• <do Jan>Add students to Keycloak (Jan) including group and role, e.g. WS2021_IP17</do>
• <do>Setup a editable Wiki branch for student documentation</do>
• <do Jan>Setup Nextcloud Docker for files Sharing</do>
• <do>Setup Discord Server including categories (IP …) and channels (General Discussion? Announcements?) → Responsive comm.</do>
• <do>(Mattermost?)</do>
• <do>Missing orders! Powerback (350€ incl. tax), Drivers (291€ incl. tax)</do>

Main goal: Develop, design and implement teaching materials on artificial intelligence (AI) for schools (primary schools, age 9-11 years).

Requirements:

• Learning path (easy to follow): The teaching material should be easy to follow, with a defined structure that connects each of the topics in a way that is easy to absorb. The material can even have a story telling that proposes solutions to current problems such as global warming, garbage in the oceans, deforestation and more.
• Interactive: The content must be interactive, with gamification strategies that encourage curiosity to continue learning.
• Learning experience:

• Teaching material, design and implement
• Material for primary schools (age 9-11)
• SNAP! tutorial first (age 9-11)
• SNAP! as a frontend for image classification
• Train CNN with your own object
• Jetbot follows your own object
• Object Detector with Camera on Jetson (or rtp, rtsp, rtmp) with ssd-mobilenet
• Enhanced NVIDIA Jetson tutorial for OD suitable for kids (NVIDIA is still too complex)

• OER prototyping on Wiki
• Structure, Material, Content
• Later implementation on Moodle, openEdX, etc.
• Workshop with NVIDIA material for Jetson (understanding complexity!):
  • Dustin Franklin's material first (detectnet, IC, OD, IS)
  • Dana Sheahen's material (Jupyter, image classification)
  • Jim Benson's material (Jetbot, Linux, setup of Jetsons)
• Learn SNAP!
• Concept to communicate between detectnet and SNAP!
• Teaching material