Using process analysis techniques to understand students' learning strategies with computer models

Paper

Markauskaite, Lina, Jacobson, Michael J., Southavilay, Vilaythong and Kelly, Nick. 2012. "Using process analysis techniques to understand students' learning strategies with computer models." Tyson, Cynthia A. (ed.) 2012 American Educational Research Association Annual Meeting (AERA 2012). Vancouver, Canada 13 - 17 Apr 2012 Washington, DC. United States.
Paper/Presentation Title

Using process analysis techniques to understand students' learning strategies with computer models

Presentation TypePaper
AuthorsMarkauskaite, Lina (Author), Jacobson, Michael J. (Author), Southavilay, Vilaythong (Author) and Kelly, Nick (Author)
EditorsTyson, Cynthia A.
Journal or Proceedings TitleProceedings of the 2012 American Educational Research Association Annual Meeting (AERA 2012)
Year2012
Place of PublicationWashington, DC. United States
Conference/Event2012 American Educational Research Association Annual Meeting (AERA 2012)
Event Details
2012 American Educational Research Association Annual Meeting (AERA 2012)
Parent
American Educational Research Association Annual Meeting (AERA)
Delivery
In person
Event Date
13 to end of 17 Apr 2012
Event Location
Vancouver, Canada
Abstract

This work is a part of a larger project that investigates how high school students learn scientific knowledge of climate change with computer models. The paper presents our progress developing a methodology for capturing learning process data and preliminary results from the analysis of learning strategies adopted by high achieving and low achieving students. Our approach is based on the analysis of process data using the Hidden Markov Model (HMM) technique. Drawing on the initial results, we illustrate how the HMM can help to depict some important features of students' learning strategies. Overall, our findings indicate that successful learners adopt deeper and more systematic model exploration strategies than less successful learners.

Keywordsprocess mining; hidden Markov model; hmm; model-based learning
ANZSRC Field of Research 2020370201. Climate change processes
401001. Engineering design
390409. Learning sciences
Public Notes

This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source.

Byline AffiliationsUniversity of Sydney
Institution of OriginUniversity of Southern Queensland
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