@inproceedings{0841c5609dd14d8f868018a32dfeb342,
title = "Stage predicting student stay time length on webpage of online course based on Grey models",
abstract = "To provide adaptive learning, an e-learning system needs to gather information about what student state is while the student learns online course. A student state index is the length of time the student stays on a webpage of online course. By predicting student's stay time length, the e-learning system has potential to dynamically tailor the learning content to the students. A literature review is conducted on power law of learning and knowledge component. We assume that online course consists of knowledge components. A knowledge component crosses several successive web pages. Accordingly, an initial prediction method is proposed based on the two learning curve modes and the grey models. Based on the experimental result of this initial prediction method, construction method of grey models is modified. The results produced by the grey models based on the two construction methods are then compared and analyzed. The results show that prediction of stay time length is possible to certain degree while the students learn knowledge on web pages.",
keywords = "Grey Model, Knowledge Component, Learning Curve Mode, Power Law, Stage Prediction",
author = "Qingsheng Zhang and Kinshuk and Sabine Graf and Chang, {Ting Wen}",
year = "2011",
doi = "10.1007/978-3-642-25813-8_24",
language = "English",
isbn = "9783642258121",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "226--232",
booktitle = "Advances in Web-Based Learning, ICWL 2011 - 10th International Conference, Proceedings",
note = "10th International Conference on Advances in Web-Based Learning, ICWL 2011 ; Conference date: 08-12-2011 Through 10-12-2011",
}