Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Integrating Problem-Solving Methods into CYC
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Cognitive Computer Tutors: Solving the Two-Sigma Problem
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
An Individualized Web-Based Algebra Tutor Based on Dynamic Deep Model Tracing
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Blending Assessment and Instructional Assisting
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
What Evidence Matters? A randomized field trial of Cognitive Tutor Algebra I
Proceedings of the 2007 conference on Supporting Learning Flow through Integrative Technologies
IEEE Transactions on Learning Technologies
A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors
International Journal of Artificial Intelligence in Education
Ontology-based authoring of intelligent model-tracing math tutors
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
The MATHESIS semantic authoring framework: ontology-driven knowledge engineering for ITS authoring
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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This article describes an intelligent, integrated, web-based school for tutoring expansion and factoring of algebraic expressions. It provides full support for the management of the usual teaching tasks in a traditional school: Student and teacher registration, creation and management of classes and test papers, individualized assignment of exercises, intelligent step by step guidance in solving exercises, student interaction recording, skill mastery statistics and student assessment. The intelligence of the system lies in its Algebra Tutor, a model-tracing tutor developed within the MATHESIS project, that teaches a breadth of 16 top-level math skills algebraic operations: monomial multiplication, division and power, monomial-polynomial and polynomial-polynomial multiplication, parentheses elimination, collect like terms, identities square of sum and difference, product of sum by difference, cube of sum and difference, factoring common factor, term grouping, identities, quadratic form. These skills are further decomposed in simpler ones giving a deep domain expertise model of 104 primitive skills. The tutor has two novel features: a it exhibits intelligent task recognition by identifying all skills present in any expression through intelligent parsing, and b for each identified skill, the tutor traces all the sub-skills, a feature we call deep model tracing. Furthermore, based on these features, the tutor achieves broad knowledge monitoring by recording student performance for all skills present in any expression. Forty teachers who evaluated the system in a 3-hours workshop appreciated the fine-grained step-by-step guidance of the student, the equally fine grained student model created by the tutor and its ability to tutor any exercise that contains the aforementioned math skills. The system was also used in a real junior high school classroom with 20 students for three months. Evaluation of the students' performance in the domain of factoring gave positive learning results.