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The Design of an Adaptive Tool Supporting Formative Assessment in Data Science Courses

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Learning Technologies and Systems (ICWL 2022, SETE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13869))

Abstract

The automated correction of assignments is a task that received large support from artificial intelligence. In previous research, we approached the problem of automatically providing a grade and feedback to students in solving data science exercises, that resulted in the development of the rDSA tool. In this paper, we discuss the first steps towards the development of an adaptive system – based on the rDSA tool – supporting the students’ formative assessment activities. In particular, we present the context of use, the requirements – elicited through a study with a small cohort of students, the models enabling adaptation, and the user interface. Finally, we evaluated the user interface through a further study that involved both qualitative and quantitative measures.

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Notes

  1. 1.

    Available at URL https://vittorini.univaq.it/uts/.

  2. 2.

    Only for the polytomous case, the RS model also includes thresholds between the different values of the answer, but this topic is outside the scope of this paper.

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Author information

Authors and Affiliations

  1. University of L’Aquila, 67100, L’aquila, Italy

    Pierpaolo Vittorini

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  1. Pierpaolo Vittorini
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Correspondence to Pierpaolo Vittorini .

Editor information

Editors and Affiliations

  1. Universidad de La Laguna, Tenerife, Spain

    Carina S. González-González

  2. Universidad Complutense de Madrid, Madrid, Madrid, Spain

    Baltasar Fernández-Manjón

  3. Durham University, Durham, UK

    Frederick Li

  4. Universidad de Salamanca, Salamanca, Spain

    Francisco José García-Peñalvo

  5. Mercatorum University, Rome, Italy

    Filippo Sciarrone

  6. Université de Caen Normandie, Caen, France

    Marc Spaniol

  7. University of Salamanca, Salamanca, Spain

    Alicia García-Holgado

  8. Universidad de La Laguna, Tenerife, Spain

    Manuel Area-Moreira

  9. University of Hagen, Hagen, Germany

    Matthias Hemmje

  10. South China Normal University, Guangzhou, China

    Tianyong Hao

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Vittorini, P. (2023). The Design of an Adaptive Tool Supporting Formative Assessment in Data Science Courses. In: González-González, C.S., et al. Learning Technologies and Systems. ICWL SETE 2022 2022. Lecture Notes in Computer Science, vol 13869. Springer, Cham. https://doi.org/10.1007/978-3-031-33023-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-33023-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33022-3

  • Online ISBN: 978-3-031-33023-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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