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

Markauskaite, Lina and Jacobson, Michael J. and Southavilay, Vilaythong and Kelly, Nick (2012) Using process analysis techniques to understand students' learning strategies with computer models. In: American Educational Research Association Annual Meeting (AERA 2012): Non Satis Scire: To Know Is Not Enough, 13-17 Apr 2012, Vancouver, BC. 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: 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.
Faculty / Department / School: Historic - Australian Digital Futures Institute
Date Deposited: 25 Feb 2015 04:58
Last Modified: 27 Sep 2017 02:54
Uncontrolled Keywords: process mining; hidden Markov model; hmm; model-based learning
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
12 Built Environment and Design > 1204 Engineering Design > 120405 Models of Engineering Design
13 Education > 1303 Specialist Studies in Education > 130309 Learning Sciences
Socio-Economic Objective: C Society > 93 Education and Training > 9302 Teaching and Instruction > 930203 Teaching and Instruction Technologies
URI: http://eprints.usq.edu.au/id/eprint/25577

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