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

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Title: Prediction of Student Success or Failure Without Domain Models or Formal Assessments
Author: Baldwin, Julia
Department: School of Computer Science
Program: Computer Science
Advisor: McCuaig, Judi
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.
URI: http://hdl.handle.net/10214/7368
Date: 2013-08


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