Graphic interface design and deductive proof construction
Journal of Computers in Mathematics and Science Teaching
Ivy: a preprocessor and proof checker for first-order logic
Computer-Aided reasoning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Effective feedback content for tutoring complex skills
Human-Computer Interaction
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Towards an Intelligent Tutoring System for Propositional Proof Construction
Proceedings of the 2008 conference on Current Issues in Computing and Philosophy
I learn from you, you learn from me: How to make iList learn from students
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
A comparative analysis of cognitive tutoring and constraint-based modeling
UM'03 Proceedings of the 9th international conference on User modeling
Generating Bayesian networks from probability logic knowledge bases
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Hints: is it better to give or wait to be asked?
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Program representation for automatic hint generation for a data-driven novice programming tutor
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Experimental Evaluation of Automatic Hint Generation for a Logic Tutor
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to "seed" the hint generation process. We show that just a few expert solutions give significant coverage (over 50%) for hints. This seeding method greatly speeds up the time needed to reliably generate hints. We discuss how this feature can be integrated into the Hint Factory and some potential pedagogical issues that the expert solutions introduce.