View in EDS HTML Full Text PDF Full Text

A Systematic Review on Data Mining for Mathematics and Science Education.

Saved in:
Bibliographic Details
Title: A Systematic Review on Data Mining for Mathematics and Science Education.
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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 149070704
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Systematic Review on Data Mining for Mathematics and Science Education.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shin%2C+Dongjo%22">Shin, Dongjo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shim%2C+Jaekwoun%22">Shim, Jaekwoun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jaekwoun.shim@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Science+%26+Mathematics+Education%22">International Journal of Science & Mathematics Education</searchLink>. Apr2021, Vol. 19 Issue 4, p639-659. 21p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Science+education%22">Science education</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematics+education%22">Mathematics education</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=149070704
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10763-020-10085-7
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 639
    Subjects:
      – SubjectFull: Science education
        Type: general
      – SubjectFull: Mathematics education
        Type: general
      – SubjectFull: Instructional systems
        Type: general
      – SubjectFull: Academic achievement
        Type: general
      – SubjectFull: Data mining
        Type: general
    Titles:
      – TitleFull: A Systematic Review on Data Mining for Mathematics and Science Education.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shin, Dongjo
      – PersonEntity:
          Name:
            NameFull: Shim, Jaekwoun
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 15710068
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: International Journal of Science & Mathematics Education
              Type: main
ResultId 1