Multi-dimensional Data Optimal Classification Algorithm for Quality Evaluation of Distance Teaching in Universities.
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| Title: | Multi-dimensional Data Optimal Classification Algorithm for Quality Evaluation of Distance Teaching in Universities. |
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| Authors: | Chen, Feng1 (AUTHOR), Gadekallu, Thippa Reddy2,3,4,5,6 (AUTHOR) thippareddy@ieee.org |
| Source: | Mobile Networks & Applications. Jun2023, Vol. 28 Issue 3, p889-899. 11p. |
| Subjects: | Classification algorithms, OLAP technology, College teaching, Effective teaching, Data warehousing, High-dimensional model representation |
| Abstract: | In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into SVM algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of SVM algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation. [ABSTRACT FROM AUTHOR] |
| Copyright of Mobile Networks & Applications 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: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 177043581 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-dimensional Data Optimal Classification Algorithm for Quality Evaluation of Distance Teaching in Universities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Feng%22">Chen, Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gadekallu%2C+Thippa+Reddy%22">Gadekallu, Thippa Reddy</searchLink><relatesTo>2,3,4,5,6</relatesTo> (AUTHOR)<i> thippareddy@ieee.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mobile+Networks+%26+Applications%22">Mobile Networks & Applications</searchLink>. Jun2023, Vol. 28 Issue 3, p889-899. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22OLAP+technology%22">OLAP technology</searchLink><br /><searchLink fieldCode="DE" term="%22College+teaching%22">College teaching</searchLink><br /><searchLink fieldCode="DE" term="%22Effective+teaching%22">Effective teaching</searchLink><br /><searchLink fieldCode="DE" term="%22Data+warehousing%22">Data warehousing</searchLink><br /><searchLink fieldCode="DE" term="%22High-dimensional+model+representation%22">High-dimensional model representation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into SVM algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of SVM algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mobile Networks & Applications 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11036-023-02186-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 889 Subjects: – SubjectFull: Classification algorithms Type: general – SubjectFull: OLAP technology Type: general – SubjectFull: College teaching Type: general – SubjectFull: Effective teaching Type: general – SubjectFull: Data warehousing Type: general – SubjectFull: High-dimensional model representation Type: general Titles: – TitleFull: Multi-dimensional Data Optimal Classification Algorithm for Quality Evaluation of Distance Teaching in Universities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Feng – PersonEntity: Name: NameFull: Gadekallu, Thippa Reddy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 1383469X Numbering: – Type: volume Value: 28 – Type: issue Value: 3 Titles: – TitleFull: Mobile Networks & Applications Type: main |
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