The Effects of Algorithm Visualisation on Elementary School Students' Algorithm-Learning Performance, Motivation, and Behaviour

IF 5.1 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2025-05-21 DOI:10.1111/jcal.70049

Qian Fu, Xinyi Zhou, Yafeng Zheng, Zhenyi Wang

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引用次数: 0

Abstract

Background

Understanding algorithms is crucial for programming education, yet their abstract nature often challenges students. Algorithm visualisation (AV) has been proven effective in enhancing algorithmic thinking among university students. However, its efficacy for elementary school students and the optimal forms of AV tools remain unclear.

Objectives

This study aims to assess learners' performance, motivation, and behaviour under three AV forms (i.e., algorithm animation, static visualisation, and no visualisation) from both scientific and behavioural perspectives.

Methods

A quasiexperimental design was employed, involving 104 sixth-grade students (aged 11–12) from a K–12 school in eastern China. A 9-week algorithm-teaching activity covering the optimal path, enumeration, and search algorithms was conducted in an in-school extension class. Two experimental groups and one control group each used a different AV form. Quantitative data were collected through questionnaires and an algorithm competency test (ACT), whereas behavioural data were analysed from computer screen recordings and classroom video recordings.

Results and Conclusions

Although no significant differences were found in overall learning performance, algorithm animation was particularly beneficial for high-proficiency students. Algorithm animation and static visualisation significantly enhanced students' learning motivation compared with no visualisation. A behavioural analysis revealed that students using algorithm animation demonstrated greater autonomy and initiative, whereas those students who did not use visualisation preferred passive learning. This study on AV-based algorithm teaching concludes that introducing AV effectively improves students' initiative and motivation, providing insights for integrating visualisations in instructor-mediated classrooms.

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算法视觉化对小学生算法学习表现、动机及行为之影响

理解算法对于编程教育是至关重要的，然而它们的抽象性经常给学生带来挑战。算法可视化（Algorithm visualization， AV）已被证明对增强大学生的算法思维是有效的。然而，它对小学生的功效和最佳形式的AV工具仍不清楚。本研究旨在从科学和行为的角度评估三种AV形式（即算法动画、静态可视化和无可视化）下学习者的表现、动机和行为。方法采用准实验设计，对华东地区某小学6年级学生104名（11 ~ 12岁）进行调查。在校内扩展班进行了为期9周的算法教学活动，内容包括最优路径、枚举和搜索算法。两个实验组和一个对照组分别使用不同的AV形式。定量数据通过问卷调查和算法能力测试（ACT）收集，而行为数据则通过电脑屏幕录音和课堂录像进行分析。结果与结论虽然在整体学习成绩上没有发现显著差异，但算法动画对高水平学生特别有益。算法动画和静态可视化与无可视化相比，显著提高了学生的学习动机。一项行为分析显示，使用算法动画的学生表现出更大的自主性和主动性，而那些不使用可视化的学生则更喜欢被动学习。基于AV的算法教学研究得出结论，引入AV有效地提高了学生的主动性和积极性，为在教师介导的课堂中整合可视化提供了见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope