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A Literature Review on the Empirical Studies of the Integration of Mathematics and Computational Thinking

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Title: A Literature Review on the Empirical Studies of the Integration of Mathematics and Computational Thinking
Language: English
Authors: Lv, Lin, Zhong, Baichang (ORCID 0000-0001-5837-4433), Liu, Xiaofan
Source: Education and Information Technologies. Jul 2023 28(7):8171-8193.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2023
Document Type: Journal Articles
Information Analyses
Education Level: Elementary Secondary Education
Descriptors: Literature Reviews, Mathematics Education, Integrated Curriculum, Computation, Thinking Skills, Elementary Secondary Education, Research Methodology
DOI: 10.1007/s10639-022-11518-2
ISSN: 1360-2357
1573-7608
Abstract: In K-12 education, Computational Thinking (CT) has been a trendy issue in mathematics education, but the approach and results of CT + Math are not yet clear enough. This paper systematically reviewed 22 SSCI journal papers from three perspectives: the current status, outcomes, and implications of mathematics and CT integration. Results indicate that: (1) The empirical studies were more inclined to be carried out in primary school; (2) The sample size inversely proportional to the duration, the same as the duration and the learning phase; (3) the integration of mathematics and CT were gradually emerging in kindergartens, while the empirical studies in junior and senior high schools still needs to be improved; (4) The experimental type prioritizes case studies and lacks mixed research; (5) Most research designs employ a variety of measuring instruments but limited in multimodal data; (6) Through the teaching model of plug-in programming, the integration of mathematics and CT was centred on the field of geometry and number operations; and (7) The CT skills involved are mainly Problem decomposition, Pattern recognition, Abstraction, Algorithm design and Debugging. The limitations and future directions are also discussed in this paper.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1383222
Database: ERIC
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  Data: A Literature Review on the Empirical Studies of the Integration of Mathematics and Computational Thinking
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  Data: <searchLink fieldCode="AR" term="%22Lv%2C+Lin%22">Lv, Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhong%2C+Baichang%22">Zhong, Baichang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5837-4433">0000-0001-5837-4433</externalLink>)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaofan%22">Liu, Xiaofan</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: In K-12 education, Computational Thinking (CT) has been a trendy issue in mathematics education, but the approach and results of CT + Math are not yet clear enough. This paper systematically reviewed 22 SSCI journal papers from three perspectives: the current status, outcomes, and implications of mathematics and CT integration. Results indicate that: (1) The empirical studies were more inclined to be carried out in primary school; (2) The sample size inversely proportional to the duration, the same as the duration and the learning phase; (3) the integration of mathematics and CT were gradually emerging in kindergartens, while the empirical studies in junior and senior high schools still needs to be improved; (4) The experimental type prioritizes case studies and lacks mixed research; (5) Most research designs employ a variety of measuring instruments but limited in multimodal data; (6) Through the teaching model of plug-in programming, the integration of mathematics and CT was centred on the field of geometry and number operations; and (7) The CT skills involved are mainly Problem decomposition, Pattern recognition, Abstraction, Algorithm design and Debugging. The limitations and future directions are also discussed in this paper.
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