Instructors

AI for Robots, SPCS 2017
Stanford Pre-Collegiate Studies, Summer Institutes 2017

Dr. Qin Chen has a Ph.D. in Electrical and Computer Engineering with a research focus on Robotics. Prior to her high tech career as a software engineer in Silicon Valley, she was on the engineering faculty at University of Redlands and taught electrical engineering classes for two years. For the past 10+ years, she has been working with students from the Bay Area high schools on all levels of math in classroom and private settings. As an instructor for Stanford Pre-Collegiate Studies, Dr. Chen teaches Artificial Intelligence for Robots to students in 11th-12th grade.

Dr. Zhu received his Ph.D. in Computer Science from Stanford University, with a focus on Artificial Intelligence. After 20+ years in the high tech industry, he returns to education as a co-founder of Pocket AI. He is also a lecturer at Stanford University and Stanford Pre-Collegiate Studies, as well as a USACO coach.

Philosophy

Robot piano keyboard in After-school class, Spring 2017
After-School Class, Spring 2017

Computer Science is not only about computers or programming languages, more generally, it is about learning a new way of thinking/problem solving - how to think like a computer scientist - a.k.a. Computational Thinking. Many online and offline coding classes focus on teaching the WHAT (“mechanics”, i.e., the syntax, of programming languages), but are not effective in teaching Computational Thinking. Without an understanding of HOW and WHY (problem solving skills), children don’t see the relevance of what’s taught in coding classes and forget what they have learned (the syntax) quickly. The challenges faced by Computer Science educators for young children are 1) concepts too abstract and 2) curriculum too boring to keep students engaged.

Dr. Chen believes that it is much more important to teach children the HOW and WHY than the WHAT of programming methodology. Her approach to teaching Computational Thinking and Artificial Intelligence uses a robot, a tangible object in the real world. Students will learn to create a brain for the robot step by step, which makes learning Computational Thinking intuitive and fun, and at the same time be exposed to basic concept of AI and Robotics.

Dr. Chen is an advocate for student-centered, discovery learning where students use information they already know to acquire more knowledge. In addition to programming the robot, she has integrated many popular paper-and-pencil and board games into her curriculum. Instead of learning programming syntax(the WHAT) and then trying to apply them to making a game, her students will start by playing a game that they are familiar with. They will then analyze the game from programmer’s perspective, identify the code components in the game and what they need to learn to build the components. This top-down, application driven approach is proven effective in keeping students motivated and enjoy learning.

Curriculum

Stanford Pre-Collegiate Studies, Summer Institutes, 2017

Co-developed with Dr. David Zhu, a Ph.D in Robotics and AI from Stanford University, the curriculum grew out of classes taught at Stanford University (CS123) and Summer Institutes of Stanford Pre-Collegiate Studies (Artificial Intelligence for Robots).

The multi-level project-based spiral curriculum is designed for 5-12th grade students, focusing on teaching problem solving and computational thinking, with an emphasis on being intuitive and fun. The introductory courses assume no prior knowledge of computer programming, while advanced courses cover theory and concepts of AI and robotics that are normally taught in college level courses. The curriculum is very hands-on and requires students to put what they learn into practice by writing programs to control a robot or building computer games