(she/her/hers)
Massachusetts Institute of Technology
Human-computer interaction, Skill-learning, Adaptive learning, Game-based learning, Makerskills
Dishita Turakhia is a Ph.D. candidate in the EECS dept. at the Massachusetts Institute of Technology, where she is advised by Prof. Stefanie Mueller (HCIE group, MIT CSAIL) and Prof. Kayla DesPortes (NYU).
Her current research lies at the intersection of system design and learning sciences, and in her PhD., she builds systems for autodidactic learning of skills, such as motor skills, fabrication skills, and makerskills.
She is the recipient of the Meta (Facebook) Ph.D. Research Fellowship and the MIT Edwin S. Webster Graduate Fellowship. Her research is supported by several grants, including the National Science Foundation and the MIT Integrated Learning Initiative. She is also a MIT SERC scholar, a Grace Hopper Conference scholar, and a TEDx speaker.
Before starting her Ph.D in EECS at MIT, she completed a dual master's degree in EECS (MS) and Architecture (SMArchS computation) at MIT in 2017, and a master's degree in Design & Technology (EmTech) at the Architectural Association in 2011.
She is also currently a research scientist intern at the Meta Reality Labs. Besides academic research, she has industry experience as a computational designer in London, Singapore, and Bern, and as a licensed architect in Mumbai, where she co-led her architecture design firm, ArchitextureBuro. Outside of work, she is passionate about traveling and has visited over 40 countries.
Autodidactic Skill-learning: Reimagining Tools to Support Learning of Hands-on Skills
With advances in enabling technologies, such as sensing, computer vision, and AR/VR, new opportunities have emerged to design skill-learning systems, that lower the entry barrier for beginners and support novices in learning hands-on creative skills, such as motor skills, fabrication, circuit prototyping, and design. However, HCI researchers have pointed out that these systems can be improved to support learning by (re)centering their design around the learner, the instructor, and the learning processes instead of centering them around their enabling technologies.
In my research, I contribute to this body of work by designing tools that are centered around the learner's skills and self-learning process. In particular, my systems support autodidactic learning, i.e. learning by oneself. This work lies at the intersection of system design, learning sciences, and technologies that support physical skill-learning.
I present three sets of research projects - (1) adaptive learning of motor skills, (2) game-based learning for fabrication skills, and (3) reflection-based learning of maker skills. Through these projects, I demonstrate how we can leverage existing theories, frameworks, and approaches from the learning sciences to design autodidactic systems for skill-learning, and reimagine the learning of the future.