  One goal of online social recommendation systems is to harness the wisdom of
crowds in order to identify high quality content. Yet the sequential voting
mechanisms that are commonly used by these systems are at odds with existing
theoretical and empirical literature on optimal aggregation. This literature
suggests that sequential voting will promote herding---the tendency for
individuals to copy the decisions of others around them---and hence lead to
suboptimal content recommendation. Is there a problem with our practice, or a
problem with our theory? Previous attempts at answering this question have been
limited by a lack of objective measurements of content quality. Quality is
typically defined endogenously as the popularity of content in absence of
social influence. The flaw of this metric is its presupposition that the
preferences of the crowd are aligned with underlying quality. Domains in which
content quality can be defined exogenously and measured objectively are thus
needed in order to better assess the design choices of social recommendation
systems. In this work, we look to the domain of education, where content
quality can be measured via how well students are able to learn from the
material presented to them. Through a behavioral experiment involving a
simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we
show that sequential voting systems can surface better content than systems
that elicit independent votes.
