在 K-12 教育中培养算法思维能力:不插电和数字评估工具的比较研究

IF 4.9 Q1 PSYCHOLOGY, EXPERIMENTAL Computers in human behavior reports Pub Date : 2024-08-01 DOI:10.1016/j.chbr.2024.100466
Giorgia Adorni , Igor Artico , Alberto Piatti , Elia Lutz , Luca Maria Gambardella , Lucio Negrini , Francesco Mondada , Dorit Assaf
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引用次数: 0

摘要

在数字能力迅速发展的今天,我们越来越需要一种强大而通用的方法来评估学生的算法思维(AT)技能。算法思维指的是分析问题并逐步解决问题的能力。本研究调查了交叉阵列任务(CAT)作为瑞士义务教育系统中算法思维能力评估工具的有效性。交叉阵列任务最初的概念是一种不插电的活动,学生在没有数字技术的情况下完成任务(例如,在纸上使用手势),由管理员对他们进行人工评估。CAT 的数字化转型实现了学生答卷评分和数据收集的自动化,简化了评估流程,提高了大规模评估的效率。它还增强了可扩展性,使 CAT 适合在教育环境中广泛使用。此外,它还能为学生和教育工作者提供即时反馈,支持及时干预和个性化学习体验。我们的研究旨在全面调查义务教育中的算法能力,研究其变化和影响因素。这项研究考察了瑞士的年龄、性别、教育环境和学校特点(如教育水平和年级)、地区因素(如学校所在州)等关键变量,以及与具体评估工具相关的特点,包括所用人工制品的类型、生成算法的复杂性和自主程度。此外,它还试图分析不插电和数字方法在评估 AT 技能方面的有效性,特别是比较不插电和虚拟 CAT 版本,旨在深入了解它们的优势和潜在的协同作用。这项调查描述了 AT 技能在义务教育阶段的发展进程,强调了年龄对算法开发和解决问题策略的影响。此外,我们还揭示了人工制品的影响和数字工具的潜力,以促进不同年龄组的高级信息与传播技术技能发展。最后,我们的研究深入探讨了学校环境和性别差异对智能辅助学习成绩的影响,以及受个人能力和外部环境影响的显著个体差异。这些发现强调了有针对性的教育干预和公平实践的重要性,以适应不同的学习情况,优化学生在不同教育环境中的智能辅助学习成绩。
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Development of algorithmic thinking skills in K-12 education: A comparative study of unplugged and digital assessment instruments

In the rapidly evolving landscape of digital competencies, the need for a robust and universal method to assess students’ algorithmic thinking (AT) skills has become increasingly pronounced. Algorithmic thinking refers to the ability to analyse a problem and develop a step-by-step process to solve it.

This research investigates the efficacy of the Cross Array Task (CAT) as an assessment tool for AT skills within Switzerland’s compulsory education system. Originally conceptualised as an unplugged activity, where students performed the task without digital technologies (e.g., by using gestures on paper) and an administrator manually assessed them, the CAT evolved into a digital activity that runs on an iPad. The CAT’s digital transformation has automated the scoring of student responses and data collection, streamlining the assessment processes and facilitating efficient large-scale assessments. It has also enhanced scalability, making the CAT suitable for widespread use in educational settings. Furthermore, it provides immediate feedback to students and educators, supporting timely interventions and personalised learning experiences.

Our study aims to comprehensively investigate algorithmic competencies in compulsory education, examining their variations and influencing factors. This research examines key variables, such as age, sex, educational environment and school characteristics (e.g., the level and grade of education), and regional factors (e.g., the canton of the school) in Switzerland, and characteristics related to the specific assessment tool, including the type of artefact used, the complexity of the algorithms generated, and the level of autonomy. Additionally, it seeks to analyse the effectiveness of the unplugged and digital approaches in assessing AT skills, specifically comparing the unplugged and virtual CAT versions, aiming to provide insights into their advantages and potential synergies.

This investigation delineates the developmental progression of AT skills across compulsory education, emphasising the influence of age on algorithm development and problem-solving strategies. Furthermore, we reveal the impact of artefacts and the potential of digital tools to facilitate advanced AT skill development across diverse age groups. Finally, our investigation delves into the influence of school environments and sex disparities on AT performance, alongside the significant individual variability influenced by personal abilities and external circumstances.

These findings underscore the importance of tailored educational interventions and equitable practices to accommodate diverse learning profiles and optimise student outcomes in AT across educational settings.

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