Expediting expertise: supporting informal social learning in the enterprise

Abstract

In this paper, we present Expediting Expertise, a system designed to provide structured support to the otherwise informal process of social learning in the enterprise. It employs a data-driven approach where online content is automatically analyzed and categorized into relevant topics, topic-specific user expertise is calculated by comparing the models of individual users against those of the experts, and personalized recommendation of learning activities is created accordingly to facilitate expertise development. The system's UI is designed to provide users with ongoing feedback of current expertise, progress, and comparison with others. Learning recommendation is visualized with an interactive treemap which presents estimated return on investment and distance to current expertise for each recommended learning activity. Evaluation of the system showed very positive results.