Exploring the relationship between social presence and learners’ prestige in MOOC discussion forums using automated content analysis and social network analysis

Wenting Zou, Xiao Hu, Zilong Pan, Chenglu Li, Ying Cai, Min Liu

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Research has repeatedly proven the importance of social interactions in online learning contexts such as Massive Open Online Courses (MOOCs), where learners often reported isolation and a lack of peer support. Previous studies of social presence suggested that the ways learners present themselves socially online affect their learning outcomes. In order to further understand the role of learners' social presence, this study attempts to examine the relationship between social presence and learners' prestige in the learner network of a MOOC. An automated text classification model based on the latest machine learning techniques was developed to identify different social presence indicators from forum posts, while two metrics (in-degree and authority score) in social network analysis (SNA) were used to measure learners' prestige in the learner network. Results revealed that certain social presence indicators such as Asking questions, Expressing gratitude, Self-disclosure, Sharing resources and Using Vocatives have positive correlations with learners' prestige, while the expressions of Disagreement/doubts/criticism and Negative emotions were counterproductive to learners' prestige. The findings not only reinforce the importance of social presence in online learning, but also shed light on the strategies of leveraging social presence to improve individual's prestige in social learning contexts like MOOCs.

Original languageEnglish (US)
Article number106582
JournalComputers in Human Behavior
Volume115
DOIs
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • General Psychology

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