Sustaining online academic discussions: Identifying the characteristics of messages that receive responses

Gaowei CHEN, Chung Kwan LO, Liru HU

Research output: Contribution to journalArticles

Abstract

More and more students are learning via online academic discussions, posting messages in an attempt to discuss their learning problems. However, many messages do not receive responses. Posting messages that elicit responses is essential to students' experiences of learning through online discussions, but the characteristics of such messages are seldom studied. To fill this gap, this paper examines the relationship between the characteristics of an online discussion message and its likelihood of receiving a response from others. We conducted the study with a public, online discussion forum about high school-level mathematics—a non-formal learning environment that is not confined to a specific classroom. We randomly sampled 140 topics from the forum and analysed 1,559 reply messages using multilevel logistic regressions at the topic and message level. We found that during an online discussion, a message that either expressed disagreement, included a correct or incorrect idea, or asked a question was more likely to receive a response. Time was another significant predictor; messages posted during the early stage of a discussion or users who responded more promptly were more likely to receive a response. The findings contribute to the understanding of the discourse process and students' learning behaviour in online academic discussions. We propose several recommendations for future research. Copyright © 2020 Elsevier Ltd. All rights reserved.
Original languageEnglish
Article number103938
JournalComputers & Education
Volume156
Early online dateMay 2020
DOIs
Publication statusE-pub ahead of print - May 2020

Citation

Chen, G., Lo, C. K., & Hu, L. (2020). Sustaining online academic discussions: Identifying the characteristics of messages that receive responses. Computers & Education, 156. Retrieved from https://doi.org/10.1016/j.compedu.2020.103938

Keywords

  • Online discussion
  • Discussion forum
  • Responsiveness
  • Non-formal learning
  • Statistical discourse analysis

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