New York University, Steinhardt School of Culture, Education, and Human Development
A majority of my research resides in the physical computing domain, where learners can engage with technology at the intersection of computer science and electronics. These experiences facilitate learners to create tangible “things” that interact with the physical environment and the people within it. For example, making a sculpture that changes color and shape based on the weather data correlated to the location someone touched on a map. Designing learning experiences and educational tools to facilitate novices to create with physical computing, creates a complexity of challenges for both the educators and the learners. In this post, I provide one perspective of how attending to data could expand how we design for playful learning experiences within the physical computing domain.
Physical computing experiences encapsulate a number ways one can engage with technology, but for the purposes of this post, I’m going to refer to it as describing a situation in which the learner can engage in building circuits and developing code to create programs that interact with their circuits (ex. working with the Arduino, Micro:Bit, or Circuit Playground). This space is of particular interest to me because it provides learners with multiple ways to engage with technology. However, its complexity has led some researchers to be wary of these types of experiences for novices without lending a critical eye for what the learners are being exposed to (Blikstein, 2015). While those concerns resonate with me, a perspective focused on data offers some insight into how we can provide novices with opportunities to navigate through the complexities presented in this space. Further, attending to play forces us to consider the independence and agency of the learner outside of the educator. With this perspective, I will discuss two areas where focusing on data could guide expansion of the research for creating playful learning experiences with physical computing. First, I will examine how we might capitalize on data from online DIY projects, and second, I will explore how we might attend to the representations of data provided by the tools.
Online DIY Projects
The physical computing domain is uniquely situated because the physical nature presents opportunities for learners to engage in a variety of other creative domains that incorporate designing, creating and constructing. Learners may have a variety of relevant skills and interests in areas such as woodworking, drawing, painting, and sewing that could offer starting points for designing with technology. However, creating dynamic environments that are responsive to a variety of learners remains a difficult challenge. The information in these projects could assist learners in self-identifying ways to engage with physical computing that capitalize on their current knowledge and scaffold learners to integrate this knowledge into their own physical computing projects. Algorithms and interfaces could glean, manipulate, and represent data from repositories of projects and resources in order to help learners in find, sort, and understand a spectrum of projects that are relevant to them. Not only would this help promote a diversity of learning opportunities but it would also help ease the burden on the educator if the learners were building with skills they already have.
The data gleaned from online projects could also inform how we approach teaching physical computing. Because physical computing is relatively young in terms of what we know about teaching it, there remains open questions concerning what is most important to learn. Analyzing data on the skills required to accomplish the variety of physical computing projects posted online, offers insight into the types and depth of skills that are useful for working with physical computing. For example, understanding what coding constructs are most prevalent in Arduino projects and what types of electronic components hobbyists use most often, could help prioritize learning objectives. It is important to acknowledge the biases that would exist in this sample of projects—i.e. a fairly homogeneous set of the population is empowered to work with physical computing and only small portion of them actually post. However, it provides a set of data grounded in projects from a subset of people who engage in self-directed learning experiences with physical computing. Exploring this data could provide new ways for beginners to playfully explore physical computing.
Integrating Electronic Feedback
The second area where an examination from the perspective of data could help develop avenues for playful learning is through how designers integrate feedback into the physical computing tools. Physical computing projects have proven to be difficult even for those who are experienced in creating with code and electronics (Booth et al., 2016). It is difficult for the learner to have agency over their projects if they are not able to problem solve efficiently as they build and explore. I advocate for learners to have visibility into the data that the electronic components are sending and receiving as they construct projects in context. For example, having visibility into wearable electronics as they are mounted to a dancer. By enabling learners to experiment more freely in context, we can facilitate playful interactions with the technology.
Numerous researchers have begun exploring the design space for supporting learners to overcome challenges in these types of computing projects. However, few have offered opportunities for learners to gain insight into the data while creating a semi-permanent solution. The majority of tools have focused on either black boxing much of the technology, allowing more seamless interactions but shielding learners from learning about the technology; or they have focused on providing data within the prototyping phase (ex. displaying voltages on a breadboard (Drew et al., 2016)), allowing the learner to have more insight into the technology but separating it from the implementation that makes it interesting. There is an open space for the design of tools that can help learners more quickly experiment with construction of their ideas while also having feedback into the electrical signals and components that make everything work. Specifically we need investigate where representations of data should exist, what forms these representations should take on, and how they should integrate into the learner’s processes. One step forward is through expanding the form factors available for engaging with physical computing while attending to how representations of data can integrate into these form factors. By continuing to understand how various types of data can be leveraged for learning opportunities in physical computing we can continue to develop avenues that not only support engaging with technology but support avenues for mastery of concepts that can empower learners to continue learning and creating technology personally valuable ways.
Blikstein, P. (2015). Computationally enhanced toolkits for children: Historical review and a framework for future design. Foundations and Trends® in Human–Computer Interaction, 9(1), pp. 1-68.
Booth, T., Stumpf, S., Bird, J., & Jones, S. (2016). Crossed wires: Investigating the problems of end-user developers in a physical computing task. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 3485-3497). ACM.
Drew, D., Newcomb, J. L., McGrath, W., Maksimovic, F., Mellis, D., & Hartmann, B. (2016). The toastboard: Ubiquitous instrumentation and automated checking of breadboarded circuits. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology, pp. 677-686. ACM.