Prediction of student actions using weighted Markov models

Huang, Xiaodi and Yong, Jianming and Li, Jiuyong and Gao, Junbin (2008) Prediction of student actions using weighted Markov models. In: IEEE International Symposium on IT in Medicine and Education (ITME 2008) , 12-14 Dec 2008, Xiamen, China.


Abstract

The Markov model has been applied to many prediction applications including the student models of intelligent tutoring systems. In this paper, we extend this well-known model to the weighted Markov model, and then apply it to student models in order to predict student behaviors. The prediction using our models is based not only on the frequency of collective behaviors of previous users, but also on the degrees of the relations between the predicted user and others. In doing so, a novel way is presented to quantify the similarities between previous students and the current active student. These similarity scores will be used as weights in the weighted Markov model.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Business - School of Information Systems (1 Apr 2007 - 31 Dec 2010)
Faculty/School / Institute/Centre: Historic - Faculty of Business - School of Information Systems (1 Apr 2007 - 31 Dec 2010)
Date Deposited: 13 Oct 2009 03:11
Last Modified: 12 Feb 2015 06:05
Uncontrolled Keywords: weighted Markov model; prediction; student behaviour
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Identification Number or DOI: https://doi.org/10.1109/ITME.2008.4743842
URI: http://eprints.usq.edu.au/id/eprint/5838

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