Modelling assessment rubrics through Bayesian networks: a pragmatic approach

Mangili, Francesca and Adorni, Giorgia and Piatti, Alberto and Bonesana, Claudio and Antonucci, Alessandro (2022) Modelling assessment rubrics through Bayesian networks: a pragmatic approach. In: 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 22-24 September 2022, Split, Croatia.

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Abstract: 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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