A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors
International Journal of Artificial Intelligence in Education
Engineering Applications of Artificial Intelligence
An authoring language as a key to usability in a problem-solving ITS framework
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Adaptive tutorials for virtual microscopy: a design paradigm to promote pedagogical ownership
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Expecting the unexpected: warehousing and analyzing data from ITS field use
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Knowledge component suggestion for untagged content in an intelligent tutoring system
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Content learning analysis using the moment-by-moment learning detector
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Content creation is a large component of the cost of creating educational software. Estimates are that approximately 200 hours of development time are required for every hour of instruction. We present an authoring tool designed to reduce this cost as it helps to refine and maintain content. The ASSISTment Builder is a tool designed to effectively create, edit, test, and deploy tutor content. The Web-based interface simplifies the process of tutor construction to allow users with little or no programming experience to develop content. We show the effectiveness of our Builder at reducing the cost of content creation to 40 hours for every hour of instruction. We describe new features that work toward supporting the life cycle of ITS content creation through maintaining and improving content as it is being used by students. The Variabilization feature allows the user to reuse tutoring content across similar problems. The Student Comments feature provides a way to maintain and improve content based on feedback from users. The Most Common Wrong Answer feature provides a way to refine remediation based on the users' answers. This paper describes our attempt to support the life cycle of content creation.