NASA DEVELOP Adaptation Model
Supported by AWS Proof-of-Concept Student Cloud Facility
Whilst effective models exist for the hardware engineering design process (such as the EDP), the software engineering process (such as Agile) and the Scientific Method for experiments (long promoted by the Royal Society), the future of space technology requires an approach that considers the bespoke, coded automation of engineering...
NASA DEVELOP has successfully provided field internships following a ten-week model, and more frequently these internships require skills in programming. This brings about a new challenge; engineering internships are starting to require computer science upskilling. Just like practising an instrument takes many years to master, coding similarly requires much practice, which our program intends to provide to students pre-university.
Alike to software engineering, NASA DEVELOP consider the range of concerns impacting the end-user. Our adaptation of the NASA DEVELOP model will serve to 'scaffold' multifaceted elements of a scalable engineering project, involving many styles of computer code. We are calling this method SCAFFOLDED-PROJECT-IMMERSION.
Our adapted learning model is also inspired to encompass Community Concerns, Project Objectives, Decision-Making Practices & Policies, Earth Observations & an End Products Overview, Product Benefit to the End User, a Project Continuation Plan and the additions of Failure Mode Thinking and Team-Coder Transparency. Learn more about NASA DEVELOP here
Reducing the concepts of an industry-scale automated project into manageable student field-project form, and by delivering the reduced automation codes for the student level project in one language, students can pull apart the different working exemplar code sections and also adapt them to make them their own.
This is carried out whilst the support of each and every other component of the full exemplar engineering project remains intact and working, so the student may make sense of the individual component they are working on, in context. Carried out on an existing AWS platform, the project will always be scalable.
