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Prediction of Student Success or Failure Without Domain Models or Formal Assessments

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dc.contributor.advisor McCuaig, Judi
dc.contributor.author Baldwin, Julia
dc.date.accessioned 2013-08-15T15:07:58Z
dc.date.available 2013-08-15T15:07:58Z
dc.date.copyright 2013-08
dc.date.created 2013-08-07
dc.date.issued 2013-08-15
dc.identifier.uri http://hdl.handle.net/10214/7368
dc.description.abstract Models of student knowledge provide intelligent tutoring systems(ITS) with the required information to make informed adaptations that improve student learning. Student models are often based on a domain model and can be cumbersome to build. This work demonstrates that it is possible to create a model of student interactions with a Learning Management System(LMS) that can be used to predict student success at any point during the semester without a domain model or the use of formal evaluations. This research shows that it is possible to predict student success or failure early and accurately for students who will do very poorly or very well in the course. This work describes the framework of a domain independent student model that can predict student success throughout the semester. en_US
dc.language.iso en en_US
dc.subject EDM en_US
dc.title Prediction of Student Success or Failure Without Domain Models or Formal Assessments en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
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