Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach
As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this ch...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2522 |
| _version_ | 1827307165805707264 |
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| author | Zhaoyu Shou Mingquan Xie Jianwen Mo Huibing Zhang |
| author_facet | Zhaoyu Shou Mingquan Xie Jianwen Mo Huibing Zhang |
| author_sort | Zhaoyu Shou |
| collection | DOAJ |
| description | As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance. |
| first_indexed | 2024-04-24T18:34:37Z |
| format | Article |
| id | doaj.art-9317f8f70500429da5b5281c122e604d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:34:37Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9317f8f70500429da5b5281c122e604d2024-03-27T13:19:59ZengMDPI AGApplied Sciences2076-34172024-03-01146252210.3390/app14062522Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis ApproachZhaoyu Shou0Mingquan Xie1Jianwen Mo2Huibing Zhang3Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaAs an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance.https://www.mdpi.com/2076-3417/14/6/2522online learningmultidimensional time-series datastudent performance predictionmulti-classification predictionindividualized educationearly prediction |
| spellingShingle | Zhaoyu Shou Mingquan Xie Jianwen Mo Huibing Zhang Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach Applied Sciences online learning multidimensional time-series data student performance prediction multi-classification prediction individualized education early prediction |
| title | Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach |
| title_full | Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach |
| title_fullStr | Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach |
| title_full_unstemmed | Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach |
| title_short | Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach |
| title_sort | predicting student performance in online learning a multidimensional time series data analysis approach |
| topic | online learning multidimensional time-series data student performance prediction multi-classification prediction individualized education early prediction |
| url | https://www.mdpi.com/2076-3417/14/6/2522 |
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