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Antonucci_2022_SoftCOM
Journal article

Modelling assessment rubrics through Bayesian networks : a pragmatic approach

  • Mangili, Francesca ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Adorni, Giorgia ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Piatti, Alberto DFA, Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Locarno, Switzerland
  • Bonesana, Claudio ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Antonucci, Alessandro ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
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  • 2022
Published in:
  • 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). - 2022, p. 1-6
Probabilistic reasoning
Noisy-OR Bayesian networks
Assessment rubrics
Computational thinking
English Automatic assessment of learner competencies is a fundamental task in intelligent tutoring systems. An assessment rubric typically and effectively describes relevant competencies and competence levels. This paper presents an approach to deriving a learner model directly from an assessment rubric defining some (partial) ordering of competence levels. The model is based on Bayesian networks and exploits logical gates with uncertainty (often referred to as noisy gates) to reduce the number of parameters of the model, so to simplify their elicitation by experts and allow real-time inference in intelligent tutoring systems. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills. The simple elicitation of the model starting from the assessment rubric opens up the possibility of quickly automating the assessment of several tasks, making them more easily exploitable in the context of adaptive assessment tools and intelligent tutoring systems.
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Computer science and technology
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https://n2t.net/ark:/12658/srd1326804
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