View source for Challenge Design and Mission Execution

Designing Data Science Challenges: Gather a team of AI/Data Science experts, event organizers, and media production/User Experience designers. They will form a Challenge Design team to design a series of workshops that teaches novices how to utilize Raspberry Pis to resolve certain domain-specific applications. This team will work together for about a month, and create a workshop as a set of executable missions. This workshop is ideally executed in one or two days, so that it can fit into a weekend. All or parts of the workshop should be operable online, so that remote participants can also be joining the efforts to learn and create solutions during the two days. 
Testing and Executing the Designed Missions: Event organizers and local AI/Data Science community leaders, local school teachers, should identify an initial set of participants to experience their designed missions. The success rates and bugs in the design missions will be recorded, analyzed and refined, then, a full deployment package containing these solutions will be deployed as a service product with a complete set of material and instrumentation required to execute the mission. (In most cases, Raspberry Pi will be used and reused in these missions.) This packaged material set will be bundled in a physical box, ready to be shipped to multiple locations for parallel execution of the same mission specification.
Repeatedly Executing the Designed Missions: Once a Designed Mission has gone through proper qualification procedures, it will be deployed to a set of prescribed target audience for mission execution. Some of the missions will be completely online, meaning no need for physical presence or delivery of physical goods. Some of the missions will rely on components or devices that must be provided by mission executors. In either case, relevant data will be captured by the targeted mission executors, and submitted to the Challenge Design team.  deployed to Using Raspberry Pi-based data servers to capture these online/offline event records, success rate of mission execution will be analyzed and 
te in the , event organizers, and media production/User Experience designers. They will form a Challenge Design team to design a series of workshops that teaches novices how to utilize Raspberry Pis to resolve certain domain-specific applications. This team will work together for about a month, and create a workshop that is ideally executed in one or two days, so that it can be fit into a weekend. All or parts of the workshop should be operable online, so that remote participants can also be joining the efforts to learn and create solutions during the two days.A learning outcome assessment workflow that is designed to work with the programming interfaces mentioned above, so that it can compare students’ learning outcomes given their usage history of the above-mentioned Interactive Games with a consistent User Experience.
A set of games (hundreds of interactive games, videos and animations) authored by instructors and [[Gasing Method ]] practitioners. These games should also include learning diagnostic data presentation interfaces. They will be able to help users diagnose the strengths and weaknesses of the learning agents and agencies.
An operational procedure, and regular workshops that help schools in Indonesia to replicate the MU three-layered framework for their local educational needs. Develop a data exchange framework to protect data privacy, while allowing objective and trust-worthy data witnessing of student learning outcomes.
Document the unique features of the educational program with student and instructor content. The content of this document will present the key ingredients of the educational material ([[Gasing Method]]) and student learning outcomes such as their learning project results. 
Students and instructors will design a MU-makerspace, aimed to be replicated around Indonesia. The design plan will be presented on a PKC instance operating on a Raspberry Pi cluster built by the first-year student. Computer hardware, software, and Internet of Things projects will be part of the first-year curriculum. Based on the operating experience of this overall curriculum, students, instructors, and campus administrators will co-design the MU-makerspace and present it as an open-sourced Makerspace design plan.
A Capability Demonstration Project: Dr. Irendra Radjawali will lead students in the first year to work with globally distributed researchers to demonstrate students’ data literacy using Raspberry Pis, Arduinos, and relevant sensor/actuators to create physically engaging Internet of Things applications. Initially, a set of drones controlled by Raspberry Pi, and deploy these drones to collect local maps.

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