%0 Journal Article %1 citeulike:13433164 %A Champaign, John %A Cohen, Robin %C Inderscience Publishers, Geneva, SWITZERLAND %D 2013 %I Inderscience Publishers %J Int. J. Learn. Technol. %K personalized-learning sequencing student-modeling %N 4 %P 337--361 %R 10.1504/ijlt.2013.059130 %T Ecological Content Sequencing: From Simulated Students to an Effective User Study %U http://dx.doi.org/10.1504/ijlt.2013.059130 %V 8 %X In this paper, we present an algorithm for reasoning about the sequencing of content for students in an intelligent tutoring system, influenced by McCalla's ecological approach. We record with each learning object those students who experienced the object, together with their initial and final states of knowledge, and then use these interactions to reason about the most effective lesson to show future students based on their similarity to previous students. We validate our approach through a novel method of validation, providing details of the model of learning used in the simulation and the results obtained in order to demonstrate the value of our model. Beyond confirmation through simulations of student learning, we report on a study with human users and expand on a previous pilot study. We demonstrate the effectiveness of our algorithms for selection of learning objects to solidify the overall defence of our approach.

@article{citeulike:13433164, abstract = {{In this paper, we present an algorithm for reasoning about the sequencing of content for students in an intelligent tutoring system, influenced by McCalla's ecological approach. We record with each learning object those students who experienced the object, together with their initial and final states of knowledge, and then use these interactions to reason about the most effective lesson to show future students based on their similarity to previous students. We validate our approach through a novel method of validation, providing details of the model of learning used in the simulation and the results obtained in order to demonstrate the value of our model. Beyond confirmation through simulations of student learning, we report on a study with human users and expand on a previous pilot study. We demonstrate the effectiveness of our algorithms for selection of learning objects to solidify the overall defence of our approach.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {Inderscience Publishers, Geneva, SWITZERLAND}, author = {Champaign, John and Cohen, Robin}, biburl = {https://www.bibsonomy.org/bibtex/2bf4766025e8c61ba6f89e0856848bd1b/aho}, citeulike-article-id = {13433164}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2579571.2579573}, citeulike-linkout-1 = {http://dx.doi.org/10.1504/ijlt.2013.059130}, doi = {10.1504/ijlt.2013.059130}, interhash = {6f872ed7a2d92c4ec2715cbf9189f767}, intrahash = {bf4766025e8c61ba6f89e0856848bd1b}, issn = {1477-8386}, journal = {Int. J. Learn. Technol.}, keywords = {personalized-learning sequencing student-modeling}, month = feb, number = 4, pages = {337--361}, posted-at = {2014-11-18 16:39:04}, priority = {0}, publisher = {Inderscience Publishers}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Ecological Content Sequencing: From Simulated Students to an Effective User Study}}, url = {http://dx.doi.org/10.1504/ijlt.2013.059130}, volume = 8, year = 2013 }