ABSTRACT
Purpose: This paper aims to use a quantitative approach to explore the role of online learning behavior in students' academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context. Design/methodology/approach: Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis. Findings: The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly. Originality/value: Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students' online learning behavior to boost blended learning effects during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.
ABSTRACT
With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance.
ABSTRACT
With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance. [ FROM AUTHOR]
ABSTRACT
Purpose: This paper aims to use a quantitative approach to explore the role of online learning behavior in students’ academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context. Design/methodology/approach: Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis. Findings: The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly. Originality/value: Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students’ online learning behavior to boost blended learning effects during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.