With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technologies. It has become increasingly important for them to understand how these technologies are trained, what their limitations are and how they work. To introduce children to AI and Machine Learning (ML) concepts, recent efforts introduce tools that integrate ML concepts with physical computing and robotics. However, some of these tools cannot be easily integrated into building projects and the high price of robotics kits can be a limiting factor to many schools. We address these limitations by offering a low-cost hardware and software toolkit that we call the Smart Motor to introduce supervised machine learning to elementary school students. Our Smart Motor uses the nearest neighbor algorithm and utilizes visualizations to highlight the underlying decision-making of the model. We conducted a one week long study using Smart Motors with 9- to 12- year old students and measured their learning through observation, questioning and examining what they built. We found that students were able to integrate the Smart Motors into their building projects but some students struggled with understanding how the underlying model functioned. In this paper we discuss these findings and insights for future directions for the Smart Motor.

When a human dyad jointly manipulates an object, they must communicate about their intended motion plans. Some of that collaboration is achieved through the motion of the manipulated object itself, which we call "haptic communication." In this work, we captured the motion of human-human dyads moving an object together with one participant leading a motion plan about which the follower is uninformed. We then captured the same human participants manipulating the same object with a robot collaborator. By tracking the motion of the shared object using a low-cost IMU, we can directly compare human-human shared manipulation to the motion of those same participants interacting with the robot. Intra-study and post-study questionnaires provided participant feedback on the collaborations, indicating that the human-human collaborations are significantly more fluent, and analysis of the IMU data indicates that it captures objective differences in the motion profiles of the conditions. The differences in objective and subjective measures of accuracy and fluency between the human-human and human-robot trials motivate future research into improving robot assistants for physical tasks by enabling them to send and receive anthropomorphic haptic signals.

In this innovative practice paper, we present case studies of participants using a trainable robotics tool called Smart Motors from two workshops: one with participants from elementary schools in a robotics camp and the second with students from a high school. We designed the workshops to help us observe two key aspects: how students engage with the tool as new users from different age groups and the variability in levels of enjoyment and iterative thinking with an engineering design task while using the tool. Learning robotics can be complex as it can involve learning programming, mechanical design, and electrical circuits simultaneously. Some existing robotics kits support beginners with mechanical design tasks via easy-to-integrate sensor kits, motors, and control hubs, and some support beginners with alternative programming methods like web-based block coding languages and QR codes. However, cost, availability, and ease of use make integrating robotics into the curriculum difficult for some classes. Smart Motors are easy-to-use, low-cost alternatives to lower those barriers to bringing robotics into the classroom. Smart Motors simplifies mechanical “set-up” by packaging the user interface, motor, and sensor in one unit. They use training and a machine learning algorithm called the nearest neighbor to make decisions. Since they do not require coding to generate desired outcomes, students can use them in classrooms without computers or internet access. We allowed first-time Smart Motor users to learn the tool while participating in a play-based design activity with three sessions. The first session of the workshop was a discussion of Artificial Intelligence and Machine Learning. The second session was an introduction to Smart Motors, in which the participants built a “hello-world” waving contraption using Smart Motors and LEGO® pieces. The third session consisted of an activity derived from Tufts University's Novel Engineering curriculum, where the participants listened to a story, chose a character, and designed solutions for them. We analyzed their work with the help of field notes and video data. Using the Learning through Play Experience Tool, we looked for evidence of two of the five characteristics of playful learning: Joyful and Iterative. We transcribed the video data in detail and coded for the states of play in two-minute chunks, where we looked at and analyzed the play trends. We found that Smart Motors supports playful learning in engineering design workshops, allowing participants from different ages and experience levels to engage creatively to design products of various complexity.

Systems engineering is the interdisciplinary process of managing and executing complex engineering projects. As K-12 education increasingly emphasizes STEM education and engagement, our project-based systems engineering curriculum aims to add to the existing body of effective instructional approaches for stronger collaboration, problem-solving, and engineering skills in students. We designed and tested our curriculum at Acera, an independent Massachusetts school for science, creativity, and leadership. Elementary school students at a four-day Acera camp participated in our systems engineering project: building a smart model of a LEGO city. Split into teams, students earned badges for developing skills such as Python coding, and Robotics as they ventured to build a school and a town hall, a transportation system, and even a renewable energy source. Our field observations and interviews revealed patterns of motivation and applications of systems thinking in our students. We found that gamification, badges, and earnable points both engaged and united students by rewarding growth in STEM skills and setting achievable, tangible, class-wide goals. Students encountered problems head-on, working on faulty code or missing railroad pieces without direction from an adult, demonstrating our project's facilitation of implicit motivation. Students also communicated across teams by suggesting new tasks, which was rewarded with class-wide points, or adjusting their own work based on the results of another team, demonstrating an adoption of systems thinking. All of their efforts culminated into a city of many moving parts-motorized trains, color-sensor cars, windmills, and conveyor belts. Each part was made by a different team, so each team had to collaborate across disciplines to create the LEGO city.

Motivated by the philosophical overlap between makerspace culture and the needs of assistive technology users, we investigated the ways that makerspaces can support the development of new technologies by and for disabled makers. Using eleven semi-structured interviews with makerspace operators and disabled makerspace users, we identified five categories of barriers to makerspace participation: recruitment/outreach, physical access, financial, access to information, and belonging. Based on these interviews, we highlight ways makerspaces can better welcome makers with disabilities: enabling members to create adaptive technologies for the space (“makerspacing the makerspace”), making the physical space and information within the space accessible, and fostering belonging by building relationships with the disability community. Overall, our work contributes to our understanding of the possibilities and challenges of connecting the disabled community with the maker community and suggests new directions for collaboration, especially towards building hybrid makerspaces that provide multiple modalities for connection and creativity.

