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A Mixture Hidden Markov Model to Mine Students’ University Curricula

Author

Listed:
  • Silvia Bacci

    (Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Viale Morgagni 59, 50134 Firenze, Italy
    These authors contributed equally to this work.)

  • Bruno Bertaccini

    (Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Viale Morgagni 59, 50134 Firenze, Italy
    These authors contributed equally to this work.)

Abstract

In the context of higher education, the wide availability of data gathered by universities for administrative purposes or for recording the evolution of students’ learning processes makes novel data mining techniques particularly useful to tackle critical issues. In Italy, current academic regulations allow students to customize the chronological sequence of courses they have to attend to obtain the final degree. This leads to a variety of sequences of exams, with an average time taken to obtain the degree that may significantly differ from the time established by law. In this contribution, we propose a mixture hidden Markov model to classify students into groups that are homogenous in terms of university paths, with the aim of detecting bottlenecks in the academic career and improving students’ performance.

Suggested Citation

  • Silvia Bacci & Bruno Bertaccini, 2022. "A Mixture Hidden Markov Model to Mine Students’ University Curricula," Data, MDPI, vol. 7(2), pages 1-19, February.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:2:p:25-:d:754381
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    References listed on IDEAS

    as
    1. Formann, Anton K., 2007. "Mixture analysis of multivariate categorical data with covariates and missing entries," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5236-5246, July.
    2. Altman, Rachel MacKay, 2007. "Mixed Hidden Markov Models: An Extension of the Hidden Markov Model to the Longitudinal Data Setting," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 201-210, March.
    3. Antonello Maruotti, 2011. "Mixed Hidden Markov Models for Longitudinal Data: An Overview," International Statistical Review, International Statistical Institute, vol. 79(3), pages 427-454, December.
    4. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    5. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 125-145, June.
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    Cited by:

    1. Ali Çetinkaya & Ömer Kaan Baykan & Havva Kırgız, 2023. "Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude," Sustainability, MDPI, vol. 15(17), pages 1-16, August.

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