Mining students' inquiry actions for understanding of complex systems

This study lies at an intersection between advancing educational data mining methods for detecting students' knowledge-in-action and the broader question of how conceptual and mathematical forms of knowing interact in exploring complex chemical systems. More specifically, it investigates students' inquiry actions in three computer-based models of complex chemical systems when their goal is to construct an equation relating physical variables of the system. The study's participants were 368 high-school students who interacted with the Connected Chemistry (CC1(1)) curriculum and completed identical pre- and post-test content knowledge questionnaires. The study explores whether and how students adapt to different mathematical behaviors of the system, examines how these explorations may relate to prior knowledge and learning in terms of conceptual and mathematical models, as well as components relating to understanding systems. Students' data-collection choices were mined and analyzed showing: (1) In about half the cases, mainly for two out of the three models explored, students conduct mathematically-astute (fit) explorations: (2) A third of the students consistently adapt their strategies to the models' mathematical behavior: (3) Fit explorations are associated with prior conceptual knowledge, specifically understanding of the system as complex, however, the three explorations' fitness is predicted by the understanding of distinct sets of systems' components: (4) Fit explorations are only somewhat associated with learning along complementary dimensions. These results are discussed with respect to 1) the importance of a conceptual understanding regarding individual system elements even when engaged in large-scale quantitative problem solving, 2) how distinct results for the different models relate to previous literature on conceptual understanding and particular affordances of the models, 3) the importance of engaging students in creating mathematical representations of scientific phenomena, as well as 4) educational applications of these results in learning environments. (c) 2010 Elsevier Ltd. All rights reserved.