Case-based reasoning
Introspective multistrategy learning: on the construction of learning strategies
Artificial Intelligence
Feature Weight Maintenance in Case Bases Using Introspective Learning
Journal of Intelligent Information Systems
Shared reality: physical collaboration with a virtual peer
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Learning to Improve Case Adaption by Introspective Reasoning and CBR
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
A regression based adaptation strategy for case-based reasoning
Eighteenth national conference on Artificial intelligence
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
An Intelligent Agent that Learns How to Tutor Students: Design and Results
Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation
Classifying learner engagement through integration of multiple data sources
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Probabilistic goal recognition in interactive narrative environments
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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Many intelligent tutoring systems (ITSs) have been developed, deployed, assessed, and proven to facilitate learning. However, most of these systems do not generally adapt to new circumstances, do not self-evaluate and self-configure their own strategies, and do not monitor the usage history of the learning content being delivered or presented to the students. These shortcomings force ITS developers to often spend much development time in manual revision and fine-tuning of the learning and instructional contents of an ITS. In this paper, we describe an intelligent agent that delivers learning material adaptively to different students, factoring in the usage history of the learning materials and student profiles as observed by the agent. Student-tutor interaction includes the activities of going through learning material, such as a topical tutorial, a set of examples, and a set of problems. Our assumption is that our agent will be able to capture and utilize these student activities as the primer to select the appropriate examples or problems to administer to the student. Using an integrated introspective case-based reasoning approach, our agent further learns from its experience and refines its reasoning process-including the instructional strategies-to adapt to student needs. Moreover, our agent monitors the usage history of the learning materials to improve its performance. We have built an end-to-end ITS using an agent powered by this integrated introspective case-based reasoning engine. We have deployed the ITS in a CS course. Results indicate that the ITS was able to learn to deliver more appropriate examples and problems to the students.