Convex Optimization
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Identifiability: A Fundamental Problem of Student Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Bayesian networks for student model engineering
Computers & Education
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Ensembling predictions of student knowledge within intelligent tutoring systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
KT-IDEM: introducing item difficulty to the knowledge tracing model
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Carelessness and goal orientation in a science microworld
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A Motivation Guided Holistic Rehabilitation of the First Programming Course
ACM Transactions on Computing Education (TOCE)
The relationship between carelessness and affect in a cognitive tutor
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Contextual slip and prediction of student performance after use of an intelligent tutor
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Modeling individualization in a bayesian networks implementation of knowledge tracing
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Detecting the moment of learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
The sum is greater than the parts: ensembling models of student knowledge in educational software
ACM SIGKDD Explorations Newsletter
Collaboration in cognitive tutor use in latin America: field study and design recommendations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detecting learning moment-by-moment
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Learning what works in its from non-traditional randomized controlled trial data
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Enhancing the automatic generation of hints with expert seeding
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Content learning analysis using the moment-by-moment learning detector
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Towards automatically detecting whether student learning is shallow
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Modeling multiple distributions of student performances to improve predictive accuracy
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
User Modeling and User-Adapted Interaction
Hi-index | 0.00 |
Modeling students' knowledge is a fundamental part of intelligent tutoring systems. One of the most popular methods for estimating students' knowledge is Corbett and Anderson's [6] Bayesian Knowledge Tracing model. The model uses four parameters per skill, fit using student performance data, to relate performance to learning. Beck [1] showed that existing methods for determining these parameters are prone to the Identifiability Problem:the same performance data can be fit equally well by different parameters, with different implications on system behavior. Beck offered a solution based on Dirichlet Priors [1], but, we show this solution is vulnerable to a different problem, Model Degeneracy, where parameter values violate the model's conceptual meaning (such as a student being more likely to get a correct answer if he/she does not know a skill than if he/she does).We offer a new method for instantiating Bayesian Knowledge Tracing, using machine learning to make contextual estimations of the probability that a student has guessed or slipped. This method is no more prone to problems with Identifiability than Beck's solution, has less Model Degeneracy than competing approaches, and fits student performance data better than prior methods. Thus, it allows for more accurate and reliable student modeling in ITSs that use knowledge tracing.