Using Smartphone Sensors to Teach Engineering Problem Solving
Anthony Thomas
Description
I describe efforts to redesign the curriculum of ENG6, a large introductory undergraduate course on engineering problem solving and programming. Our efforts have focused on developing a new set of lab curriculum centered on data analysis tasks using measurements collected by students using native sensors on their smartphones (e.g. accelerometers). These activities serve to concretize programming and problem solving concepts through physically intuitive experiments that connect to core topics from students' introductory math and physics courses like calculus and motion. The interactive nature of the labs and the innate complexity of real measurements require students to engage with the structure and intent of their code, even when AI is used in its production. A preliminary analysis indicates positive impacts on students' perceived self-knowledge in problem solving, programming, and AI literacy.
Link to the video [video.ucdavis.edu]
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About the Presenter
I am an assistant professor of teaching in the electrical and computer engineering department at UC Davis. I am broadly interested in algorithmic aspects of data science and learning under constraints on computational resources like memory, precision, and network communication. I am particularly interested in exploiting randomness to develop algorithms that have formal guarantees of correctness while lending themselves to realization in highly-parallel and noisy hardware. Prior to joining UC Davis, I was a postdoctoral scholar in the Redwood Center for Theoretical Neuroscience at UC Berkeley. I received my PhD in Computer Science from UC San Diego in September 2023, where I was supervised by Sanjoy Dasgupta and Tajana Rosing.
