We will be presenting our Embodied Code project at CHI’22 as part of the Interactions program. In both online and in-person formats, we will demo the Embodied Coding Environment, and take participants through a short (5 minute) experience with the embodied coding system.
Exploring Virtual Reality and Embodied Computational Reasoning
A workshop for ICER 2021, the ACM International Computing Education Research conference.
Date: Saturday, August 14, 11:00AM – 1PM PDT
Description: The increasing sophistication and availability of Augmented and Virtual Reality (AR/VR) technologies wield the potential to transform how we teach and learn computational concepts and coding. This workshop examines how AR/VR can be leveraged in computer science (CS) education within the context of embodied learning. It has been theorized that abstract computational concepts, such as data, operators, and loops, are grounded in embodied representations that arise from our sensorimotor experience of the physical world. For instance, researchers have shown that when CS students describe algorithms, conditionals, and other computational structures, they frequently gesture in ways that suggest they are conceptualizing interactions with tangible objects. Can learning to code become a more intuitive process if lessons take into account these types of embodied conceptual phenomena? This two-hour workshop explores 1) theories of embodiment and 2) new and existing tools and practices that support embodied CS learning — ranging from Papert’s LOGO turtles to a preview of an innovative 3D spatial coding platform for AR/VR under development by our group. Other open-source and commercially available resources will also be examined through streamed video demos and a hands-on break-out session for participants.
Together with Prof. Karcher Morris and Postdoctoral scholar Jon Paden, we have been awarded a $45,000 grant from the UC San Diego Course Development and Instructional Improvement Program (CDIIP) to develop and pilot imagination for engineers within STEM curricula. This builds on work I have done as a lecturer in Data Science, Electrical and Computer Engineering/ML for the Arts, bridging cultivate of human imagination within STEM education, and focused on imagination as a driver of engagement, retention, and broadening the scope of STEM disciplines. The modules and resources we develop (and publish) will be shaped with an eye towards broad applicability across diverse educational fields.
Description: With recent advancements in machine learning techniques, researchers have demonstrated remarkable achievements in image synthesis (BigGAN, StyleGAN), textual understanding (GPT-3), and other areas of text and image manipulation. This hands-on workshop introduces state-of-the-art techniques for text-to-image translation, where textual prompts are used to guide the generation of visual imagery. Participants will gain experience with Open AI’s CLIP network and Google’s BigGAN, using free Google Colab notebooks which they can apply to their own work after the event. We will discuss other relationships between text and image in art and literature; consider the strengths and limitations of these new techniques; and relate these computational processes to human language, perception, and visual expression and imagination. Please bring a text you would like to experiment with!
I’m excited that our workshop, Measuring Computational Creativity: Collaboratively Designing Metrics to Evaluating Creative Machines will be featured at ISEA2020 – Why Sentience? in Montreal in October. Eunsu Kang, Jean Oh, and I, together with ISEA participants, will develop metrics to assess computational creativity. We will address questions including:
How do we make a creative machine? Creativity is not a sudden burst out of blank space. It involves “a multitude of definitions, conceptualizations, domains, disciplines that bear on its study, empirical methods, and levels of analysis, as well as research orientations that are both basic and applied – and applied in varied contexts.” From Newell, Shaw, and Simon’s insights on computational creativity to Boden’s definitions such as combinational creativity, exploratory creativity, and transformational creativity, defining what kind of creativity, which is appropriate for the specific task of a machine, would be a sensible first step to build a creative algorithm/machine. Yet some questions remain. Can we computationally model ambiguity? Would a novelty search result in valuable discoveries? Where is the threshold between randomness and creativity? Last but not least, how do we evaluate the creativity of an algorithm? This workshop is a first attempt to establish evaluation metrics assessing computational creativity in our current international Arts and Machine Learning (ML) research renaissance.