Smart Motors is a low-cost, low-barrier-to-entry trainable robotic system enabling playful learning with technology in various classroom types. Many kits and online tools have been developed as educational resources for children to learn about robotics, enabling an increasingly widespread integration of engineering and robotics into classrooms. Despite the positive features of many existing technologies, however, issues of equitable access for students in under-resourced classrooms have emerged: the high costs of many of the kits and the need for internet connectivity, computers, and technical expertise on the part of teachers all limit their adoption. Smart Motors offers a low-cost solution based on readily available materials, enabling students in diverse educational contexts and ages to gain exposure to robotics and artificial intelligence. Users can train this type of motor to run to various positions corresponding to different sensor inputs, enabling students to bring their engineering projects to life. The underlying shift from coding to training robots eliminates the need for access to computers and the internet for students to engage in hands-on robotics activities. Furthermore, the system can be developed with locally available materials, enabling its adoption in diverse and international settings. In this paper, we describe the international collaboration in developing Smart Motors, detail various prototypes built with locally on-hand materials, describe the issues we have faced in this endeavor, and outline future plans. We are creating an international community of engineers and educators developing new designs and testing various materials available in local settings. This global network enables a wide range of educational contexts to be at the core of the development process. Studying students' engagement with the system at workshops further ensures that the product is accessible and contributes to students' and teachers' learning in various settings.

New developments to computer-aided design (CAD) software transform a once solitary modelling task into a collaborative one. The emerging multi-user CAD (MUCAD) systems allow virtual, real-time collaboration, with the potential to expand the learning outcomes and teaching methods of CAD. This paper proposes a MUCAD collaborative learning framework (MUCAD-CLF) to interpret backend analytic data from commercially available MUCAD software. The framework builds on several existing metrics from the literature and introduces newly developed methods to classify CAD actions collected from users’ analytic data. The framework contains two different classification approaches of user actions, categorizing actions by action type (e.g., creating, revising, viewing) and by design space (e.g., constructive, organizing), for comparative analysis. Next, the analytical framework is applied via a collaborative design challenge, corresponding to over 20,000 actions collected from 31 participants. Illustrative analyses utilizing the MUCAD-CLF are presented to demonstrate the resulting insight. Differences in CAD behaviour, indicating differences in learning, are observed between teams made up entirely of novices, entirely of experienced users, or a mix. In pairs of experts and novices, we see both a perceived high-satisfaction apprenticeship experience for the novices and preliminary evidence of an increase in expert design behaviours for the novices. The proposed framework is critical for MUCAD systems to make the most of the educational possibility of combining technical skill-building with team collaboration. Preliminary evidence collected in a fully-virtual design learning activity, and analyzed using the proposed MUCAD-CLF, shows that novice students gain advanced CAD design knowledge when collaborating with experienced teammates. With the user data captured by modern MUCAD software and the MUCAD-CLF presented herein, instructors and researchers can more efficiently assess and visualize students’ performance over the design learning process.

Computational thinking is nowadays considered an essential skill in the K-12 educational curriculum. Many tangible computational kits designed for early childhood are either too expensive to benefit a wide range of children or only provide predetermined challenges with limited creative content creation opportunities. In this paper, we investigated low-cost and expressive tangible interfaces that foster computational literacy. We present Draw2Code, a paper-based computational kit for young children to create an interactive AR animation. Children use Draw2Code to make their paper drawing alive as an animated virtual sprite and control it using hand gestures. It exposes children to basic programming concepts through playful and tangible interaction. Results from our initial evaluation with nine child-parent dyads indicate that children ages 5 to 12 successfully used Draw2Code and played with Draw2Code in 33 minutes on average while creating 2 to 5 diverse AR animations based on their ideas. Throughout the session, all children were engaged in computational thinking concepts and practices and learned drawing and gesture-based interactions.

In this WIP-Innovation Practice, the authors present a shared Collaborative Online Robotics platform embedded in Google Slides application for students from primary grades to University to enhance the learning and teaching process in online and blended/hybrid environments. This Computer Supported Cooperative Work (CSCW) system is intended to facilitate an immersive experience in a collaborative virtual environment by combining physical digital artifacts with remote STEM-based instruction. Particularly relevant during COVID-19, it also helps connect students potentially isolated by other factors (geographic, economic, environmental, health limitations, etc.). In this paper we present a first approach to the performance, acceptance, and adherence to a novel remote collaborative platform that facilitates STEM and Social Emotional Learning (SEL) enhanced by the interaction of a multiple-users with a LEGO MINDSTORM EV3 Robotic platform based on the Positive Technological Development (PTD) framework.

This paper presents an analysis of two comparable studies with LEGO Robotics-based activities in a social skills training program for children with autism spectrum disorders (ASD). One study has been carried out with a group of 16 children in the Unit of Pediatrics Psychology and Psychiatry in HSJD in Barcelona, Spain and the other with a group of 17 children at the Center for Education and Engineering Outreach (Tufts U.) in Boston, USA. The aim of this comparison is discuss lessons learnt and develop empirical based guidelines for intervention design.

This paper proposes a novel architecture supporting dynamic load balancing on an FPGA for a molecular dynamics algorithm. Load balancing is primarily achieved through the use of specialized processing units, referred to as FLEX units. FLEX units are able to switch between tasks required by a molecular dynamics algorithm as often as needed in order to cater to the nature of the input parameters. This architecture is capable of run-time performance analysis and dynamic resource allocation in order to maximize throughput. Results of a prototype of the architecture targeting an FPGA are presented.