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.

翻译：学习者能力自动评估是智能导学系统中的核心任务。评估量规通常能有效描述相关能力及其等级。本文提出一种直接从定义能力等级（部分）排序的评估量规中推导学习者模型的方法。该模型基于贝叶斯网络，利用带不确定性的逻辑门（常称为噪声门）来减少模型参数数量，从而简化专家参数标定过程，并支持智能导学系统中的实时推理。我们通过计算思维技能测试活动的自动化人工评估案例，阐释了该方法的应用流程。这种从评估量规直接构建模型的简易标定方式，为快速实现多任务评估自动化提供了可能，使其能更便捷地应用于自适应评估工具与智能导学系统。