Expediting Expertise - overview
Expediting Expertise aims to provide a personalized and social learning environment that allows knowledge workers and enterprises to capitalize on the informal learning that takes place naturally as part of everyday business.
Corporations have the need to both ramp-up expertise within their ranks as well as make employees more productive by giving them access to just-in-time expertise when needed in order to increase efficiency. Expediting Expertise aims to provide a personalized and social learning environment that allows knowledge workers and enterprises to capitalize on the informal learning that takes place naturally as part of everyday business. The tool facilitates expertise ramp-up in the enterprise through the use of automatic user and expertise modeling. It automatically and iteratively analyzes the content and activities associated with a given user in the enterprise based on available information from various sources (e.g. employee learning tools, online social business tools, paper and patent databases, organizational structure, and other information repositories) to create a model of topics in which this user is well versed. Further, the expertise model for a given topic (e.g. social software) is created by aggregating individual user models of the identified experts on this topic. A user's distance and path to the target level of expertise for a given topic are determined by comparing the model of this user for the topic and the expertise model for the topic. Both the user models and the expertise models are constantly updated to incorporate new content and user activities, allowing the system to dynamically evaluate and monitor the changing expertise levels of specific individual users as well as the aggregate of all contributors including the experts.
Examples of the capabilities provided by the tool include:
- Support personalized exploratory learning
- Help users understand their distance and best path to targeted expertise and their progress in getting to the target through intuitive and interactive visualizations
- Facilitate users in getting closer to their target with dynamic recommendations for learning materials and tasks (e.g. read paper, post blog in community, answer questions associated with a learning video)
- Support just-in-time expertise sharing in the enterprise by surfacing groups of people for real-time communication/questions on a given topic
- Track the informal learning that user undertakes (e.g. when reading a web page a user can easily mark it as something that the system should include when updating his/her user model)
- Provide a vetting system for evaluating emerging experts based on automated calculations
- Identify gaps in expertise on certain topics in the enterprise
- Identify emerging hot topics (i.e. topics that many users have selected to increase their expertise in)
- Identify the content that cultivates interaction between experts (via monitoring of content consumed and created by the knowledge workers)
Team membersJeff Boston, Jason Crawford, Jennifer Lai, Jie Lu, Shimei Pan, Mercan Topkara, Justin Weisz
For more information, please contact Jennifer Lai (firstname.lastname@example.org).