Multi Level Knowledge in Modeling Qualitative PhysicsLearning
Machine Learning - Special issue on multistrategy learning
Import and export to/from computing science education: the case of mathematics education research
Proceedings of the 7th annual conference on Innovation and technology in computer science education
Afterword: from this revolution to the next
Smart machines in education
The Foundations and Architecture of Autotutor
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
ICLS '04 Proceedings of the 6th international conference on Learning sciences
Specifying Strategies for Exercises
Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
International Journal of Artificial Intelligence in Education
Learning membership functions in a function-based object recognition system
Journal of Artificial Intelligence Research
No Code Required: Giving Users Tools to Transform the Web
No Code Required: Giving Users Tools to Transform the Web
User Modeling and User-Adapted Interaction
Source selection for analogical reasoning an empirical approach
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Detecting learning moment-by-moment
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Problem order implications for learning transfer
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Automating next-step hints generation using ASTUS
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Efficient cross-domain learning of complex skills
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Automating the modeling of learners' erroneous behaviors in model-tracing tutors
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
A trace-based framework for analyzing and synthesizing educational progressions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
| Hi-index | 0.00 |
From the Publisher:As children acquire arithmetic skills, they often develop "bugs" - small, local misconceptions that cause systematic errors. Mind Bugs combines a novel cognitive simulation process with careful hypothesis testing to explore how mathematics students acquire procedural skills in instructional settings, focusing in particular on these procedural misconceptions and what they reveal about the learning process. VanLehn develops a theory of learning that explains how students develop procedural misconceptions that cause systematic errors. He describes a computer program, "Sierra," that simulates learning processes and predicts exactly what types of procedural errors should occur. These predictions are tested with error data from several thousand subjects from schools all over the world. Moreover, each hypothesis of the theory is tested individually by determining how the predictions would change if it were removed from the theory. Integrating ideas from research in machine learning, artificial intelligence, cognitive psychology, and linguistics, Mind Bugs specifically addresses error patterns on subtraction tests, showing, for example, why some students have an imperfect understanding of the rules for borrowing. Alternative explanatory hypotheses are explored by incorporating them in Sierra in place of the primary hypotheses, and seeing if the program still explains all the subtraction bugs that it explained before. Kurt VanLehn is Assistant Professor in the Department of Psychology at Carnegie Mellon University. Mind Bugs is included in the series Learning, Development, and Conceptual Change, edited by Lila Gleitman, Susan Carey, ElissaNewport, and Elizabeth Spelke. A Bradford Book