Supporting answerers with feedback in social Q&A

Abstract

Prior research has examined the use of Social Question and Answer (Q&A) websites for answer and help seeking. However, the potential for these websites to support domain learning has not yet been realized. Helping users write effective answers can be beneficial for subject area learning for both answerers and the recipients of answers. In this study, we examine the utility of crowdsourced, criteria-based feedback for answerers on a student-centered Q&A website, Brainly.com. In an experiment with 55 users, we compared perceptions of the current rating system against two feedback designs with explicit criteria (Appropriate, Understandable, and Generalizable). Contrary to our hypotheses, answerers disagreed with and rejected the criteria-based feedback. Although the criteria aligned with answerers' goals, and crowdsourced ratings were found to be objectively accurate, the norms and expectations for answers on Brainly conflicted with our design. We conclude with implications for the design of feedback in social Q&A. Figure 1: An answer on Brainly.com. Feedback is given to answerers by clicking the 'thanks' button, rating the answer from 1 to 5 stars, or adding a comment.

How do we elicit more user feedback in the social Q&A community? A consideration of the expertise-required question

“…In the SQA community, users can vote for, agree with and like, and give feedback on or approve of answers. They can also make comments to express their doubts, agreement or opposition or provide additional materials to supplement the content, a process through which users can collaboratively learn from the feedback (Costa et al, 2017;Frens et al, 2018;Oeldorf-Hirsch and Sundar, 2015). Two forms of feedbackapproval and collaborationtogether with the action of publishing answers, shape the production of SQA community content.…”

“…They can also make comments to express their doubts, agreement or opposition or provide additional materials to supplement the content, a process through which users can collaboratively learn from the feedback (Costa et al. , 2017; Frens et al. , 2018; Oeldorf-Hirsch and Sundar, 2015).…”