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Prediction of the Trend of Higher Education Development Using a Weakening Buffer Operator-Based Gm (1, 1) Model

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Title: Prediction of the Trend of Higher Education Development Using a Weakening Buffer Operator-Based Gm (1, 1) Model
Language: English
Authors: Linyan Li (ORCID 0000-0002-9620-6884), Xiao Bai (ORCID 0000-0001-5226-5068), Hongshan Xia (ORCID 0000-0001-5414-5756)
Source: Education and Information Technologies. 2024 29(2):2523-2538.
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: 16
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Prediction, Educational Trends, Higher Education, Models, Educational Development, Enrollment Rate, Accuracy, Foreign Countries
Geographic Terms: Kazakhstan
DOI: 10.1007/s10639-023-11762-0
ISSN: 1360-2357
1573-7608
Abstract: The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross enrollment rate (GER) of higher education as the subject of study, which eliminates the disturbance of the shock perturbation system and increases prediction accuracy. Seven models with varying sample sizes are constructed. It is discovered that the short sequence prediction model outperforms the long sequence prediction model. To demonstrate the superiority of the proposed method, cubic curves and logistic models are chosen for comparison. The results of the study revealed that the cubic curve has a better fitting, but the prediction results are overly large due to the quick growth rate of the recent raw data, which is not in line with the realistic development; the logistic model has poor fitting and cannot be used for prediction; the buffer operator-based GM (1, 1) model can effectively deal with the issue of missing data or data outliers, and provide accurate predictions of the trend of higher education development. When compared to other methods, the proposed method is more practicable, reliable, and superior.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1411664
Database: ERIC
FullText Text:
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  Data: Prediction of the Trend of Higher Education Development Using a Weakening Buffer Operator-Based Gm (1, 1) Model
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  Data: <searchLink fieldCode="AR" term="%22Linyan+Li%22">Linyan Li</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9620-6884">0000-0002-9620-6884</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xiao+Bai%22">Xiao Bai</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5226-5068">0000-0001-5226-5068</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hongshan+Xia%22">Hongshan Xia</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5414-5756">0000-0001-5414-5756</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Education+and+Information+Technologies%22"><i>Education and Information Technologies</i></searchLink>. 2024 29(2):2523-2538.
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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: 16
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  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Trends%22">Educational Trends</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Development%22">Educational Development</searchLink><br /><searchLink fieldCode="DE" term="%22Enrollment+Rate%22">Enrollment Rate</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Kazakhstan%22">Kazakhstan</searchLink>
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  Data: 10.1007/s10639-023-11762-0
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  Data: 1360-2357<br />1573-7608
– Name: Abstract
  Label: Abstract
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  Data: The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross enrollment rate (GER) of higher education as the subject of study, which eliminates the disturbance of the shock perturbation system and increases prediction accuracy. Seven models with varying sample sizes are constructed. It is discovered that the short sequence prediction model outperforms the long sequence prediction model. To demonstrate the superiority of the proposed method, cubic curves and logistic models are chosen for comparison. The results of the study revealed that the cubic curve has a better fitting, but the prediction results are overly large due to the quick growth rate of the recent raw data, which is not in line with the realistic development; the logistic model has poor fitting and cannot be used for prediction; the buffer operator-based GM (1, 1) model can effectively deal with the issue of missing data or data outliers, and provide accurate predictions of the trend of higher education development. When compared to other methods, the proposed method is more practicable, reliable, and superior.
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      – SubjectFull: Educational Trends
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      – SubjectFull: Higher Education
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      – SubjectFull: Educational Development
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      – SubjectFull: Enrollment Rate
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      – SubjectFull: Accuracy
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Kazakhstan
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