MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy
In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6629 |
| _version_ | 1797515687490486272 |
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| author | Xiaoliang Zhu Yuanxin Ye Liang Zhao Chen Shen |
| author_facet | Xiaoliang Zhu Yuanxin Ye Liang Zhao Chen Shen |
| author_sort | Xiaoliang Zhu |
| collection | DOAJ |
| description | In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance. |
| first_indexed | 2024-03-10T06:50:48Z |
| format | Article |
| id | doaj.art-140daaa5d33d4e90ae2003c8da299a34 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:50:48Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-140daaa5d33d4e90ae2003c8da299a342023-11-22T16:49:00ZengMDPI AGSensors1424-82202021-10-012119662910.3390/s21196629MOOC Behavior Analysis and Academic Performance Prediction Based on EntropyXiaoliang Zhu0Yuanxin Ye1Liang Zhao2Chen Shen3National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, ChinaNational Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, ChinaNational Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, ChinaNational Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, ChinaIn recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.https://www.mdpi.com/1424-8220/21/19/6629MOOCsdata miningacademic performance prediction |
| spellingShingle | Xiaoliang Zhu Yuanxin Ye Liang Zhao Chen Shen MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy Sensors MOOCs data mining academic performance prediction |
| title | MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy |
| title_full | MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy |
| title_fullStr | MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy |
| title_full_unstemmed | MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy |
| title_short | MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy |
| title_sort | mooc behavior analysis and academic performance prediction based on entropy |
| topic | MOOCs data mining academic performance prediction |
| url | https://www.mdpi.com/1424-8220/21/19/6629 |
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