Supporting Answerers with Feedback in Social Q&A

TL;DR

This paper investigates the use of explicit, criteria-based feedback to improve answer quality on social Q&A platforms, finding that answerers rejected such feedback despite its accuracy and alignment with goals.

Contribution

It introduces and tests a criteria-based feedback system for answerers in social Q&A, revealing challenges in acceptance and integration with existing norms.

Findings

Answerers rejected criteria-based feedback despite its accuracy.

Crowdsourced ratings were objectively accurate.

Norms and expectations conflicted with feedback design.

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.