The virtual CAT: A tool for algorithmic thinking assessment in Swiss compulsory education

• URL: http://arxiv.org/abs/2408.01263v2
• Date: Tue, 27 Aug 2024 16:02:48 GMT
• Title: The virtual CAT: A tool for algorithmic thinking assessment in Swiss compulsory education
• Authors: Giorgia Adorni, Alberto Piatti,
• Abstract summary: This paper introduces the virtual Cross Array Task (CAT), a digital adaptation of an unplugged assessment activity designed to evaluate algorithmic skills in Swiss compulsory education. The platform offers scalable and automated assessment, reducing human involvement and mitigating potential data collection errors. The findings show the platform's usability, proficiency and suitability for assessing AT skills among students of diverse ages, development stages, and educational backgrounds.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In today's digital era, holding algorithmic thinking (AT) skills is crucial, not only in computer science-related fields. These abilities enable individuals to break down complex problems into more manageable steps and create a sequence of actions to solve them. To address the increasing demand for AT assessments in educational settings and the limitations of current methods, this paper introduces the virtual Cross Array Task (CAT), a digital adaptation of an unplugged assessment activity designed to evaluate algorithmic skills in Swiss compulsory education. This tool offers scalable and automated assessment, reducing human involvement and mitigating potential data collection errors. The platform features gesture-based and visual block-based programming interfaces, ensuring its usability for diverse learners, further supported by multilingual capabilities. To evaluate the virtual CAT platform, we conducted a pilot evaluation in Switzerland involving a heterogeneous group of students. The findings show the platform's usability, proficiency and suitability for assessing AT skills among students of diverse ages, development stages, and educational backgrounds, as well as the feasibility of large-scale data collection.

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