The CT-cube: A framework for the design and the assessment of computational thinking activities

Piatti, Alberto; Adorni, Giorgia; El-Hamamsy, Laila; Negrini, Lucio; Assaf, Dorit; Gambardella, Luca Maria; Mondada, Francesco

doi:10.1016/j.chbr.2021.100166

Cited by 17 publications

(28 citation statements)

References 21 publications

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“…The findings are consistent with other studies that found that simple loops are already mastered in early primary school (Montuori et al, 2022), with very young students (starting 3 years old) already being able to solve algorithmic problems and their results improving with age (Piatti et al, 2022). As CT skills relate to students' numerical, verbal, and nonverbal reasoning abilities (Tsarava et al, 2022), it is likely that the findings align with students' maturation, increase in working memory (which is required to achieve tasks, Cowan, 2016), and executive functions over time.…”

Section: Limitationssupporting

confidence: 92%

“…As numerous researchers have put forward, instruments such as the BCTt and the cCTt should be combined with other forms of assessments in a systems of assessments (Grover et al, 2015 ; Román-González et al, 2019 ; Weintrop et al, 2021a ) to accurately measure the full range of competencies at play when considering CT (Brennan and Resnick, 2012 ; Piatti et al, 2022 ). The systems of assessments could therefore include other instruments which assess CT practices such as the test by Li et al ( 2021 ), employ direct observations of students' thought processes and strategies (Lye and Koh, 2014 ; Chevalier et al, 2020 ), or learning analytics and educational data mining techniques (Cock et al, 2021 ; Nasir et al, 2021 ; Zapata-Cáceres and Martín-Barroso, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners' CT test (BCTt) and the competent CT test (cCTt)

El-Hamamsy

Zapata-Cáceres

Marcelino³

et al. 2022

Front. Psychol.

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IntroductionWith the increasing amount of research around Computational Thinking (CT) and endeavors introducing CT into curricula worldwide, assessing CT at all levels of formal education is of utmost importance to ensure that CT-related learning objectives are met. This has contributed to a progressive increase in the number of validated and reliable CT assessments for K-12, including primary school. Researchers and practitioners are thus required to choose among multiple instruments, often overlapping in their age validity.MethodsIn this study, we compare the psychometric properties of two of these instruments: the Beginners' CT test (BCTt), developed for grades 1–6, and the competent CT test (cCTt), validated for grades 3–4. Classical Test Theory and Item Response Theory (IRT) were employed on data acquired from 575 students in grades 3–4 to compare the properties of the two instruments and refine the limits of their validity.ResultsThe findings (i) establish the detailed psychometric properties of the BCTt in grades 3–4 for the first time, and (ii) through a comparison with students from the same country, indicate that the cCTt should be preferred for grades 3–4 as the cCTt is able to discriminate between students of low and medium ability. Conversely, while the BCTt, which is easier, shows a ceiling effect, it is better suited to discriminate between students in the low ability range. For these grades, the BCTt can thus be employed as a screening mechanism to identify low ability students.DiscussionIn addition to providing recomendations for use of these instruments, the findings highlight the importance of comparing the psychometric properties of existing assessments, so that researchers and practitioners, including teachers and policy makers involved in digital education curricular reforms, may take informed decisions when selecting assessments.

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Section: Limitationssupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners' CT test (BCTt) and the competent CT test (cCTt)

El-Hamamsy

Zapata-Cáceres

Marcelino³

et al. 2022

Front. Psychol.

Self Cite

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show abstract

Section: Introductionmentioning

confidence: 99%

“…To illustrate this approach, we focus on the activity proposed in [16] for the standardised assessment of algorithmic skills along the entire K-12 school path. We compare four learner models based on different assumptions and sets of expertelicited parameters and apply them to the dataset collected in [16]. Overall, we obtain a general and compact approach to implementing a learner model given a set of competencies of interest and the corresponding assessment rubric.…”

Section: Introductionmentioning

confidence: 99%

“…• Section II-C illustrates how to model a generic assessment rubric by Bayesian Networks. • Section III presents the case study and, in particular, the specific assessment rubric developed for the Cross Array Task (CAT) from [16] as well as the procedure for translating it into a learner model. • Section IV discusses the model outcomes based on the dataset of 109 pupils collected in [16].…”

Section: Introductionmentioning

confidence: 99%

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

Adorni

Mangili

Piatti

et al. 2023

JCOMSS

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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.

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Assessing Computational Thinking: The Relation of Different Assessment Instruments and Learning Tools

Masiulionytė-Dagienė

Jevsikova

2022

Informatics in Schools. A Step Beyond Digital Education

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The CT-cube: A framework for the design and the assessment of computational thinking activities

Cited by 17 publications

References 21 publications

Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners' CT test (BCTt) and the competent CT test (cCTt)

Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners' CT test (BCTt) and the competent CT test (cCTt)

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

Assessing Computational Thinking: The Relation of Different Assessment Instruments and Learning Tools

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