WHAT WE DO?

Science Through Technology Enhanced Play

In this Cyberlearning: Transforming Education DIP (Development and Implementation) project, the PIs are investigating how embodied play among elementary school students can be used to help them understand scientific phenomena (e.g., the working of forces, complex behaviors of bees).

They are instrumenting elementary school classrooms with advanced tracking. The PIs are building upon the ways that young children engage in socio-dramatic play. Through role play, children explore and reflect upon the complex rules that govern the world, and in this project, they are asked to play roles in a natural system (e.g., bees in a beehive) and identify the rules that would make their role play match the workings of the natural system.

Motion capture technology is used to record their interactions, and they reflect together as a class after role-playing experiences. Research focuses on the qualities of reflection and subsequent learning afforded by two aspects of such play -- when children interact to plan their role play (equivalent to modeling) and when they act out the system or phenomenon (equivalent to simulation) and then have a chance to examine their interactions.

To understand the affordances and importance of embodied play, reflection and learning outcomes are also compared across conditions of acting out a system or phenomenon (1st person embodied simulation) and running a computer simulation of that same system or phenomenon (3rd person virtual simulation).

TECHNOLOGY THAT WE USE

OpenPTrack

OpenPTrack is an open source project launched in 2013 to create a scalable, multi-camera solution for person tracking.

It enables many people to be tracked over large areas in real time.

It is designed for applications in education, arts, and culture, as a starting point for exploring group interaction with digital environments.

Based on the widely used, open source Robot Operating System (ROS), OpenPTrack provides:

  • user-friendly camera network calibration;
  • person detection from RGB/infrared/depth images;
  • efficient multi-person tracking;
  • UDP and NDN streaming of tracking data in JSON format.

COLLABORATORS

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WANT TO COLLABORATE?

Noel Enyedy
enyedy@gseis.ucla.edu