Kristin Stephens-Martinez

Assistant Professor of the Practice · Computer Science Department
D224 LSRC Research Drive Box 90129 · Duke University, NC 27708 · (919) 660-6581 · ksm@cs.duke.edu

I am an Assistant Professor of the Practice at Duke University in the Computer Science Department. I received my Ph.D. in Computer Science from UC Berkeley.

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.


Education

University of California, Berkeley

Doctor of Philosophy: Computer Science
Advisor: Armando Fox
December 2017

University of California, Berkeley

Master of Science: Computer Science
Advisor: Vern Paxson
December 2013

University of Maryland, College Park

Bachelor of Science: Computer Science
Summa Cum Laude
May 2009

Professional Appointments

Assistant Professor of the Practice

Duke University

The classes I teach focus on introductory computer science within the first two semesters.

December 2017 - Now

Co-Instructor

University of California, Berkeley

Co-taught CS194-25 Special topics: Building Your Next Generation Education Technologies with Dawn Song.

Fall 2012

Head Teaching Assistant

University of California, Berkeley

CS169 Software Engineering with Armando Fox.

Fall 2016

Graduate Teaching Assistant

University of California, Berkeley

Graduate Student Researcher

University of California, Berkeley

Wrong answers and Hints with Armando Fox

May-Aug 2016, Jan-May 2017

KnowMap with Dawn Song

May-Dec 2012

BGP Parser and HTTP Request Causation with Vern Paxson

Jan-Aug 2011, Jan-May 2012

Publications

Conferences

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.

Manuscripts and Unrefereed Reports

(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


Teaching

Duke University

CompSci 101 Introduction to Computer Science

Spring 2020; Fall 2019; Spring 2019; Fall 2018; 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.

CompSci 116 Foundations of Data Science

Fall 2019
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.

CompSci 249 CompSci Ed Research

Spring 2020; Fall 2019
(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.

University of California, Berkeley

CS194-25 Special topics: Building Your Next Generation Education Technologies

Fall 2012 (Co-taught with Dawn Song)
In this course we will explore today's online education landscape, learn and discuss what to consider when designing education tools, and contribute to a next generation online education technology.


Research

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.

Students

Master's
Ji Yeon Kim (2019)

Misc

Affiliations

Association for Computing Machinery (ACM)

2008 - Now

Special Interest Group on Computer Science Education (SIGCSE)

2018 - Now

Honors and Awards

Outstanding Graduate Student Instructor - UC Berkeley

National Science Foundation Graduate Research Fellowship

UC Berkeley Chancellor's fellowship

Personal

Hobby crafts blog