【公开学术报告】The Role of Machines and Peers in Driving Learning from Failure: Evidence from an Online Programming Contest Community

Date & Time: 13:30-15:00 pm, Friday 1^st, November 2019

Venue: Room 2101, Tongji Building A

Language: English

Speaker：Dr. Tengjian ZOU 邹腾剑 (Singapore Management University)

ABSTRACT

While past studies on learning from failure focus primarily on failures identified by the focal individuals themselves, less attention has been paid to failures identified by other sources (i.e., a machine or peers). Past findings on whether individuals learn from their failures identified by themselves are mixed, hence it is not straightforward to infer whether people learn from failures identified by other sources. Drawing on attribution theory, we investigate this question using data collected from an online programming contest community, in which for a large number of individuals we can trace code failures that were identified by a machine or by peers. We find that whereas individuals always learn from failures identified by a machine, their learning from failures identified by peers depends on peers’ status. Specifically, we find that individuals learn only from failures identified by higher-status peers, but not from those identified by lower-status peers. Apart from these main findings, we also conducted additional analyses that yield important implications regarding whether individuals learn more from machines or humans and whether individuals learn more from their success or failure. Together, we expand experiential learning theories by considering individuals’ failures identified by other sources and by incorporating status theory.