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A system for relevance analysis of performance indicators in higher education using Bayesian networks

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Abstract

In this paper, we propose a methodology for relevance analysis of performance indicators in higher education based on the use of Bayesian networks. These graphical models provide, at first glance, a snapshot of the relevant relationships among the variables under consideration. We analyse the behaviour of the proposed methodology in a practical case, showing that it is a useful tool to help decision making when elaborating policies based on performance indicators. The methodology has been implemented in a software that interacts with the Elvira package for graphical models, and that is available to the administration board at the University of Almería (Spain) through a web interface. The software also implements a new method for constructing composite indicators by using a Bayesian network regression model.

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References

  1. Castillo E, Gutiérrez J, Hadi A (1997) Expert systems and probabilistic network models. Springer, New York

    Google Scholar 

  2. Cooper G, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9: 309–347

    MATH  Google Scholar 

  3. Cuenin S (1987) The use of performance indicators in universities: an international survey. Int J Inst Manage High Educ 2: 117–139

    Google Scholar 

  4. Dochy F, Segers M, Wijnen W (1990) Selecting performance indicators. A proposal as a result of research. In: Goedegebuure F, Maasen F, Westerheijden D (eds). Lemma B.V. Peer review and performance indicators, pp 135–153

  5. Elvira Consortium (2002) Elvira: an environment for creating and using probabilistic graphical models. In: Gámez JA, Salmerón A (eds). Proceedings of the First European Workshop on Probabilistic Graphical Models, pp. 222–230

  6. Jensen FV (2001) Bayesian networks and decision graphs. Springer, New York

    MATH  Google Scholar 

  7. Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs. Second edition. Springer, New York

    Book  MATH  Google Scholar 

  8. Jin R, Breitbart Y, Muoh C (2009) Data discretization unification. Knowl Inf Syst 19: 1–29

    Article  Google Scholar 

  9. Madsen A, Jensen FV (1999) Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artif Intell 113: 203–245

    Article  MathSciNet  MATH  Google Scholar 

  10. Moral S, Rumí R, Salmerón A (2001) Mixtures of truncated exponentials in hybrid Bayesian networks. ECSQARU’01. Lect Notes Artif Intell 2143: 135–143

    Google Scholar 

  11. Morales M, Rodríguez C, Salmerón A (2007) Selective naive Bayes for regression using mixtures of truncated exponentials. Int J Uncertain, Fuzziness Knowl Based Syst 15: 697–716

    Article  MATH  Google Scholar 

  12. Nardo M, Saisana M, Saltelli A, Tarantola S (2008) Handbook on constructing composite indicators: methodology and user guide. OECD, European Commission, Joint Research Centre

  13. Nilsson D (1998) An efficient algorithm for finding the M most probable configurations in Bayesian networks. Stat Comput 9: 159–173

    Article  Google Scholar 

  14. Qiu L, Li Y, Wu X (2008) Protecting business intelligence and customer privacy while outsourcing data mining tasks. Knowl Inf Syst 17: 99–120

    Article  Google Scholar 

  15. Shenoy P, Shafer G (1990) Axioms for probability and belief function propagation. In: Shachter R, Levitt T, Lemmer J, Kanal L (eds) Uncertainty in artificial intelligence 4. North Holland, Amsterdam, pp 169–198

    Google Scholar 

  16. Spirtes P, Glymour C, Scheines R (1993) Causation, prediction and search. Series: lecture notes in statistics, vol. 81. Springer, New York

    Google Scholar 

  17. Wang Z, Wang Q, Wang D (2009) Bayesian network based business information retrieval model. Knowl Inf Syst 20: 63–79

    Article  Google Scholar 

  18. Wu X, Kumar V, Quinlan J, Gosh J, Yang Q, Motoda H, McLachlan G, Ng A, Liu B, Yu P, Zhou Z, Steinbach M, Hand D, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14: 1–37

    Article  Google Scholar 

  19. Zhang J, Kang D, Silvescu A, Honavar V (2006) Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data. Knowl Inf Syst 9: 157–179

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Statistics and Applied Mathematics, University of Almería, 04120, Almería, Spain

    Antonio Fernández, María Morales, Carmelo Rodríguez & Antonio Salmerón

Authors
  1. Antonio Fernández
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  2. María Morales
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  3. Carmelo Rodríguez
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  4. Antonio Salmerón
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Corresponding author

Correspondence to Antonio Salmerón.

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Fernández, A., Morales, M., Rodríguez, C. et al. A system for relevance analysis of performance indicators in higher education using Bayesian networks. Knowl Inf Syst 27, 327–344 (2011). https://doi.org/10.1007/s10115-010-0297-9

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  • DOI: https://doi.org/10.1007/s10115-010-0297-9

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