Online learning behavior analysis based on machine learning
Purpose - The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.Design/methodology/approach - The prediction label in this paper is the course gra...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Emerald Publishing
2019-12-01
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| Series: | AAOU Journal |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/AAOUJ-08-2019-0029/full/pdf?title=online-learning-behavior-analysis-based-on-machine-learning |
| _version_ | 1797708711003684864 |
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| author | Ning Yan Oliver Tat-Sheung Au |
| author_facet | Ning Yan Oliver Tat-Sheung Au |
| author_sort | Ning Yan |
| collection | DOAJ |
| description | Purpose - The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.Design/methodology/approach - The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings - Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value - This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue. |
| first_indexed | 2024-03-12T06:25:29Z |
| format | Article |
| id | doaj.art-f590f343d1214f04b2fe2c418d3588ac |
| institution | Directory Open Access Journal |
| issn | 1858-3431 2414-6994 |
| language | English |
| last_indexed | 2024-03-12T06:25:29Z |
| publishDate | 2019-12-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | AAOU Journal |
| spelling | doaj.art-f590f343d1214f04b2fe2c418d3588ac2023-09-03T01:58:10ZengEmerald PublishingAAOU Journal1858-34312414-69942019-12-011429710610.1108/AAOUJ-08-2019-0029635548Online learning behavior analysis based on machine learningNing Yan0Oliver Tat-Sheung Au1Shanghai Open University, Shanghai, ChinaOpen University of Hong Kong, Kowloon, Hong KongPurpose - The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.Design/methodology/approach - The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings - Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value - This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.https://www.emerald.com/insight/content/doi/10.1108/AAOUJ-08-2019-0029/full/pdf?title=online-learning-behavior-analysis-based-on-machine-learningmachine learningonline learninglearning behaviour analysis |
| spellingShingle | Ning Yan Oliver Tat-Sheung Au Online learning behavior analysis based on machine learning AAOU Journal machine learning online learning learning behaviour analysis |
| title | Online learning behavior analysis based on machine learning |
| title_full | Online learning behavior analysis based on machine learning |
| title_fullStr | Online learning behavior analysis based on machine learning |
| title_full_unstemmed | Online learning behavior analysis based on machine learning |
| title_short | Online learning behavior analysis based on machine learning |
| title_sort | online learning behavior analysis based on machine learning |
| topic | machine learning online learning learning behaviour analysis |
| url | https://www.emerald.com/insight/content/doi/10.1108/AAOUJ-08-2019-0029/full/pdf?title=online-learning-behavior-analysis-based-on-machine-learning |
| work_keys_str_mv | AT ningyan onlinelearningbehavioranalysisbasedonmachinelearning AT olivertatsheungau onlinelearningbehavioranalysisbasedonmachinelearning |