# Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach

arXiv: https://arxiv.org/abs/2209.05467 · HTML: https://mlsanity.com/paper/2209.05467/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment](https://mlsanity.com/paper/2408.01221/) (2024)
- [Designing the virtual CAT: A digital tool for algorithmic thinking assessment in compulsory education](https://mlsanity.com/paper/2408.01263/) (2024)
- [Causal Modelling of Support Interventions for Student Competency Assessment](https://mlsanity.com/paper/2608.24632/) (2026)

## Abstract (arXiv)

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.