I work on human-computer interaction (HCI) that helps people solve problems.
I am a current Ph.D. student in Cognitive Science at the UCSD Design Lab studying human-computer interaction (HCI). I am interested in how we can use HCI to both improve and understand the cognitive processes that learners use for solving problems that are new to them. I am advised by Prof. Leo Porter.
Large language models (LLMs) are being used increasingly in computer science in general, and classrooms in particular. Plugins such as Github Copilot leverage LLMs and provide code suggestions in real time, and it is unclear how this impacts students' learning of fundamental computing principles. Our objective is to compare student outcomes in an LLM-augmented introductory computer science class (CS1-LLM) as compared with a traditional introductory computer science class (CS1), specifically examining differential outcomes across demographic backgrounds. We are developing a novel introductory CS course led by Prof. Leo Porter, UCSD, and Prof. Dan Zingaro, University of Toronto. A key component of CS1-LLM is its instruction on the use of LLMs (specifically Github Copilot) to help write effective code, including guidance with the basics of prompt engineering. 
How do people algorithmically tackle hard problems and develop generalizable principles from individual problem instances? When faced with a novel situation or task, many humans are able to formulate abstract principles that are useful to guide problem solving. The strength of this ability varies between individuals. Previous studies have investigated the role of self-explanation in understanding a novel task or phenomenon. One aspect of self-explanation that has been identified as a possible source of utility is the learner's generation of an explicit abstract principle that can then be applied to future situations or problems. I am interested in each of these factors (self explanation and reasoning experience) separately, as well as their interaction. Existing research on self-explanation has shown its importance, but the question of how exactly it helps in understanding remains open, with explicit abstract principle generation being one possible way that it contributes to understanding. My research questions are: 
The need for such lightweight interventions closely mirrors a problem I encountered as an educator prior to my experience as a graduate student. As a co-founder of Transform Tutoring, a company that provides one-on-one tutoring for high school students, I led the interview process for STEM tutors. I developed a required pre-interview reading for candidates which included an explanation and example of student-driven teaching--that is, allowing students to find the next step of a problem on their own rather than providing it to them. My student-driven teaching and learning guide yielded much better interviews, and illustrated to me the promise of lightweight teaching interventions. As a graduate student, I adapted this guide to create an Introduction to Python TA Training which I have used to train several new TAs and tutors for our introductory programming course. As I continue to develop these trainings along with teaching professors in my department, we will aim to answer the following questions:
RQ1: There are many well-established teaching principles in pedagogical literature. Which ones are suited to lightweight teaching interventions that could be effectively taught to TAs and tutors without prior teaching training?Feel free to contact me with any questions.
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