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Custom Image recognition applied to classroom environments in a study of the effects of technology and classroom strategies on learning.

Classroom Configuration Identifier (CCID) is an application utilizing AlexNet - an image recognition convolutional neural network (CNN) - trained and used for classification of classroom configurations.

CCID is currently capable of categorizing footage of Santa Clara University's classrooms as

  1. Forward-Facing Lectures
  2. Circular or 'U' Shaped Lectures and/or whole class discussions
  3. Smaller Group discussions and
  4. Empty classrooms with 97% accuracy.

Further work on the sub-categorization of these categories is underway.

CCID has been developed as a component to an ongoing, larger study into the effects of classroom configuration on student learning outcomes.

While CCID specifically deals with the categorical analysis of the classroom configuration itself, the next phases of the study will couple this classroom data with anonymized student data, in order to draw conclusions about the most optimal classroom configurations for enhancing learning.  

By analyzing correlations between different classroom configurations and corresponding student performance, the study will ultimately be able to supply educators with the information needed to setup more effective learning environments.