Moya-Anegón, F., Herrero-Solana, V., & Jiménez-Contreras, E. (2006). A connectionist and multivariate approach to science maps: The SOM, clustering and MDS applied to library and information science research. Journal of Information Science, 32(1), 63–77. https://doi.org/10.1177/0165551506059226 DOI: https://doi.org/10.1177/0165551506059226

Peters, M. A. (2019). Global university rankings: Metrics, performance, governance. Educational Philosophy and Theory, 51(1), 5–13. https://doi.org/10.1080/00131857.2017.1381472 DOI: https://doi.org/10.1080/00131857.2017.1381472

Polanco, X., François, C., & Lamirel, J. C. (2001). Using artificial neural networks for mapping of science and technology: A multi-self-organizing-maps approach. Scientometrics, 51(1), 267–292. https://doi.org/10.1023/A:1010537316758 DOI: https://doi.org/10.1023/A:1010537316758

Ruiz-Coronel, A., Jiménez-Andrade, J. L., & Carrillo-Calvet, H. (2020). National cancer institute scientific production scientometric analysis. Gaceta Medica de Mexico, 156(1), 4–10. https://doi.org/10.24875/GMM.M19000315 DOI: https://doi.org/10.24875/GMM.M19000315

Skupin, A., Biberstine, J. R., & Börner, K. (2013). Visualizing the Topical Structure of the Medical Sciences: A Self-Organizing Map Approach. PLoS ONE, 8(3), e58779. https://doi.org/10.1371/journal.pone.0058779 DOI: https://doi.org/10.1371/journal.pone.0058779

Soh, K. (2017). Don’t Read University Rankings Like Reading Football League Tables: Taking a Close Look at the Indicators. In World University Rankings (pp. 1–17). World Scientific. https://doi.org/10.1142/9789813200807_0001 DOI: https://doi.org/10.1142/9789813200807_0001

Soh, K. (2017). The seven deadly sins of world university ranking: a summary from several papers. Journal of Higher Education Policy and Management, 39(1), 104–115. https://doi.org/10.1080/1360080X.2016.1254431 DOI: https://doi.org/10.1080/1360080X.2016.1254431

Sotolongo-Aguilar, G., Guzmán-Sánchez, M. V., Saavedra-Fernández, O., & Carrillo-Calvet, H. A. (2001). Mining informetric data with self-organizing maps. Proceedings of the 8th International Society for Scientometrics and Informetrics, 665–673.

THE-LA Times Higher Education Latin America University Rankings. https://www.timeshighereducation.com/world-university-rankings/latin-america-university-rankings-2019-methodology (2019, accessed 7 November 2019)

Tsvetkova, E., & Lomer, S. (2019). Academic excellence as “competitiveness enhancement” in Russian higher education. International Journal of Comparative Education and Development, 21(2), 127–144. https://doi.org/10.1108/IJCED-08-2018-0029/FULL/PDF DOI: https://doi.org/10.1108/IJCED-08-2018-0029

U-Multirank (2021). https://www.umultirank.org/

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/S11192-009-0146-3/FIGURES/7 DOI: https://doi.org/10.1007/s11192-009-0146-3

van Eck, N. J., & Waltman, L. (2007). VOS: A New Method for Visualizing Similarities Between Objects. In R. L. H. Decker (Ed.), Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization (pp. 299–306). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_34 DOI: https://doi.org/10.1007/978-3-540-70981-7_34

Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586–600. https://doi.org/10.1109/72.846731 DOI: https://doi.org/10.1109/72.846731

Villaseñor, E. A., Arencibia-Jorge, R., & Carrillo-Calvet, H. (2017). Multiparametric characterization of scientometric performance profiles assisted by neural networks: a study of Mexican higher education institutions. Scientometrics, 110(1), 77–104. https://doi.org/10.1007/s11192-016-2166-0 DOI: https://doi.org/10.1007/s11192-016-2166-0

Wende M, Don W. Rankings and Classifications: The Need for a Multidimensional Approach. In: Van Vught F. (eds) Mapping the Higher Education Landscape. Higher Education Dynamics, vol 28. Springer, Dordrecht 2009, pp. 71–86. DOI: https://doi.org/10.1007/978-90-481-2249-3_5

Wende, M. van der, & Don, W. (2009). Rankings and Classifications: The Need for a Multidimensional Approach (pp. 71–86). https://doi.org/10.1007/978-90-481-2249-3_5 DOI: https://doi.org/10.1007/978-90-481-2249-3_5

White, HD, Lin X., Mccain, K. W. (1998). Two Modes of Automated Domain Analysis: Multidimensional Scaling vs. Kohonen Feature Mapping of Information Science Authors. In: Proceedings of the Fifth International ISKO Conference (ed M Widad, J Maniez and S. Pollitt), Structures and Relations in Knowledge Organization. Ergon Verlag, Wurzburg, 1998, pp. 57–63.

Williams, R., & de Rassenfosse, G. (2016). Pitfalls in aggregating performance measures in higher education. Studies in Higher Education, 41(1), 51–62. https://doi.org/10.1080/03075079.2014.914912 DOI: https://doi.org/10.1080/03075079.2014.914912

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Published

2024-03-19

How to Cite

Jiménez-Andrade, J. L., Martí-Lahera, Y., & Carrillo Calvet, H. (2024). Neural longitudinal mapping of multidimensional performance profiles of Latin American universities. Iberoamerican Journal of Science Measurement and Communication, 4(1), 1–16. https://doi.org/10.47909/ijsmc.92