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

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