Non-textual Feedback

The study setup of animations playing on IPads that the learners can arrange to form causal narratives

In presenting the formative feedback that supports explorations in Connectd Worlds, static representations (e.g., timelines, Sankey diagrams, and causal chains) were difficult for the educators to imagine using to support their interpretative engagement with visitors. The dynamic nature of the complex system embodied by Connected Worlds was not captured through them. One novel approach that emerged from interviews with pedagogical experts was the potential to use the “fugues” (short causal chains that explain complex concepts) to generate animated videos, similar to TikTok reels, that can illustrate the causal chains in a narrative fashion. We engaged with 74 families on the exhibit floor to aim to explore the desired features of such narrative animations that the educators could purpose.

A common fugue was represented through a series of short animated videos , wherein each 5 second short animation represented a causal part of the phenomena. These short videos could be connected together in varying orders to indicate different scientific phenomena like disequilibrium, instability, or sustainability within the complex system. This work proposed a new methodology that can help researchers understand the value in refining the representation of these fugue based animations for clearer comprehension about complex scientific concepts. The children within their family units were asked, (post interaction with Connected Worlds) to rearrange the shorter causal pieces to create a narrative that was experienced by them. Despite a defined prompt, the children came up with multiple causal narratives displaying an understanding of different conceptual complex concepts. We explored a number of scenarios as case-studies to expose the strengths of how animations were successful in exposing complex systems concepts that relate to distant causal connections, that are non-obvious and those that relate to both systemic and social contexts of their interactions (which has been a challenge in prior complex systems learning environments).