%0 Journal Article %1 2007 %A Beck, Joseph E. %D 2007 %K Dirichlet_priors estimation modelling parameter student AND bayesian network %T Difficulties in inferring student knowledge from observations (and why you should care %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.6167 %X Student modeling has a long history in the field of intelligent educational software and is the basis for many tutorial decisions. Furthermore, the task of assessing a student’s level of knowledge is a basic building block in the educational data mining process. If we cannot estimate what students know, it is difficult to perform fine-grained analyses to see if a system’s teaching actions are having a positive effect. In this paper, we demonstrate that there are several unaddressed problems with student model construction that negatively affect the inferences we can make. We present two partial solutions to these problems, using Expectation Maximization to estimate parameters and using Dirichlet priors to bias the model fit procedure. Aside from reliably improving model fit in predictive accuracy, these approaches might result in model parameters that are more plausible. Although parameter plausibility is difficult to quantify, we discuss some guidelines and propose a derived measure of predicted number of trials until mastery as a method for evaluating model parameters.

@article{2007, abstract = {Student modeling has a long history in the field of intelligent educational software and is the basis for many tutorial decisions. Furthermore, the task of assessing a student’s level of knowledge is a basic building block in the educational data mining process. If we cannot estimate what students know, it is difficult to perform fine-grained analyses to see if a system’s teaching actions are having a positive effect. In this paper, we demonstrate that there are several unaddressed problems with student model construction that negatively affect the inferences we can make. We present two partial solutions to these problems, using Expectation Maximization to estimate parameters and using Dirichlet priors to bias the model fit procedure. Aside from reliably improving model fit in predictive accuracy, these approaches might result in model parameters that are more plausible. Although parameter plausibility is difficult to quantify, we discuss some guidelines and propose a derived measure of predicted number of trials until mastery as a method for evaluating model parameters.}, added-at = {2011-06-16T12:04:25.000+0200}, author = {Beck, Joseph E.}, biburl = {https://www.bibsonomy.org/bibtex/2a6b274bb349e084e0fd0aeda0b3e2e33/jennymac}, description = {Scientific Commons: Difficulties in inferring student knowledge from observations (and why you should care (2007), 2007 [Joseph E. Beck]}, institution = {CiteSeerX - Scientific Literature Digital Library and Search Engine [http://citeseerx.ist.psu.edu/oai2] (United States)}, interhash = {c7953947a956d777d3de409eab42558a}, intrahash = {a6b274bb349e084e0fd0aeda0b3e2e33}, keywords = {Dirichlet_priors estimation modelling parameter student AND bayesian network}, location = {http://www.scientificcommons.org/47855636}, timestamp = {2015-01-21T22:01:25.000+0100}, title = {Difficulties in inferring student knowledge from observations (and why you should care}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.6167}, year = 2007 }