arXiv subjects

Alberto Piatti

Publications and source records attributed to Alberto Piatti.

7 recordsLinked to original sources

arXiv2024

The CTSkills App -- Measuring Problem Decomposition Skills of Students in Computational Thinking

This paper addresses the incorporation of problem decomposition skills as an important component of computational thinking (CT) in K-12 computer science (CS) education. Despite the growing integration of CS in schools, there is a lack of consensus on the precise definition of CT in general and decomposition in particular. While decomposition is commonly referred to as the starting point of (computational) problem-solving, algorithmic solution formulation often receives more attention in the classroom, while decomposition remains rather unexplored. This study presents "CTSKills", a web-based skill assessment tool developed to measure students' problem decomposition skills. With the data collected from 75 students in grades 4-9, this research aims to contribute to a baseline of students' decomposition proficiency in compulsory education. Furthermore, a thorough understanding of a given problem is becoming increasingly important with the advancement of generative artificial intelligence (AI) tools that can effectively support the process of formulating algorithms. This study highlights the importance of problem decomposition as a key skill in K-12 CS education to foster more adept problem solvers.

cs.HC↗

arXiv2024

Designing the virtual CAT: A digital tool for algorithmic thinking assessment in compulsory education

Algorithmic thinking (AT) is a critical skill in today's digital society, and it is indispensable not only in computer science-related fields but also in everyday problem-solving. As a foundational component of digital education and literacy, fostering AT skills is increasingly relevant for all students and should become a standard part of compulsory education. However, successfully integrating AT into formal education requires effective teaching strategies and robust and scalable assessment procedures. In this paper, we present the design and development process of the virtual Cross Array Task (CAT), a digital adaptation of an unplugged assessment activity aimed at evaluating algorithmic skills in Swiss compulsory education. The development process followed iterative design cycles, incorporating expert evaluations to refine the tool's usability, accessibility and functionality. A participatory design study played a dual role in shaping the platform. First, it gathered valuable insights from end users, including students and teachers, to ensure the tool's relevance and practicality in classroom settings. Second, it facilitated the collection and preliminary analysis of data related to students' AT skills, providing an initial evaluation of the tool's assessment capabilities across various developmental stages. This was achieved through a pilot study involving a diverse group of students aged 4 to 12, spanning preschool to lower secondary school levels. The resulting instrument features multilingual support and includes both gesture-based and visual block-based programming interfaces, making it accessible to a broad range of learners. Findings from the pilot study demonstrate the platform's usability and accessibility, as well as its suitability for assessing AT skills, with preliminary results showing its ability to cater to diverse age groups and educational contexts.

cs.HC↗

arXiv2024

Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment

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.

cs.AI↗

arXiv2024

FADE-CTP: A Framework for the Analysis and Design of Educational Computational Thinking Problems

In recent years, the emphasis on computational thinking (CT) has intensified as an effect of accelerated digitalisation. While most researchers are concentrating on defining CT and developing tools for its instruction and assessment, we focus on the characteristics of computational thinking problems (CTPs) - activities requiring CT to be solved - and how they influence the skills students can develop. In this paper, we present a comprehensive framework for systematically profiling CTPs by identifying specific components and characteristics, while establishing a link between these attributes and a structured catalogue of CT competencies. The purposes of this framework are (i) facilitating the analysis of existing CTPs to identify which abilities can be developed or measured based on their inherent characteristics, and (ii) guiding the design of new CTPs targeted at specific skills by outlining the necessary characteristics required for CT activation. To illustrate the framework functionalities, we begin by analysing prototypical activities in the literature, a process that leads to the definition of a taxonomy of CTPs across various domains, and we conclude with a case study on the design of a different version of one of these activities, the Cross Array Task (CAT), set in different cognitive environments. This approach allows an understanding of how CTPs in different contexts display unique and recurring characteristics that promote the development of distinct skills. In conclusion, this framework can inform the development of assessment tools, improve teacher training, and facilitate the analysis and comparison of existing CT activities, contributing to a deeper understanding of competency activation and guiding curriculum design in CT education.

cs.HC↗

arXiv2022

Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach

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.

cs.CY↗

arXiv2009

Limits of Learning about a Categorical Latent Variable under Prior Near-Ignorance

In this paper, we consider the coherent theory of (epistemic) uncertainty of Walley, in which beliefs are represented through sets of probability distributions, and we focus on the problem of modeling prior ignorance about a categorical random variable. In this setting, it is a known result that a state of prior ignorance is not compatible with learning. To overcome this problem, another state of beliefs, called \emph{near-ignorance}, has been proposed. Near-ignorance resembles ignorance very closely, by satisfying some principles that can arguably be regarded as necessary in a state of ignorance, and allows learning to take place. What this paper does, is to provide new and substantial evidence that also near-ignorance cannot be really regarded as a way out of the problem of starting statistical inference in conditions of very weak beliefs. The key to this result is focusing on a setting characterized by a variable of interest that is \emph{latent}. We argue that such a setting is by far the most common case in practice, and we provide, for the case of categorical latent variables (and general \emph{manifest} variables) a condition that, if satisfied, prevents learning to take place under prior near-ignorance. This condition is shown to be easily satisfied even in the most common statistical problems. We regard these results as a strong form of evidence against the possibility to adopt a condition of prior near-ignorance in real statistical problems.

cs.LG↗

arXiv2007

Learning about a Categorical Latent Variable under Prior Near-Ignorance

It is well known that complete prior ignorance is not compatible with learning, at least in a coherent theory of (epistemic) uncertainty. What is less widely known, is that there is a state similar to full ignorance, that Walley calls near-ignorance, that permits learning to take place. In this paper we provide new and substantial evidence that also near-ignorance cannot be really regarded as a way out of the problem of starting statistical inference in conditions of very weak beliefs. The key to this result is focusing on a setting characterized by a variable of interest that is latent. We argue that such a setting is by far the most common case in practice, and we show, for the case of categorical latent variables (and general manifest variables) that there is a sufficient condition that, if satisfied, prevents learning to take place under prior near-ignorance. This condition is shown to be easily satisfied in the most common statistical problems.

math.PR↗