Academic Journal
Research on art innovation teaching platform based on data mining algorithm.
| Τίτλος: | Research on art innovation teaching platform based on data mining algorithm. |
|---|---|
| Συγγραφείς: | Li, Gang, Wang, Fang |
| Πηγή: | Cluster Computing; Nov2019 Supplement 6, Vol. 22, p13867-13872, 6p |
| Θεματικοί όροι: | Data mining, Instructional innovations, Educational standards, Psychology of students, Decision trees |
| Περίληψη: | The art teaching has been paid more and more attention. And a series of training standards and achievement standards for the education curriculum had been formulated by the Ministry of Education. Based on this, this paper introduces the data mining technology for the artistic achievement evaluation. Firstly, the ID3 algorithm of the art teaching achievement mining decision tree has been built, then Comb the Data Flow in Algorithm. Secondly, test the algorithm concerned with the students' art test scores to analyze the data mining. Finally, we get the valuable student characteristics information, which indicates that the algorithm constructed in this paper has applicability and it can serve the art teaching in schools very well. [ABSTRACT FROM AUTHOR] |
| Copyright of Cluster Computing 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10586-018-2119-x Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 139866517 RelevancyScore: 886 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 885.557678222656 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Research on art innovation teaching platform based on data mining algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Gang%22">Li, Gang</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Fang%22">Wang, Fang</searchLink> – Name: TitleSource Label: Source Group: Src Data: Cluster Computing; Nov2019 Supplement 6, Vol. 22, p13867-13872, 6p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+innovations%22">Instructional innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+standards%22">Educational standards</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The art teaching has been paid more and more attention. And a series of training standards and achievement standards for the education curriculum had been formulated by the Ministry of Education. Based on this, this paper introduces the data mining technology for the artistic achievement evaluation. Firstly, the ID3 algorithm of the art teaching achievement mining decision tree has been built, then Comb the Data Flow in Algorithm. Secondly, test the algorithm concerned with the students' art test scores to analyze the data mining. Finally, we get the valuable student characteristics information, which indicates that the algorithm constructed in this paper has applicability and it can serve the art teaching in schools very well. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Cluster Computing 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=edb&AN=139866517 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10586-018-2119-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 13867 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Instructional innovations Type: general – SubjectFull: Educational standards Type: general – SubjectFull: Psychology of students Type: general – SubjectFull: Decision trees Type: general Titles: – TitleFull: Research on art innovation teaching platform based on data mining algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Gang – PersonEntity: Name: NameFull: Wang, Fang IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 11 Text: Nov2019 Supplement 6 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 13867857 Numbering: – Type: volume Value: 22 Titles: – TitleFull: Cluster Computing Type: main |
| ResultId | 1 |