My research is in computer science education, both the study of learning computer science and applying computer science to education problems. My research focus is on scaling classes, such as how do we add more students to a class without sacrificing quality? I pursue this research by examining data from course tools to find interpretable data-driven insights that inform learning interventions.
I'm also the creator of The CS-Ed Podcast.
Below are highlights from my CV.
The classes I teach focus on introductory computer science within the first two semesters.
Wrong answers and Hints with Armando Fox
KnowMap with Dawn Song
BGP Parser and HTTP Request Causation with Vern Paxson
Kristin Stephens-Martinez, Armando Fox. 2018. Giving Hints is Complicated: Understanding the challenges of an automated hint system based on frequent wrong answers. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education 2018. ACM ITiCSE '18.
Kristin Stephens-Martinez, An Ju, Krishna Parashar, Regina Ongowarsito, Nikunj Jain, Sreesha Venkat, Armando Fox. 2017. Taking Advantage of Scale by Analyzing Frequent Constructed-Response, Code Tracing Wrong Answers ACM International Computing Education Research 2017. ACM ICER '17.
Kristin Stephens-Martinez, Marti A. Hearst, and Armando Fox. 2014. Monitoring MOOCs: Which Information Sources Do Instructors Value? ACM Learning At Scale 2014. ACM L@S '14.
(Ph.D. Thesis) K. Stephens-Martinez, Serving CS Formative Feedback on Assessments Using Simple and Practical Teacher-Bootstrapped Error Models, EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2017-166, Nov. 2017.
(Master's Report) Kristin Stephens. 2013. Towards Sound HTTP Request Causation Inference. EECS Department, University of California, Berkeley. UCB/EECS-2013-141
Spring 2018 (Co-taught with Owen Astrachan)
Introduction to practices and principles of computer science and programming and their impact on and potential to change the world. Algorithmic, problem-solving, and programming techniques in domains such as art, data visualization, mathematics, natural and social sciences. Programming using high-level languages and design techniques emphasizing abstraction, encapsulation, and problem decomposition. Design, implementation, testing, and analysis of algorithms and programs. No previous programming experience required.
Given data arising from some real-world phenomenon, how does one turn that data into knowledge and that knowledge into action? Students will learn critical concepts and skills in computer programming and statistical inference in the process of conducting analysis of real-world datasets. Students will write computer programs for projects using the Python programming language. In considering applications, we will discuss how data can be used responsibly to benefit society.
(Co-taught with Susan Rodger and Robert Duvall)
This is the computer science department’s undergrad TA training class. The goal of this class is to help you become an awesome TA. We believe that helping you become a good UTA will help your students learn and we believe that it is important to help you with this process. When it comes to teaching, no one is perfect. But no one can improve in a vacuum. It takes practice, acquiring new knowledge and skills, and a lot of reflection. The purpose of this class is to help you through that process and to prepare you to teach lab, run consulting hours, and support the faculty that teach in the department.
My research is in computer science education, specifically on how to scale learning. With many classes growing in size, we cannot ignore the gap between the supply of teachers and demand in terms of the number of students. I do not believe a computer can replace a teacher, but I do think computers can help this situation. How to support the teacher, student, and class are the research questions I am most interested in.
My research approach involves using mixed methods to analyze classroom data collected from class tools. I then apply the insights from this analysis to inform learning interventions.
If you are interested in working with me or collaborating, feel free to email me. For undergrad students, you might be most interested in Duke University's summer undergrad research program (CS+). I usually have at least one project in this program every summer.
Association for Computing Machinery (ACM)
Special Interest Group on Computer Science Education (SIGCSE)
Outstanding Graduate Student Instructor - UC Berkeley
National Science Foundation Graduate Research Fellowship
UC Berkeley Chancellor's fellowship