My goal as a teacher and researcher is to make CS more equitable. I try to do this through:
- Understanding and optimizing learning
- Identifying and removing barriers
My research is on Google Scholar or ACM Digital Library or check out a video about my research, or the selected publications below.
If you’re a teacher – Check out CSTeachingTips.org!
If you’re interested in the community here at UIUC – Check out our awesome grad students!
Research Area 1: K-12 CS Education
- Should kids learn CS? Yes! but maybe not for the reasons you think!
- Lewis, C. M. (2017). Good (and bad) reasons to teach all students computer science. In S. B. Fee, A. M. Holland-Minkley, & T. E. Lombardi, New Directions for Computing Education: Embedding Computing Across Disciplines. New York: Springer.
- Does Java experience in high school confer an advantage in college? It can! At UC Berkeley, Java experience led to students doing better in CS2 where Java was used (Note – Java was not used in CS0.5 and CS1). Without controlling for Java experience, it just looked like women did worse than men in CS2, but not in CS1 (which may be because of differential participation among men and women on AP CS A).
- Lewis, C. M., Titterton, N., & Clancy, M. (2012). Using Collaboration to Overcome Disparities in Java Experience. Proceedings of the International Computer Science Education Research Workshop. Auckland, NZ. 79-86.
- Does programming overlap with math? It can. Our 6th grade course did. Students’ 4th grade math scores correlated with their performance in our class. And we can see overlaps in the mathematical content tested in that 4th grade test and the math students could practice in our class.
- Lewis, C. M. & Shah, N. (2012). Building Upon and Enriching Grade Four Mathematics Standards with Programming Curriculum. ACM SIGCSE Bulletin. 43(1). 57-62.
- Should kids learn block-based or text-based programming? In a comparison of Logo and Scratch, students who learned Scratch did better on assessments of conditions, but students who learned Logo were more confident in their programming ability.
- Lewis, C. M. (2010). How programming environment shapes perception, learning and goals: Logo vs. Scratch, ACM SIGCSE Bulletin. 41(1), 346-350.
- Do kids know that they’re learning programming when they do Scratch? Probably not! Many students described screenshots of programming languages as “not programming” and the screenshot of the green characters from the movie the Matrix as “definitely programming.”
- Lewis, C. M., Bhattacharyya, V., Dominguez, N., Esper, S., Fa-Kaji, N., & Schlesinger, A. (2014). Children’s perception of what counts as a programming language. The Journal of Computing Sciences in Colleges. 29(4), 123-133.
- Should kids pair program? Maybe, maybe not. Much of the positive pair programming research compares pair programming to learning environments with zero collaboration! Of course pair programming is better there! I compare pair program to what I call “buddy programming” and didn’t replicate the positive results others had found. Also look at the equity work below!
- Lewis, C. M. (2011). Is pair programming more effective than other forms of collaboration for young students? Computer Science Education. 21(2). 105-134.
- Are high school CS teachers in California isolated (i.e., the only CS teacher at their school)? Yes! CS is one of the subjects with the greatest percentage of isolated teachers!
- Saffar Perez, M., & Lewis, C. M. (2024). Predictors of K-12 CS Teacher Isolation and Course Offerings. RESPECT 2024.
- Should we make a new curriculum repository for CS? Probably not. But if you do, here is some advice.
- Leake, M. & Lewis, C. M. (2017) Recommendations for Designing CS Resource Sharing Sites for All Teachers. ACM SIGCSE Bulletin. 48(1), 357-362.
Research Area 2: Equity in CS
- Why do we see patterns of underrepresentation in CS? This is reflective of systems of inequity that shape our narratives about CS and create barriers to accessing CS. This chapter summarizes how this works and applies the ideas to scenarios to try to provide practical strategies for educators.
- Lewis, C. M., Shah, N., & Falkner, K. (2019). Equity and diversity. In S. Fincher & A. Robins, The Cambridge Handbook of Computing Education Research. Cambridge University Press.
- Do high school students in California between 2004 and 2018 have equitable access to CS? No! Even controlling for school size, having a higher proportion of Black and Hispanic students at a school predicts that the school is less likely to offer CS.
