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A Framework for Detecting Factors Influencing Students’ Academic Performance: A Longitudinal Analysis

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  • Angela M. D’Uggento

    (University of Bari “Aldo Moro”)

  • Francesco D. d’Ovidio

    (University of Bari “Aldo Moro”)

  • Ernesto Toma

    (University of Bari “Aldo Moro”)

  • Rosa Ceglie

    (Data Engineering Staff, University of Bari “Aldo Moro”)

Abstract

Following the introduction of performance management in Italian universities, Business intelligence has become a strategical means to use administrative data in support of governance and decision-making processes. The aim of this paper is to show the important role played by a decision support system inside an organization by evaluating the outcomes of the fair tuition fee policy of the University of Bari, in favour of low-income students, stated as a priority in its mission. A longitudinal analysis is carried out on the cohort of first-year students enrolled in the academic year 2015–2016, searching for a predictive model of their performance given some explicative variables. The usefulness of adopting a periodic monitoring system, investigating data by means of suitable statistical techniques (logistic regression, survival analysis, Cox regression model), allows to early detect those factors to be modified in order to achieve optimal results with respect to student expectations and quality of higher education.

Suggested Citation

  • Angela M. D’Uggento & Francesco D. d’Ovidio & Ernesto Toma & Rosa Ceglie, 2021. "A Framework for Detecting Factors Influencing Students’ Academic Performance: A Longitudinal Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 389-407, August.
    • Handle: RePEc:spr:soinre:v:156:y:2021:i:2:d:10.1007_s11205-020-02334-7
    DOI: 10.1007/s11205-020-02334-7
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    References listed on IDEAS

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    1. Massimiliano Bratti & Daniele Checchi & Guido De Blasio, 2008. "Does the Expansion of Higher Education Increase the Equality of Educational Opportunities? Evidence from Italy," LABOUR, CEIS, vol. 22(s1), pages 53-88, June.
    2. Daniele Checchi & Vito Peragine, 2010. "Inequality of opportunity in Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 8(4), pages 429-450, December.
    3. Marc Fleurbaey & Vito Peragine, 2013. "Ex Ante Versus Ex Post Equality of Opportunity," Economica, London School of Economics and Political Science, vol. 80(317), pages 118-130, January.
    4. Aaberge, Rolf & Mogstad, Magne & Peragine, Vito, 2011. "Measuring long-term inequality of opportunity," Journal of Public Economics, Elsevier, vol. 95(3), pages 193-204.
    5. Paolo Brunori & Francisco H. G. Ferreira & Vito Peragine, 2013. "Inequality of Opportunity, Income Inequality, and Economic Mobility: Some International Comparisons," Palgrave Macmillan Books, in: Eva Paus (ed.), Getting Development Right, chapter 0, pages 85-115, Palgrave Macmillan.
    6. Delaney, Liam & Harmon, Colm & Redmond, Cathy, 2011. "Parental education, grade attainment and earnings expectations among university students," Economics of Education Review, Elsevier, vol. 30(6), pages 1136-1152.
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    Cited by:

    1. Silva, Polyana Tenório de Freitas e & Sampaio, Luciano Menezes Bezerra, 2023. "Does student aid make a degree more likely? Evidence of the permanence scholarship program from survival models," International Journal of Educational Development, Elsevier, vol. 96(C).

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    More about this item

    Keywords

    University governance; Decision support systems; Students’ academic performance; Predictive models; Logistic model; Cox regression model;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • I2 - Health, Education, and Welfare - - Education

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