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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.16907 |
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| _version_ | 1866917731710795776 |
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| author | Wang, Chun Chen, Jiexiao Xie, Ziyang Zou, Jianke |
| author_facet | Wang, Chun Chen, Jiexiao Xie, Ziyang Zou, Jianke |
| contents | The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16907 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning Wang, Chun Chen, Jiexiao Xie, Ziyang Zou, Jianke Computers and Society Machine Learning The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results. |
| title | Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2407.16907 |