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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.

Robotics research has been focusing on cooperative multi-agent problems, where agents must work together and communicate to achieve a shared objective. To tackle this challenge, we explore imitation learning algorithms. These methods learn a controller by observing demonstrations of an expert, such as the behaviour of a centralised omniscient controller, which can perceive the entire environment, including the state and observations of all agents. Performing tasks with complete knowledge of the state of a system is relatively easy, but centralised solutions might not be feasible in real scenarios since agents do not have direct access to the state but only to their observations. To overcome this issue, we train end-to-end Neural Networks that take as input local observations obtained from an omniscient centralised controller, i.e., the agents' sensor readings and the communications received, producing as output the action to be performed and the communication to be transmitted. This study concentrates on two cooperative tasks using a distributed controller: distributing the robots evenly in space and colouring them based on their position relative to others. While an explicit exchange of messages between the agents is required to solve the second task, in the first one, a communication protocol is unnecessary, although it may increase performance. The experiments are run in Enki, a high-performance open-source simulator for planar robots, which provides collision detection and limited physics support for robots evolving on a flat surface. Moreover, it can simulate groups of robots hundreds of times faster than real-time. The results show how applying a communication strategy improves the performance of the distributed model, letting it decide which actions to take almost as precisely and quickly as the expert controller.

A test is adaptive when its sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. Graphical models, such as Bayesian networks, are used for adaptive tests as they allow to model the uncertainty about the questions and the skills in an explainable fashion, especially when coping with multiple skills. A better elicitation of the uncertainty in the question/skills relations can be achieved by interval probabilities. This turns the model into a credal network, thus making more challenging the inferential complexity of the queries required to select questions. This is especially the case for the information theoretic quantities used as scores to drive the adaptive mechanism. We present an alternative family of scores, based on the mode of the posterior probabilities, and hence easier to explain. This makes considerably simpler the evaluation in the credal case, without significantly affecting the quality of the adaptive process. Numerical tests on synthetic and real-world data are used to support this claim.