Help the robot efficiently build with blocks and navigate the city paths to make gift deliveries in STEM-tastic Adventures! The STEM-tastic Adventures app and related hands-on activities for home and classroom (coming soon!) were co-designed with preschool educators and families, media and curriculum developers, and researchers. These resources combine traditional common learning activities with digital learning to promote young children’s engagement with developmentally appropriate computational thinking, mathematics, and science skills and practices across school and home. Research shows that using the digital resources and hands-on activities helped children learn important computational thinking and STEM concepts.

STEM-tastic Adventures allows children to play two different games: Better Building and City Walk. In Better Building, children sort and label blocks to help build more efficiently. In City Walk, children provide a sequence of directions to deliver gifts to happy recipients. Each of these adventures promotes computational thinking skills, while also engaging with relevant mathematics concepts and science practices.

City Walk promotes:
-Computational Thinking.
Identify a precise set of steps/instructions to achieve a goal or solve a problem. Identify an error and determine how to fix it.

-Math concepts.
Provide directional commands that allow others to navigate pathways. Use spatial vocabulary such as left, right, forward, and backward.

Better Building promotes:
-Computational Thinking.
Label groups of objects by identifying important information that should be highlighted in the label while ignoring details that are not necessary to describe the group.

-Science practices.
Identify characteristics that are similar and different across objects. Determine characteristics by which to sort objects. Sort objects into groups given specific characteristics.

App features include:
-Multiple level gameplay
-Adaptive feedback
-Based on research-based early learning trajectories

Copyright
This app is licensed under the following licenses:
https://creativecommons.org/licenses/by-sa/3.0/
https://creativecommons.org/licenses/by/4.0/
https://www.gnu.org/licenses/gpl-3.0.html

This material is based upon work supported by the National Science Foundation under Grant DRL# 1827293. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.