  In modern and personalised education, there is a growing interest in
developing learners' competencies and accurately assessing them. In a previous
work, we proposed a procedure for deriving a learner model for automatic skill
assessment from a task-specific competence rubric, thus simplifying the
implementation of automated assessment tools. The previous approach, however,
suffered two main limitations: (i) the ordering between competencies defined by
the assessment rubric was only indirectly modelled; (ii) supplementary skills,
not under assessment but necessary for accomplishing the task, were not
included in the model. In this work, we address issue (i) by introducing dummy
observed nodes, strictly enforcing the skills ordering without changing the
network's structure. In contrast, for point (ii), we design a network with two
layers of gates, one performing disjunctive operations by noisy-OR gates and
the other conjunctive operations through logical ANDs. Such changes improve the
model outcomes' coherence and the modelling tool's flexibility without
compromising the model's compact parametrisation, interpretability and simple
experts' elicitation. We used this approach to develop a learner model for
Computational Thinking (CT) skills assessment. The CT-cube skills assessment
framework and the Cross Array Task (CAT) are used to exemplify it and
demonstrate its feasibility.
