Communityquestion-answering (CQA) enables both information retrieval and social interactions. CQA questions are viewed as goal-expressions from the askers' perspectives. Most prior studies mainly focused on the goals expressed in the questions, but not on how responders' expectations and responses are influenced by the goal-expressions. To fill the gap, this research proposes the use of framing theory to understand how different expressions of goals influence responses. Cues of questions were used to identify goal-frames in CQA questions. Social network analysis was used to construct response networks whose nodes represent postings and connections represent responses. Our results reveal that goal-frames with high complexity, high specificity, and rewards tend to increase the centrality of questions. In contrast, low complexity and low specificity tend to generate extensive conversations. Implications for both researchers and practitioners are discussed in the final section.
The author, a novice problem-based learning (PBL) advocate in China and a young faculty member of the School of Stomatology at Wuhan University, joined a PBL tutorial group as an observer during his two-month visit at McMaster University. He describes his observations and thoughts as they relate to the current reform of health sciences education in China.
Community question-answering (CQA) enables responders to select questions, and respond to the questions by answering, commenting or voting. Accordingly, questions with different cues (i.e. complexity, specificity, emotional expressiveness, politeness, popularity, rewards) tend to attract different responders. However, the research is limited regarding the types of responders based on the questions they responded to. The gap inhibits us to form a complete understanding of how questions bridge askers with responders. Moreover, how different types of responders contribute to maintaining the ecosystem of the CQA has not been studied adequately. Accordingly, we conducted an online survey to organize responders by the cues of questions. Cluster analysis was used to group responders into three types: (1) "leaders" respond to complex and popular questions, attracting many followers in CQA; (2) "socializers" answer less complex and specific questions with emotion-laden words; and (3) "specialists" respond to complex questions with high specificity but seldom use the social functions of CQAs. Finally, contributions and limitations are discussed.
Community Question‐Answering (CQA) sites are virtual communities where users participate in collective online information sharing. Questions in CQA sites serve as starting points for information sharing, eliciting a response network (RN), where nodes are the postings, while edges represent the responses between postings. This research employed framing theory to investigate how question frames affected their resulting RNs. Question frames were operationalized as conversational and informational. Social network analysis was conducted to explore the RNs of different question frames for both Science, Technology, Engineering and Mathematics (STEM) and non‐STEM communities. Results revealed that most RNs of STEM conversational questions were larger in size and had higher centrality, whereas RNs of informational questions comprised closer relationships between postings. However, no significant differences were found in the non‐STEM community. Our research suggests that community moderators and askers should appropriately utilize question frames to organize information sharing inside CQA communities.
The following publications are associated with this Ph.D. research project based on data collected from the study or literature reviewed relating to the research topic. I am the principal author of all these publications. Wu, Q., & Kember, D. (2018). How do students make decisions about overseas higher education? A case study of Chinese international students at a regional Australian university. In D. Kember & M. Corbett (Eds.), Structuring the thesis: Matching method, paradigm, theories and findings (pp. 65-76). Singapore: Springer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.