ICCS 2018 Workshop: Complex Systems: Literacy and Learning

Thursday July 26 6-9PM.

Agenda, Abstracts and Speaker Bios

Workshop Description

Over the past two decades the nature of science has radically shifted away from reductive, atomistic reasoning (that all scientific questions can ultimately be answered by breaking phenomena into smaller and smaller pieces), to complexity, systems or network thinking (in which we can deepen our understanding of nature through examining the patterns and interactions within and among complex systems). This revolution in understanding has demanded a transformation in the nature of tools and methods for science. The technologies for gathering data and analyzing the complexity of nature are increasingly centered around tools and methods for: streaming data from such sources as remote sensing satellites and environmental sensors; protein mass spectrometry; and genomics. Much of science is now data-driven and relies on access to high speed and distributed computing, big data federation and analysis, and machine learning.

Unfortunately, this revolution in science has not been accompanied by a revolution in science teaching and learning. Educational systems worldwide are not keeping up with the explosion in big data and data-driven sciences that are increasingly necessary for an innovative workforce, nor are they providing even a basic understanding of how this change in technologies affect our lives, inform us about vital trends, and have the potential to empower us to solve our greatest social and environmental challenges. This escalation in the complexity of the kinds of biomedical, socio-economic, environmental, and technological problems science has to deal with, along with the ability to gather and store vast amounts of data, brings with it skills needed by the 21st century STEM workforce including, but not limited to:

●      The ability to interact with and extract knowledge from large amounts of dynamic data.

●      Facility with visualizing complex, multivariate and dynamic data streams in order to see patterns in, and extract useful knowledge from big data.

●      The ability to understand the changing role of scientific and engineering models.

●      Higher-order thinking associated with models that can accommodate probabilistic and stochastic events.

●      The ability to synthesize exploratory and inductive skills to identify general patterns and characterize their behavior across a wide range of differing environments and processes.

In this interactive workshop we will explore the wide range of efforts being made to address this gap in education, and collaborate on potential new approaches.