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Research shows that students have difficulty achieving deep understanding of many fundamental science concepts, for instance, the nature of matter, pressure, density, and electrical circuits to name but a few. After students have presumably learned the scientific explanations, they often revert back to their initial explanations. The Understandings of Consequence Project has demonstrated that part of the problem arises from differences in how students and scientists think about cause and effect. Scientific explanations often require students to structure knowledge in ways that contradict their expectations about the nature of how causes and effects behave. Such explanations can involve: causal mechanisms that are inferred or abstract; causal patterns that extend beyond linear and unidirectional to cyclic, reciprocal, and non-sequential; correspondences between causes and effects that are in various respects probabilistic; and causal agents that are decentralized and involve aspects of emergence. These are ways of thinking that students typically are not familiar with. Thus students attempt to assimilate information about complex concepts into simplistic causal structures which ultimately distort the information. In order to achieve deep understanding of scientific explanations, students need to learn the levels of these dimensions that fit the level of explanation needed. We have developed a taxonomy of causal models to guide these teaching and learning efforts. We have also developed a taxonomy of epistemological "moves", such as comparing more than one model and being alert to possible gaps in one's explanation, that serve scientific inquiry and lead to more complex conceptions.

Playable fictions are interactive stories in which one is positioned as a protagonist who makes game choices that have consequence in the fictional world. Further still, we often find ourselves engaging in struggles and developing understandings that shape player's worldview outside of the game.