A Systematic Review on Data Mining for Mathematics and Science Education.
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| Title: | A Systematic Review on Data Mining for Mathematics and Science Education. |
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| Authors: | Shin, Dongjo1 (AUTHOR), Shim, Jaekwoun1 (AUTHOR) jaekwoun.shim@gmail.com |
| Source: | International Journal of Science & Mathematics Education. Apr2021, Vol. 19 Issue 4, p639-659. 21p. |
| Subject Terms: | *Science education, *Mathematics education, *Instructional systems, *Academic achievement, Data mining |
| Abstract: | Educational data mining is used to discover significant phenomena and resolve educational issues occurring in the context of teaching and learning. This study provides a systematic literature review of educational data mining in mathematics and science education. A total of 64 articles were reviewed in terms of the research topics and data mining techniques used. This review revealed that data mining in mathematics and science education has been commonly used to understand students' behavior and thinking process, identify factors affecting student achievements, and provide automated assessment of students' written work. Recently, researchers have tended to use such data mining techniques as text mining to develop learning systems for supporting teachers' instruction and students' learning. We also found that classification, text mining, and clustering are major data mining techniques researchers have used. Studies using data mining were more likely to be conducted in the field of science education than in the field of mathematics education. We discuss the main results of our review in comparison with the previous reviews of educational data mining (EDM) literature and with EDM studies conducted in the context of science and mathematics education. Finally, we provide implications for research and teaching and learning of science and mathematics and suggest potential research directions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | Educational data mining is used to discover significant phenomena and resolve educational issues occurring in the context of teaching and learning. This study provides a systematic literature review of educational data mining in mathematics and science education. A total of 64 articles were reviewed in terms of the research topics and data mining techniques used. This review revealed that data mining in mathematics and science education has been commonly used to understand students' behavior and thinking process, identify factors affecting student achievements, and provide automated assessment of students' written work. Recently, researchers have tended to use such data mining techniques as text mining to develop learning systems for supporting teachers' instruction and students' learning. We also found that classification, text mining, and clustering are major data mining techniques researchers have used. Studies using data mining were more likely to be conducted in the field of science education than in the field of mathematics education. We discuss the main results of our review in comparison with the previous reviews of educational data mining (EDM) literature and with EDM studies conducted in the context of science and mathematics education. Finally, we provide implications for research and teaching and learning of science and mathematics and suggest potential research directions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15710068 |
| DOI: | 10.1007/s10763-020-10085-7 |
