Knowledge-based tutoring: the GUIDON program
Knowledge-based tutoring: the GUIDON program
Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
Explanation Patterns: Understanding Mechanical and Creatively
Explanation Patterns: Understanding Mechanical and Creatively
A tutoring and student modelling paradigm for gaming environments
SIGCSE '76 Proceedings of the ACM SIGCSE-SIGCUE technical symposium on Computer science and education
The goal structure of a socratic tutor
ACM '77 Proceedings of the 1977 annual conference
A process model of cased-based reasoning in problem solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
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Case-based teaching systems, like good human teachers, tell stories in order to help students learn. A case-based teaching system engages a student in a challenging task and monitors his actions looking for opportunities to tell stories that will assist the learning process. In order to produce stories at the appropriate moment, a casebased teaching system must have a library of stories that are indexed according to how they should be used and a set of reminding strategies to retrieve stories when they are relevant. In this paper, I discuss CreANIMate, a biology tutor that uses stories to help teach elementary school students about animal morphology. In particular, I discuss the reminding strategies and indexing schemes that enable the system to achieve its educational objectives. These reminding strategies are example remindings, similarity-based remindings, and expectation violation remindings.