Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach
This work addresses the challenge of automating human assessment for adaptive educational tools, but it is incremental as it builds on existing Bayesian network and noisy gate methods.
The paper tackles the problem of automating learner competency assessment in intelligent tutoring systems by deriving a Bayesian network model directly from assessment rubrics, using noisy gates to reduce parameters and simplify expert elicitation, enabling real-time inference and application to computational thinking skills testing.
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.