%0 Conference Paper %1 Villano1992 %A Villano, Michael %B ITS '92: Proceedings of the Second International Conference on Intelligent Tutoring Systems %C London, UK %D 1992 %I Springer-Verlag %K StudentModel BelieveNetworks CbKST %P 491--498 %T Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory %X The applicability of Knowledge Space Theory and Bayesian Belief Networks as probabilistic student models embedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge represenlation, adaptive assessment, curriculum Advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, teaming and forgetting, and unanticipated student response patterns, However, a probabilistic student model can represent uncertainty regarding me estimate of the student's knowledge empirical student data and established statistical techniques, %@ 3-540-55606-0

@inproceedings{Villano1992, abstract = {The applicability of Knowledge Space Theory and Bayesian Belief Networks as probabilistic student models embedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge represenlation, adaptive assessment, curriculum Advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, teaming and forgetting, and unanticipated student response patterns, However, a probabilistic student model can represent uncertainty regarding me estimate of the student's knowledge empirical student data and established statistical techniques,}, added-at = {2009-11-19T19:28:47.000+0100}, address = {London, UK}, author = {Villano, Michael}, biburl = {https://www.bibsonomy.org/bibtex/259a0f30ea09ad00ec8de49dc26f2b959/georg.oettl}, booktitle = {ITS '92: Proceedings of the Second International Conference on Intelligent Tutoring Systems}, file = {:Psych\\Villano1992.pdf:PDF}, interhash = {60cf53f4b49c529b86eebf059ad5a1e4}, intrahash = {59a0f30ea09ad00ec8de49dc26f2b959}, isbn = {3-540-55606-0}, keywords = {StudentModel BelieveNetworks CbKST}, pages = {491--498}, publisher = {Springer-Verlag}, review = {@georg Combination of Knowledge Space Theory and Bayesian Believe Networks. Attempt to combine student models and cbkst (AI and Assessment). The basic unit of knowledge is an item. Each item can be in the form of a question or a class of questions a student has to answer. Task -> equals procedural knowledge. KST can do all BBN features for student assessment; other direction is false. Open research questions regarding similarities between BBN and KST; References to "statements" that this can be easily proved. No prove available yet.}, timestamp = {2009-11-19T19:28:47.000+0100}, title = {Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory}, year = 1992 }