This paper discusses an instructional design heuristic called "emergent modeling", with an instructional sequence on data analysis as an example. The emergent modeling approach is presented as an alternative for instructional approaches that focus on teaching ready-made representations. In relation to this, a distinction is made between modeling as "translation" and modeling as "organizing". Emergent modeling fits the latter. Within this perspective, the model and the situation modeled are mutually constituted in the course of modeling activity. This gives the label "emergent" a dual meaning. It refers to both the process by which models emerge, and the process by which these models support the emergence of more formal mathematical knowledge. This is reflected in the exemplary instructional sequence, in which the model co-evolves with the notion of distribution as an entity.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education