FADE-CTP:教育计算思维问题分析与设计的框架。

IF 3 Q1 EDUCATION & EDUCATIONAL RESEARCH
Technology Knowledge and Learning Pub Date : 2025-01-01 Epub Date: 2025-03-10 DOI:10.1007/s10758-025-09833-x
Giorgia Adorni, Alberto Piatti, Engin Bumbacher, Lucio Negrini, Francesco Mondada, Dorit Assaf, Francesca Mangili, Luca Maria Gambardella
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引用次数: 0

摘要

近年来,随着数字化的加速发展,对计算思维(CT)的重视程度越来越高。当大多数研究人员专注于定义计算机思维问题和开发用于其指导和评估的工具时,我们关注的是计算思维问题(ctp)的特征——需要计算机思维解决的活动——以及它们如何影响学生能够发展的技能。在本文中,我们提出了一个全面的框架,通过识别特定的成分和特征来系统地分析ctp,同时在这些属性和CT能力的结构化目录之间建立联系。该框架的目的是:(i)促进对现有ctp的分析,以确定哪些能力可以根据其固有特征进行开发或测量,以及(ii)通过概述激活ctp所需的必要特征来指导针对特定技能的新ctp的设计。为了说明框架的功能,我们首先分析了文献中的原型活动,这一过程导致了跨不同领域ctp分类的定义,并以在不同认知环境中设置这些活动之一的不同版本的设计案例研究作为结论,交叉阵列任务(CAT)。这种方法可以让我们了解不同背景下的ctp如何表现出独特和反复出现的特征,从而促进不同技能的发展。综上所述,该框架可以为评估工具的开发提供信息,改善教师培训,促进对现有CT活动的分析和比较,有助于更深入地理解能力激活,指导CT教育的课程设计。补充信息:在线版本包含补充资料,提供地址:10.1007/s10758-025-09833-x。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

Supplementary information: The online version contains supplementary material available at 10.1007/s10758-025-09833-x.

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来源期刊
Technology Knowledge and Learning
Technology Knowledge and Learning EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
9.50
自引率
6.10%
发文量
43
期刊介绍: Technology, Knowledge and Learning emphasizes the increased interest on context-aware adaptive and personalized digital learning environments. Rapid technological developments have led to new research challenges focusing on digital learning, gamification, automated assessment and learning analytics. These emerging systems aim to provide learning experiences delivered via online environments as well as mobile devices and tailored to the educational needs, the personal characteristics and the particular circumstances of the individual learner or a (massive) group of interconnected learners. Such diverse learning experiences in real-world and virtual situations generates big data which provides rich potential for in-depth intelligent analysis and adaptive feedback as well as scaffolds whenever the learner needs it. Novel manuscripts are welcome that account for how these new technologies and systems reconfigure learning experiences, assessment methodologies as well as future educational practices. Technology, Knowledge and Learning also publishes guest-edited themed special issues linked to the emerging field of educational technology.    Submissions can be empirical investigations, work in progress studies or emerging technology reports. Empirical investigations report quantitative or qualitative research demonstrating advances in digital learning, gamification, automated assessment or learning analytics. Work-in-progress studies provide early insights into leading projects or document progressions of excellent research within the field of digital learning, gamification, automated assessment or learning analytics. Emerging technology reports review new developments in educational technology by assessing the potentials for leading digital learning environments.   Manuscripts submitted to Technology, Knowledge and Learning undergo a blind review process involving expert reviews and in-depth evaluations. Initial feedback is usually provided within eight weeks including in progress open-access abstracts and review snapshots.
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