Participatory Design: Principles and Practices
Participatory Design: Principles and Practices
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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Expert users of programs that handle complicated data management problems develop methods for coping with data overload, multi-user cooperation, and real-time situations. These expert methods incorporate domain and/or user interface knowledge. If such methods were inherent in a system, then novice users could benefit from the expert's experience, the learning curve would be shortened and a more effective system would result. Defining and implementing a complete set of expert methods at design time is a daunting task. Collecting such information from a system's usage, after it has been deployed, should provide a more accurate database of expert methodologies, Current adaptive systems attempt to capture and automate such features during run-time. However, these systems can never evolve very far beyond their original design, since the adaptations occur within the scope of that design. Our method is to offer the expert's usage database as input to the designer, reintroducing the designer in the development cycle after a system has been deployed initially, so that a more effective system can be produced in the next generation.