Dynamic detection of novice vs. skilled use without a task model
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Incorporating tutoring principles into interactive knowledge acquisition
International Journal of Human-Computer Studies
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Workers from all sectors use software applications to complete day-to-day tasks. The mastery of new software applications can be frustrating to users who are otherwise job-experts and can temporarily decrease productivity. Job and task experts are not well served by tutoring approaches that combine instruction about the task with instruction about the tool. This work presents an architecture and prototype implementation that selects timely, task-appropriate hints for expert users as they work with an application to complete real tasks. The architecture maintains models of user and task, as well as a specialized model of tutoring-for-experts that was created by observing human tutors. This research shows that domain experts can be successfully scaffolded with adaptive hints while doing their work and that they endure less cognitive load than users for whom the scaffolding is not adapted to the task.