ECR Projects

Explore past and current fundamental STEM education research projects across the three research areas that NSF's EDU Core Research (ECR) program funds, as well as across ECR funding types. Other search filters draw from both NSF's data and the ECR Hub's hand coding of award abstracts.

Ninth-grade biology students create cell models using clay.

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Large Scale Neuron Reconstruction through Development of Crowdsourced Reconstruction Experts

Effective Years: 2015-2017

This award is supported by the EHR Core Research (ECR) program. The ECR program of fundamental research in STEM education provides funding in critical research areas that are essential, broad and enduring. EHR seeks proposals that will help synthesize, build and/or expand research foundations in the following focal areas: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The ECR program is distinguished by its emphasis on the accumulation of robust evidence to inform efforts to (a) understand, (b) build theory to explain, and (c) suggest interventions (and innovations) to address persistent challenges in STEM interest, education,learning, and participation.

Neuroscience is arguably one of the most important sciences in terms of potential breakthroughs in the next decade. Through a neuroscience game, the PI expects that people will learn many aspects of science that are directly and indirectly related to the game. There is strong evidence of this collateral learning process in the Foldit community where many people learned more about proteins and shared this knowledge with hundreds of their team members. Foldit is an online puzzle game about protein folding.

The PI will build a virtual gaming environment around neuron reconstruction that carefully scaffolds instruction along with social support and a reward system for novice players. This would allow motivated players to contribute to neuroscience directly by performing neuron reconstructions, independently verifying others' results, iteratively testing interfaces, visualizations and reconstruction tools, as well as collectively developing a corpus of knowledge around the activity of neuronal reconstruction that can be studied and absorbed back into automated methods. These results will be fed into a new database created in a subsequent larger project - to classify neurons.