%0 Journal Article %1 wiley2008 %A García, P. %A Schiaffino, S. %A Amandi, A. %D 2008 %J Journal of Computer Assisted Learning %K AI artificial assesment bayesian eassessment elearning intelligence its learning modelling styles web wleformativeeassessment %N 4 %P 305-315 %R 10.1111/j.1365-2729.2007.00262.x %T An enhanced Bayesian model to detect students' learning styles in Web-based courses %U http://dx.doi.org/10.1111/j.1365-2729.2007.00262.x %V 24 %X Abstract Students acquire and process information in different ways depending on their learning styles. To be effective, Web-based courses should guarantee that all the students learn despite their different learning styles. To achieve this goal, we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In a previous work, we have presented an approach that uses Bayesian networks to detect a student's learning style in Web-based courses. In this work, we present an enhanced Bayesian model designed after the analysis of the results obtained when evaluating the approach in the context of an Artificial Intelligence course. We evaluated the precision of our Bayesian approach to infer students' learning styles from the observation of their actions with a Web-based education system during three semesters. We show how the results from one semester enabled us to adjust our initial model and helped teachers improve the content of the course for the following semester, enhancing in this way students' learning process. We obtained higher precision values when inferring the learning styles with the enhanced model.

@article{wiley2008, abstract = {Abstract Students acquire and process information in different ways depending on their learning styles. To be effective, Web-based courses should guarantee that all the students learn despite their different learning styles. To achieve this goal, we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In a previous work, we have presented an approach that uses Bayesian networks to detect a student's learning style in Web-based courses. In this work, we present an enhanced Bayesian model designed after the analysis of the results obtained when evaluating the approach in the context of an Artificial Intelligence course. We evaluated the precision of our Bayesian approach to infer students' learning styles from the observation of their actions with a Web-based education system during three semesters. We show how the results from one semester enabled us to adjust our initial model and helped teachers improve the content of the course for the following semester, enhancing in this way students' learning process. We obtained higher precision values when inferring the learning styles with the enhanced model.}, added-at = {2008-09-06T03:37:49.000+0200}, author = {García, P. and Schiaffino, S. and Amandi, A.}, biburl = {https://www.bibsonomy.org/bibtex/2b67d1053cfc84a6ef72e2613c093cb78/yish}, doi = {10.1111/j.1365-2729.2007.00262.x}, interhash = {2463a5d63b98d2f4adcbf87d048ac9d4}, intrahash = {b67d1053cfc84a6ef72e2613c093cb78}, journal = {Journal of Computer Assisted Learning}, keywords = {AI artificial assesment bayesian eassessment elearning intelligence its learning modelling styles web wleformativeeassessment}, number = 4, pages = {305-315}, timestamp = {2008-09-06T03:37:49.000+0200}, title = {An enhanced Bayesian model to detect students' learning styles in Web-based courses}, url = {http://dx.doi.org/10.1111/j.1365-2729.2007.00262.x}, volume = 24, year = 2008 }