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An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark

Author

Listed:
  • María Morales

    (Department of Mathematics, University of Almería, 04120 Almería, Spain)

  • Antonio Salmerón

    (Department of Mathematics, University of Almería, 04120 Almería, Spain)

  • Ana D. Maldonado

    (Department of Mathematics, University of Almería, 04120 Almería, Spain)

  • Andrés R. Masegosa

    (Department of Computer Science, Aalborg University, 2450 Copenhagen SV, Denmark)

  • Rafael Rumí

    (Department of Mathematics, University of Almería, 04120 Almería, Spain)

Abstract

Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In this study, we analyze the effect of the continuous assessment on the performance of the students in their final exams in courses of Statistics at the University of Almería. Specifically, we study if the performance of a student in the continuous assessment determines the score obtained in the final exam of the course in such a way that this score can be predicted in advance using the continuous assessment performance as an explanatory variable. After using and comparing some powerful statistical procedures, such as linear, quantile and logistic regression, artificial neural networks and Bayesian networks, we conclude that, while the fact that a student passes or fails the final exam can be properly predicted, a more detailed forecast about the grade obtained is not possible.

Suggested Citation

  • María Morales & Antonio Salmerón & Ana D. Maldonado & Andrés R. Masegosa & Rafael Rumí, 2022. "An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark," Mathematics, MDPI, vol. 10(21), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3994-:d:955358
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    References listed on IDEAS

    as
    1. Manuel Koller & Werner A. Stahel, 2017. "Nonsingular subsampling for regression S estimators with categorical predictors," Computational Statistics, Springer, vol. 32(2), pages 631-646, June.
    2. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
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    Cited by:

    1. Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.
    2. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.

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