How does social support detected automatically in discussion forums relate to online learning burnout? The moderating role of students’ self-regulated learning

Abstract

Engaging students in online discussion forums with social support holds significant potential for preventing and alleviating student burnout. However, the mechanisms by which different types of social support influence learning burnout remain poorly understood. Additionally, existing methods for detecting social support detection are limited in both practical application and theoretical advancement. This study addresses these gaps by developing a robust text classification model for social support and examining its effects on online learning burnout among learners with varying levels of self-regulated learning. We first developed a robust natural language processing model based on bidirectional encoder representations from transformers - bidirectional long short-term memory (BERT-Bi-LSTM) framework, trained on 11226 manually labeled posts from various course forums. This model was then applied to classify forum posts from an educational technology course over one semester. Multiple regression analysis revealed that informational support was negatively associated with two dimensions of learning burnout: emotional exhaustion and improper behavior, and emotional support was negatively correlated with emotional exhaustion and a low sense of achievement. Moreover, a moderating effect analysis indicated that self-regulated learning moderated the negative associations between informational support and improper behavior, as well as between emotional support and emotional exhaustion, with stronger effects observed among learners with lower self-regulated learning. These findings contribute to advancing automated content analysis of social support and provide actionable insights for mitigating student burnout through targeted social support.