TY - JOUR
T1 - Online learning behavior analysis based on machine learning
AU - Yan, Ning
AU - Au, Oliver Tat Sheung
N1 - Funding Information:
This work is supported by the Croucher Chinese Visiting Scholar Project (2018–2019). The authors would like to thank the Croucher Foundation and the Open University of Hong Kong for providing the valuable research opportunity.
Publisher Copyright:
© 2019, Ning Yan and Oliver Tat-Sheung Au.
PY - 2019/12/5
Y1 - 2019/12/5
N2 - 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.
AB - 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.
KW - Learning behaviour analysis
KW - Machine learning
KW - Online learning
UR - http://www.scopus.com/inward/record.url?scp=85101430547&partnerID=8YFLogxK
U2 - 10.1108/AAOUJ-08-2019-0029
DO - 10.1108/AAOUJ-08-2019-0029
M3 - Article
AN - SCOPUS:85101430547
SN - 1858-3431
VL - 14
SP - 97
EP - 106
JO - Asian Association of Open Universities Journal
JF - Asian Association of Open Universities Journal
IS - 2
ER -