The role of emotion regulation in predicting emotional engagement mediated by meta-emotion in online learning environments: a two-stage SEM-ANN approach

Abstract

Understanding the mechanism of emotion regulation and the formation of emotional engagement can improve online learning persistence and academic performance. This study was set to pinpoint the potential pathways between emotion regulation and emotional engagement through meta-emotion and develop a predictive model for online emotional engagement. The data collected from 302 college students were analysed using a two-stage structural equation modelling-artificial neural network approach. Firstly, the path analysis implied the significant linkages from emotion regulation to emotional engagement through emotional repair. Secondly, the artificial neural network analysis results suggested that emotional repair contributed to the development of emotional engagement most, and the current model predicted emotional engagement with an accuracy of 91.1%. The main contribution of the present study is providing empirical evidence to predict emotional engagement from novel perspectives through a two-stage approach.

Keywords:

• Emotional engagement
• emotion regulation
• meta-emotion
• artificial neural network
• online learning

Authors’ contribution

Changqin Huang: Conceptualisation, Methodology, Writing-original draft, Writing-review & editing. Linjie Zhang: Conceptualisation, Methodology, Formal analysis, Writing-original draft. Tao He: Conceptualisation, Formal analysis, Writing-review & editing. Xuemei Wu: Conceptualisation, Data Collection, Writing-original draft. Yafeng Pan: Formal analysis, Writing-review & editing. Zhongmei Han: Conceptualisation, Writing-review & editing. Wenzhu Zhao: Conceptualisation, Data Collection. All authors approved the manuscript for submission.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the National Natural Science Foundation of China under Grant 62337001, the National Key R&D Program of China under Grant 2022ZD0117104, and the Key R&D Program of Zhejiang Province under Grant 2022C03106.