The Effects of Algorithm Visualisation on Elementary School Students' Algorithm‐Learning Performance, Motivation, and Behaviour.
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...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 3; pp. 1 - 16 |
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| Autores principales: | , , , |
| Formato: | computer program pictorial research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185452190&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185452190 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Jun2025 vid: 41 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 185452190 185452190 185452190 10.1111/jcal.70049 185452190 ppf: 1 ppct: 15 formats: tig: atl: The Effects of Algorithm Visualisation on Elementary School Students' Algorithm‐Learning Performance, Motivation, and Behaviour. aug: au: Fu, Qian Zhou, Xinyi Zheng, Yafeng Wang, Zhenyi affil: School of Educational Technology, Beijing Normal University, Beijing, China sug: subj: Students, Elementary Learning Methods Algorithms Videorecording Motion Pictures Academic Performance Evaluation Motivation Evaluation Child Behavior Evaluation Human China Funding Source Quasi-Experimental Studies Randomized Controlled Trials Random Assignment Child Questionnaires Competency Assessment Simple Random Sample Male Female Analysis of Covariance Descriptive Statistics Kruskal-Wallis Test Chi Square Test Post Hoc Analysis Confidence Intervals Child: 6-12 years Male Female ab: 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. pubtype: Academic Journal doctype: computer program pictorial research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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