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Robustness Analysis of a Methodology to Detect Biases, Inconsistencies and Discrepancies in the Evaluation Process

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International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023) (CISIS 2023, ICEUTE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 748))

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Abstract

This paper analyzes the robustness and stability of a published methodology to improve the evaluation of complex projects in university courses. For this purpose, different types of experiments are performed on a dataset (e.g. elimination of features, input perturbations) of a subject in Computer Systems at the University of La Rioja (Spain); then, the methodology is reapplied, analyzing whether the final conclusions remain similar. The results show that the conclusions obtained, despite the variations introduced, are consistent.

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References

  1. Divasón, J., Martínez-de-Pisón, F.J., Romero, A., Sáenz-de Cabezón, E.: Artificial intelligence models for assessing the evaluation process of complex student projects. IEEE Trans. Learning Technol., 1–18 (2023)

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Acknowledgments

This work is supported by grant PID2020-116641GB-I00 funded by MCIN/AEI/ 10.13039/501100011033.

Author information

Authors and Affiliations

  1. University of La Rioja, Logroño, Spain

    Jose Divasón, Francisco Javier Martínez-de-Pisón, Ana Romero & Eduardo Sáenz-de-Cabezón

Authors
  1. Jose Divasón
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  2. Francisco Javier Martínez-de-Pisón
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  3. Ana Romero
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  4. Eduardo Sáenz-de-Cabezón
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Corresponding author

Correspondence to Jose Divasón .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer and Aerospace Engineering, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Divasón, J., Martínez-de-Pisón, F.J., Romero, A., Sáenz-de-Cabezón, E. (2023). Robustness Analysis of a Methodology to Detect Biases, Inconsistencies and Discrepancies in the Evaluation Process. In: García Bringas, P., et al. International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). CISIS ICEUTE 2023 2023. Lecture Notes in Networks and Systems, vol 748. Springer, Cham. https://doi.org/10.1007/978-3-031-42519-6_31

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