DynaLearn - An Intelligent Learning Environment for Learning Conceptual Knowledge

46 AI MAGAZINE Modeling is regarded as fundamental to human cognition and scientific inquiry (Schwarz and White 2005). It helps learners express and externalize their thinking, visualize and test components of their theories, and make materials more interesting. Particularly, the importance of learners constructing conceptual interpretations of system behavior has been pointed out many times (Mettes and Roossink [1981], Elio and Sharf [1990], Ploetzner and Spada [1998], Frederiksen and White [2002]). Modeling environments can thus make a significant contribution to the improvement of science education. A new class of knowledge construction tools is emerging that uses logic-based (symbolic, nonnumeric) representations for expressing conceptual systems knowledge (Forbus et al. 2005, Leelawong and Biswas 2008, Bredeweg et al. 2009). Different from the numeric-based tools (Richmond and Peterson 1992, Pratap 2009), these tools employ a qualitative vocabulary (Forbus 2008) for users to construct their explanation of phenomena, notably about systems and how they behave. The use of graphical interfaces has improved usability (Bouwer and Bredeweg 2010), and the tools are becoming more common in education (Forbus et al. 2004, Kinnebrew and Biswas 2011), and professional practice (Bredeweg and Salles 2009). The DynaLearn interactive learning environment (ILE) can be regarded as a member of this new class of tools. Its development is directly motivated by specific needs from the edu-