Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the Computing Education community. We hypothesise that this is due in part to discoverability, availability, and adaptability challenges. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to rely on community developed solutions that often lack a centralised authority and may be community or individually maintained. To this end, our working group explored what solutions are currently available, what instructors needed, and the reasons behind the above-mentioned phenomenon. To do so, we reviewed existing literature and surveyed the community to identify the tools that have been developed by the community; the solutions that are currently available and in use by instructors; what features are needed moving forward for classroom and research use; what support for extensions is needed to support further Computing Education research; and what institutional challenges instructors and researchers are currently facing or have faced in using community software solutions. Finally, the working group identified factors that limited adoption of solutions. This work proposes ways to integrate and improve the availability, discoverability, and dissemination of existing community projects, as well as ways to manage and overcome institutional challenges.
Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon.
[TinyMLedu](https://tinymledu.org) is working to build an international coalition of researchers and practitioners advancing TinyML in the developing world, and to develop and share high-quality, open-access educational materials globally.
In this paper, we describe our pedagogical approach to increasing access to applied ML through a four part massive open online course (MOOC) on Tiny Machine Learning (TinyML) produced in collaboration between academia (Harvard University) and industry (Google). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.
We describe the pedagogy behind the MIT Beaver Works Summer Institute Robotics Program, a new high-school STEM program in robotics. The program utilizes state-of-the-art sensors and embedded computers for mobile robotics. The program was offered as a four-week residential program at MIT in the summer of 2016.