A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model
To further solve the problems of storage bottlenecks and excessive calculation time when calculating estimators under two different formats of massive longitudinal data, an examination data analysis and evaluation method based on an improved linear mixed-effects model is proposed in this paper. Firs...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/3752598 |
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| _version_ | 1832561432981405696 |
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| author | Jing Zhao Yiwen Wang |
| author_facet | Jing Zhao Yiwen Wang |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | To further solve the problems of storage bottlenecks and excessive calculation time when calculating estimators under two different formats of massive longitudinal data, an examination data analysis and evaluation method based on an improved linear mixed-effects model is proposed in this paper. First, a three-step estimation method is proposed to improve the parameters of the linear-effects model, avoiding the complicated iterative steps of maximum likelihood estimation. Second, we perform spectral clustering based on test data on the basis of defining data attributes and basic evaluation rules. Finally, based on cloud technology, a cross-regional, multiuser educational examination big data analysis and evaluation service platform is developed for evaluating the proposed method. Experimental results have shown that the proposed model can not only effectively improve the efficiency of test data acquisition and storage but also reduce the computational burden and the memory usage, solve the problem of insufficient memory, and increase the calculation speed. |
| format | Article |
| id | doaj-art-6b95cf424d9741a2a6c96cee18fc0759 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6b95cf424d9741a2a6c96cee18fc07592025-02-03T01:25:01ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/37525983752598A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects ModelJing Zhao0Yiwen Wang1Beijing Normal University, Business School, Beijing 100875, ChinaBeijing Normal University, Faculty of Education, Beijing 100875, ChinaTo further solve the problems of storage bottlenecks and excessive calculation time when calculating estimators under two different formats of massive longitudinal data, an examination data analysis and evaluation method based on an improved linear mixed-effects model is proposed in this paper. First, a three-step estimation method is proposed to improve the parameters of the linear-effects model, avoiding the complicated iterative steps of maximum likelihood estimation. Second, we perform spectral clustering based on test data on the basis of defining data attributes and basic evaluation rules. Finally, based on cloud technology, a cross-regional, multiuser educational examination big data analysis and evaluation service platform is developed for evaluating the proposed method. Experimental results have shown that the proposed model can not only effectively improve the efficiency of test data acquisition and storage but also reduce the computational burden and the memory usage, solve the problem of insufficient memory, and increase the calculation speed.http://dx.doi.org/10.1155/2021/3752598 |
| spellingShingle | Jing Zhao Yiwen Wang A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model Complexity |
| title | A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model |
| title_full | A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model |
| title_fullStr | A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model |
| title_full_unstemmed | A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model |
| title_short | A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model |
| title_sort | novel massive big data analysis of educational examination research using a linear mixed effects model |
| url | http://dx.doi.org/10.1155/2021/3752598 |
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