Qualitative Representations for Education

Qualitative models capture conceptual knowledge about continuous phenomena and systems. This knowledge ranges from that of the person on the street, who has never taken formal mathematics or physics courses, to that of experts, such as scientists and engineers. Reasoning and learning techniques over qualitative representations have been used to create computational models of human commonsense reasoning, models of conceptual change, and models of how scientists and engineers reason in their professional work. Qualitative representations have been particularly useful in education, since they provide a level of knowledge that captures causality and everyday reasoning directly, while providing a substrate for professional knowledge.The interactive power of instructional software is significantly influenced by the richness and accessibility of the available domain knowledge. Qualitative reasoning (QR) formalisms are well suited from this perspective and are open to support learning for all kinds of tasks related to science, technology, engineering, and math (STEM). The representations are to a large degree domain-independent and can be used for a variety of systems, e.g., natural biological systems (cf. Kuipers and Kassirer, 1984; King, et al., 2005; de Jong et al., 2005; Noble et al., 2009) as well as human-created technical artifacts (cf. Shimomura et al., 1995; Price, 2000; Ironi and Tentoni, 2005).