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.
@article{arxiv.1809.10266,
title = {Supporting Answerers with Feedback in Social Q&A},
author = {John Frens and Erin Walker and Gary Hsieh},
journal= {arXiv preprint arXiv:1809.10266},
year = {2018}
}
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Published in Proceedings of the Fifth Annual ACM Conference on Learning at Scale, Article No. 10, London, United Kingdom. June 26 - 28, 2018