%0 Journal Article %1 Kai-minChangandJosephE.BeckandJackMostow2006 %A min Chang, Kai %A Beck, Joseph E. %A Mostow, Jack %A Corbett, Albert %D 2006 %I AAAI %K Bayes_net intelligent scaffolding teaching tutoring %T Does Help Help? A Bayes Net Approach to Modeling Tutor Interventions %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.116.4868 %X This paper describes an effort to measure the effectiveness of tutor help in an intelligent tutoring system. Conventional pre- and post- test experimental methods can determine whether help is effective but are expensive to conduct. Furthermore, a pre and post- test methodology ignores a source of information: students request help about words they do not know. Therefore, we propose a dynamic Bayes net (which we call the help model) that models tutor help and student knowledge in one coherent framework. The help model distinguishes two different effects of help: scaffolding immediate performance vs. teaching persistent knowledge that improves long term performance. We train the help model to fit the student performance data gathered from usage of Reading Tutor. The parameters of the trained model suggest that students benefit from both the scaffolding and teaching effects of help. Thus, our framework is able to distinguish two types of influence that help has on the student, and can determine whether help helps learning without an explicit controlled study.

@article{Kai-minChangandJosephE.BeckandJackMostow2006, abstract = {This paper describes an effort to measure the effectiveness of tutor help in an intelligent tutoring system. Conventional pre- and post- test experimental methods can determine whether help is effective but are expensive to conduct. Furthermore, a pre and post- test methodology ignores a source of information: students request help about words they do not know. Therefore, we propose a dynamic Bayes net (which we call the help model) that models tutor help and student knowledge in one coherent framework. The help model distinguishes two different effects of help: scaffolding immediate performance vs. teaching persistent knowledge that improves long term performance. We train the help model to fit the student performance data gathered from usage of Reading Tutor. The parameters of the trained model suggest that students benefit from both the scaffolding and teaching effects of help. Thus, our framework is able to distinguish two types of influence that help has on the student, and can determine whether help helps learning without an explicit controlled study.}, added-at = {2011-06-16T12:14:47.000+0200}, author = {min Chang, Kai and Beck, Joseph E. and Mostow, Jack and Corbett, Albert}, biburl = {https://www.bibsonomy.org/bibtex/22e9574876d41cfae59179a614408065a/jennymac}, description = {Scientific Commons: Does Help Help? A Bayes Net Approach to Modeling Tutor Interventions (2006), 2006 [Kai-min Chang, Joseph E. Beck, Jack Mostow, Albert Corbett]}, institution = {CiteSeerX - Scientific Literature Digital Library and Search Engine [http://citeseerx.ist.psu.edu/oai2] (United States)}, interhash = {195a74258b42702db71a5af0bdc80634}, intrahash = {2e9574876d41cfae59179a614408065a}, keywords = {Bayes_net intelligent scaffolding teaching tutoring}, location = {http://www.scientificcommons.org/42395156}, publisher = {AAAI}, timestamp = {2011-06-16T12:14:47.000+0200}, title = {Does Help Help? A Bayes Net Approach to Modeling Tutor Interventions}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.116.4868}, year = 2006 }