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Over the last 20 years, mobile computing has evolved to encompass a wide array of increasingly data-rich applications. Many of these applications were enabled by the Cloud computing revolution, which commoditized server hardware to support vast numbers of mobile users from a few large, centralized data centers. Today, mobile's next stage of evolution is spurred by interest in emerging technologies such as Augmented and Virtual Reality (AR/VR), the Internet of Things (IoT), and Autonomous Vehicles. New applications relying on these technologies often require very low latency response times, increased bandwidth consumption, and the need to preserve privacy. Meeting all of these requirements from the Cloud alone is challenging for several reasons. First, the amount of data generated by devices can quickly saturate the bandwidth of backhaul links to the Cloud. Second, achieving low-latency responses for making decisions on sensed data becomes increasingly difficult the further mobile devices are from centralized Cloud data centers. And finally, regulatory or privacy restrictions on the data generated by devices may require that such data be kept locally. For these reasons, enabling next-generation technologies requires us to reconsider the current trend of serving applications from the Cloud alone. 

BRIGHT-CS (Building Student Retention through Individuated Guided coHort Training in Computer Science) is a research and development project that 1) creates a computer science learning ecosystem for middle school Black girls and other girls of color and 2) researches the merits of the ecosystem in supporting persistence in CS to determine best practices for broadening participation to other marginalized student groups in computing. First, this paper describes the BRIGHT-CS program, from the structural, instructional, and curricular designs of the program to partnerships with local and community organizations that make up the ecosystem. Second, it presents the initial findings of research on the program and its impacts on student outcomes such as social-emotional attributes associated with persistence. The study employs a multi-method descriptive design. Data includes student surveys, interviews (from students, parents, instructors, teachers, and mentors), artifact reviews, and student observations. The study includes 46 students across four middle schools in two states. At the start of the program, 37% of the students reported being very interested in CS, and 72% reported being very confident in learning CS. This is much higher than a national benchmark of students. After four months of program implementation, the qualitative results show a more nuanced picture of the value of a learning ecosystem. First, the ecosystem offers implicit messaging about equity and success. Second, the ecosystem offers explicit messaging about personal challenges and improvement. Third, following the implicit and explicit messaging to students, students went from naïve confidence to authentic self-efficacy in CS.