Sustaining online academic discussions: Identifying the characteristics of messages that receive responses
Introduction
Thanks to advances in technology, students can interact with other students in online communities to seek information or discuss questions (Aloni & Harrington, 2018; De Wever, Schellens, Valcke, & Van Keer, 2006; Woo & Reeves, 2007). There are multiple advantages to online discussions, such as open communication, supportive collaboration, information exchange, and the connection of ideas (Garrison, 2007). It has long been recognised that although learners working together may generate cognitive conflicts (Piaget, 1974), this can enable them to solve problems at a more advanced level than if they worked on the same problems alone (Doise, Mugny, & Perret-Clermont, 1975). For this reason, online discussion forums have been used to support student learning in various educational contexts (e.g., Aloni & Harrington, 2018; Wise & Cui, 2018).
Our research focus is on the characteristics of messages that elicit responses in online academic discussions because they are central to sustaining online discussions and developing online communities (Anderson, 2006; Ridings, Gefen, & Arinze, 2002; Tsai & Pai, 2013). Messages that elicit responses increase the lifespan of a discussion, the participants' sense of belonging, and their engagement in online communities (Lee, Reid, & Kim, 2014; Lewallen, Owen, Bantum, & Stanton, 2014; Tsai & Pai, 2013). They can also reinforce the contribution of knowledge to online communities (Jin, Li, Zhong, & Zhai, 2015; Kim & Sundar, 2014; Tausczik & Pennebaker, 2012). In addition, participants can gain a deeper understanding of the learning materials by reviewing and commenting on online messages (Aloni & Harrington, 2018; Cathey, 2007; Tsai & Pai, 2013). It is therefore important to identify those characteristics that affect a message's likelihood of receiving a response in online academic discussion communities.
Past studies have identified various motivational factors that affect online participation and knowledge-sharing behaviours across a range of online communities (e.g., Jin et al., 2015; Joyce & Kraut, 2006; Kim & Sundar, 2014; Wang & Lai, 2006). For example, a survey study by Lee et al. (2014) provided evidence that higher levels of online authors’ sense of belonging would lead to higher levels of knowledge-sharing activities. However, we currently know little about the characteristics of a message per se that drive post-replying behaviour in online academic discussion communities.
The present study seeks to understand how students engage other students to respond during online discussions, and how to enhance discussions in online forums. We create a detailed model of actions and sequences across time that affect others' engagement in online discussions, thereby informing current models of motivation and informing online teaching to enhance students' online discussions. Building on past studies that primarily examine post hoc surveys of motivation, this study examines how sequences of students’ behaviours affect their likelihood of responding in an online discussion.
We study discussions in an online community forum as a form of non-formal learning. Specifically, we analyse high school students' mathematics discussions from an online public forum that is not attached to any class or school. Examining students’ interactions in independent forums not confined to the classroom can improve our understanding of their spontaneous, natural behaviours when responding to one another during online discussions.
Section snippets
Research model and hypotheses
For this study, we adopted the theoretical work of cognitive-social theorists, such as Festinger (1957) and Heider (1946), which affirms that cognitive dissonance and imbalance generate a motivational tendency to resolve contradictory cognitions. We also used the theoretical model of helping behaviour as proposed by social psychologists (e.g., Latané & Darley, 1970; Yalom, 2005) to approach the characteristics of messages that elicit responses during online discussions. We developed a research
Online discussion forum and data
In this study, we collected and analysed data from an online discussion forum for high school mathematics, which is hosted by Art of Problem Solving (AoPS) Online in the US. As one of the largest mathematics communities on the Internet, AoPS Online aims to help students expand and deepen their mathematical thinking (artofproblemsolving.com). The high school mathematics forum is a non-formal learning environment (Schwier & Seaton, 2013) which is moderated but not facilitated by the AoPS team.
Results
Overall, the analysis results indicate that only the characteristics of a current message significantly predicted its likelihood of receiving a response. In other words, the impact of its previous messages along the same thread (e.g., lag 1 and lag 2) on post-replying behaviour was not significant. Also, a variance components model showed that the outcome variable responsiveness did not differ significantly across topics, so single-level modelling (message level) was adequate. The corresponding
Discussion
In this study, we adopted statistical discourse analysis to examine how a message's content-related characteristics might affect the likelihood of receiving a response during online academic discussions. We conducted the study in a non-formal learning environment—an open online high-school-mathematics forum that is not confined to a class or a school. The analysis results support the hypotheses about disagreement (H-1a), correct ideas (H-2a), incorrect ideas (H-2b), and questions (H-3a). Apart
Conclusion and implications
This study aimed to identify the content-related characteristics that affect a message's likelihood of receiving a response in online academic discussions on mathematics topics. The results suggest that how a message looks backward (i.e., disagrees with a previous message), posts knowledge content (i.e., adds a new idea, either correct or incorrect), and looks forward (i.e., asks a question) can affect the likelihood of receiving a response. Slow response messages or those posted in the later
CRediT authorship contribution statement
Gaowei Chen: Conceptualization, Methodology, Formal analysis, Writing - original draft. Chung Kwan Lo: Investigation, Validation, Writing - review & editing. Liru Hu: Data curation, Writing - review & editing.
Acknowledgements
This research was supported by Hong Kong Research Grants Council (RGC) grant No. 17608318 and the Public Policy Research Funding Scheme (grant No. 2017.A8.073.18C) from the Policy Innovation and Co-ordination Office of the Government of the HKSAR.
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