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

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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.

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Notes

  1. Note that a student that is “far behind”, taking longer than expected to graduate and exceeding the legal period of a three years course of study, is defined “Fuori corso”; in this case he loses some benefits, for example those related to tuition fees reduction.

  2. D. M. n. 288/2019.

  3. According to the University of Bari Tuition Fees Regulation, students contribute to covering overall university services costs through the payment of the registration fee (linked to merit) and contributions (linked to the economic condition of the family). Students may benefit from a total or partial reduction of the all-encompassing contribution, calculated on the basis of three different factors (economic conditions, merit and partial exemptions due to specific situations).

  4. A simple fit measure based on the log likelihood for the model compared to the log likelihood for a baseline model is the Cox and Snell R2; but, although adjusted for the model’s complexity, it has a theoretical maximum value of less than 1 even for a "perfect" model (Cox and Snell 1989). Nagelkerke R2 is an adjusted version of the previous index, adjusting the scale of the statistic to cover the full range from 0 to 1 (Nagelkerke 1991).

  5. All those differences are statistically highly significant (p < 0.005), as certified by the Mantel-Herzel log-rank test.

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Authors and Affiliations

  1. Department of Economics and Finance, University of Bari “Aldo Moro”, Largo Abbazia Santa Scolastica, 53, Bari, Italy

    Angela M. D’Uggento, Francesco D. d’Ovidio & Ernesto Toma

  2. Data Engineering Staff, University of Bari “Aldo Moro”, Piazza Umberto I, 1, Bari, Italy

    Rosa Ceglie

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  1. Angela M. D’Uggento
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  2. Francesco D. d’Ovidio
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  3. Ernesto Toma
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  4. Rosa Ceglie
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Correspondence to Angela M. D’Uggento.

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This paper is a joint project, but A. M. D’Uggento wrote Sections 2, 3, and 4.1, F. D. d’Ovidio wrote Section 4.2, E. Toma wrote Sections 4 (intro) and 5, R. Ceglie wrote section 1.

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D’Uggento, A.M., d’Ovidio, F.D., Toma, E. et al. A Framework for Detecting Factors Influencing Students’ Academic Performance: A Longitudinal Analysis. Soc Indic Res 156, 389–407 (2021). https://doi.org/10.1007/s11205-020-02334-7

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  • DOI: https://doi.org/10.1007/s11205-020-02334-7

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