A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm
At present, the widespread use of online education platforms has attracted the attention of more and more people. The application of AI technology in online education platform makes multidimensional evaluation of students’ ability become the trend of intelligent education in the future. Currently, m...
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| Format: | Article |
| Language: | English |
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Hong Kong Bao Long Accounting & Secretarial Limited
2024-12-01
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| Series: | Knowledge Management & E-Learning: An International Journal |
| Subjects: | |
| Online Access: | https://www.kmel-journal.org/ojs/index.php/online-publication/article/view/607 |
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| author | Junfeng Man Rongke Zeng Xiangyang He Hua Jiang |
| author_facet | Junfeng Man Rongke Zeng Xiangyang He Hua Jiang |
| author_sort | Junfeng Man |
| collection | DOAJ |
| description | At present, the widespread use of online education platforms has attracted the attention of more and more people. The application of AI technology in online education platform makes multidimensional evaluation of students’ ability become the trend of intelligent education in the future. Currently, most existing studies are based on traditional statistical methods to rank and evaluate students’ achievement, but this will lead to problems of single type of data and the inability of intra-class evaluation. In order to solve above problems in traditional statistical methods, a multidimensional learning ability evaluation method is proposed in this paper, which is based on auto-encoder and quadratic K-means clustering algorithm. It will be applied to the domain of intelligence education to evaluate students’ multidimensional learning ability. First, this method uses auto-encoder (AE) to reconstruct the students’ learning behaviour features in order to improve the clustering effect, then performs k-means clustering twice on reconstruction data. By using clustering to address the issue that cannot be addressed within the category, it ranks and evaluates students. This research employs a real data set of a particular platform for comparative studies in order to assess the performance of this strategy on various data sets. The results of the experiments demonstrate that this method performs much better than both the conventional clustering algorithm and the PCA-based reconstruction clustering method. |
| format | Article |
| id | doaj-art-287193e1b5b14c6babd4e86d19e94ed5 |
| institution | Kabale University |
| issn | 2073-7904 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Hong Kong Bao Long Accounting & Secretarial Limited |
| record_format | Article |
| series | Knowledge Management & E-Learning: An International Journal |
| spelling | doaj-art-287193e1b5b14c6babd4e86d19e94ed52025-02-03T08:43:33ZengHong Kong Bao Long Accounting & Secretarial LimitedKnowledge Management & E-Learning: An International Journal2073-79042024-12-0116469771510.34105/j.kmel.2024.16.032A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithmJunfeng Man 0https://orcid.org/0000-0002-9867-178XRongke Zeng1https://orcid.org/0009-0006-6758-3767Xiangyang He2https://orcid.org/0009-0000-7220-6102Hua Jiang3https://orcid.org/0009-0008-1072-1908Hunan University of Technology, ChinaHunan University of Technology, ChinaHunan First Normal University, ChinaHunan First Normal University, ChinaAt present, the widespread use of online education platforms has attracted the attention of more and more people. The application of AI technology in online education platform makes multidimensional evaluation of students’ ability become the trend of intelligent education in the future. Currently, most existing studies are based on traditional statistical methods to rank and evaluate students’ achievement, but this will lead to problems of single type of data and the inability of intra-class evaluation. In order to solve above problems in traditional statistical methods, a multidimensional learning ability evaluation method is proposed in this paper, which is based on auto-encoder and quadratic K-means clustering algorithm. It will be applied to the domain of intelligence education to evaluate students’ multidimensional learning ability. First, this method uses auto-encoder (AE) to reconstruct the students’ learning behaviour features in order to improve the clustering effect, then performs k-means clustering twice on reconstruction data. By using clustering to address the issue that cannot be addressed within the category, it ranks and evaluates students. This research employs a real data set of a particular platform for comparative studies in order to assess the performance of this strategy on various data sets. The results of the experiments demonstrate that this method performs much better than both the conventional clustering algorithm and the PCA-based reconstruction clustering method.https://www.kmel-journal.org/ojs/index.php/online-publication/article/view/607intelligence educationlearner competence assessmentauto-encoderk-means clustering |
| spellingShingle | Junfeng Man Rongke Zeng Xiangyang He Hua Jiang A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm Knowledge Management & E-Learning: An International Journal intelligence education learner competence assessment auto-encoder k-means clustering |
| title | A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm |
| title_full | A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm |
| title_fullStr | A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm |
| title_full_unstemmed | A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm |
| title_short | A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm |
| title_sort | study on the assessment of learner ability based on the combination of auto encoder and quadratic k means clustering algorithm |
| topic | intelligence education learner competence assessment auto-encoder k-means clustering |
| url | https://www.kmel-journal.org/ojs/index.php/online-publication/article/view/607 |
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