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While most collaboration technologies are concerned with supporting particular tasks such as workflows or meetings, many work groups do not have the teamwork skills essential to effective collaboration. One way to improve teamwork is to provide dynamic feedback generated by automated analyses of behavior, such as language use. Such feedback can lead members to reflect on and subsequently improve their collaborative behavior, but might also distract from the task at hand. We have experimented with GroupMeter -a chatbased system that presents visual feedback on team members' language use. Feedback on proportion of agreement words and overall word count was presented using two different designs. When receiving feedback, teams in our study expressed more agreement in their conversations and reported greater focus on language use as compared to when not receiving feedback. This suggests that automated, realtime linguistic feedback can elicit behavioral changes, offering opportunities for future research.

Effective communication in project teams is important, but not often taught. We explore how feedback might improve teamwork in a controlled experiment where groups interact through chat rooms. Collaborators who receive high feedback ratings use different language than poor collaborators (e.g. more words, fewer assents, and less affect-laden language). Further, feedback affects language use. This suggests that a system could use linguistic analysis to automatically provide and visualize feedback to teach teamwork. To this end, we present GroupMeter, a system that applies principles discovered in the experiment to provide feedback both from peers and from automated linguistic analysis.

Modern enterprises are replete with numerous online processes. Many must be performed frequently and are tedious, while others are done less frequently yet are complex or hard to remember. We present interviews with knowledge workers that reveal a need for mechanisms to automate the execution of and to share knowledge about these processes. In response, we have developed the CoScripter system (formerly Koala [ 11]), a collaborative scripting environment for recording, automating, and sharing web-based processes. We have deployed CoScripter within a large corporation for more than 10 months. Through usage log analysis and interviews with users, we show that CoScripter has addressed many user automation and sharing needs, to the extent that more than 50 employees have voluntarily incorporated it into their work practice. We also present ways people have used CoScripter and general issues for tools that support automation and sharing of how-to knowledge. . First, we present results from an interview study that explores how people practice, learn, and share their procedural knowledge in the enterprise. Second, we present results from an extended deployment of an end-user programming system in a large organization. Finally, we discuss a number of general issues that arose in the deployment that must

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