- Saffar Perez, M., & Lewis, C. M. (2024). Predictors of K-12 CS Teacher Isolation and Course Offerings. RESPECT 2024.
- How can we create a CS department culture that support all students? Thank you for asking! Here are 12 tips and references to 40 other things you might want to read!
- Lewis, C. M. (2017). Twelve tips for creating a culture that supports all students in computing. ACM Inroads, 8(4), 17-20.
- How can we create CS department policies that supports all students? You should avoid using a competitive process to determine who gets to be a CS major.
- Nguyen, A., & Lewis, C. M. (2020). Competitive Enrollment Policies in Computing Departments Negatively Predict First-Year Students’ Sense of Belonging, Self-Efficacy, and Perception of Department. SIGCSE Bulletin (pp. 685-691). Best paper award.
- What could/should an equitable classroom look like? Here’s how we bring equity research/theory into the design of our classroom structures.
- Shah, N., Lewis, C. M., Caires, R., Khan, N., Qureshi, A., Ehsanipour, D., & Gupta, N. (2013). Building Equitable Computer Science Classrooms: Elements of a Teaching Approach. ACM SIGCSE Bulletin. 44(1). 263-268.
- How can we measure equity/inequity within pair programming? We looked at the distribution of talk between pairs as well as how often they asked each other questions (probably typically good for equity) and how often they issued their partner a command (probably typically bad for equity).
- Shah, N., Lewis, C. M., & Caires, R. (2014) Analyzing Equity in Collaborative Learning Situations: A Comparative Case Study in Elementary Computer Science. International Conferences of the Learning Sciences (ICLS). Nominated for best paper.
- Why might we see inequitable interaction within pair programming? Students might be pursuing very different goals. For example, one student trying to complete something as quickly as possible can be bad for equity.
- Lewis, C. M. & Shah, N. (2015). How Equity and Inequity Can Emerge in Pair Programming. Proceedings of the eleventh annual International Conference on International Computing Education Research (pp. 41-50).
- How can we measure equity/inequity within high school CS participation rates? Whenever possible we should compare participation rates in CS to the demographics of the educational context (e.g. the school, state, etc). We did that in this paper for AP CS participation in the US.
- Lim, K., & Lewis, C. M. (2020). Three Metrics of Success for High School CSforAll Initiatives: Demographic Patterns from 2003 to 2019 on Advanced Placement Computer Science Exams. SIGCSE Bulletin (pp. 598-604).
- Should we train TAs to be more equitable? Yes! Our paper describes some practices, drawn from education, to help people develop as educators!
- Lane, A., Mekonnen, R., Jang, C., Chen, P., & Lewis, C. M. (2021). Motivating Literature and Evaluation of the Teaching Practices Game: Preparing Teaching Assistants to Promote Inclusivity. ACM SIGCSE Proceedings. 52(1).
Research Area 3: Identity/Belonging in college-level CS
- Should educators be transparent (i.e., explicit) about the purpose of their instruction? Yes! We found that perceiving more transparency has a positive correlation with students’ self-efficacy and sense of belonging in computing while controlling for important confounding variables, such as prior CS experience. And this paper received a Best Paper Award Honorable Mention!
- Ojha, V., Watkins, A., Perdriau, C., Isenegger, K., & Lewis, C. M. (2024, August). Instructional Transparency: Just to Be Clear, It’s a Good Thing. In Proceedings of the 2024 ACM Conference on International Computing Education Research-Volume 1 (pp. 192-205). https://doi.org/10.1145/3632620.3671091
- Does a match between a students’ values of helping society and their perception of computing matter? Yes! A mismatch between a students’ goals of helping society and their perception of computing predicts a lower sense of belonging. And students from groups who – on average – are more likely to want to help society (women, Black students, Latine students, and first-generation college students), this may be particularly problematic! Our first paper (Lewis, et al., 2019) analyzed survey responses from over 7,000 students and our second paper (Isenegger, et al., 2023) analyzed data from over 45,000 students!
- Lewis, C. M., Bruno, P., Raygoza, J., & Wang, J. (2019). Alignment of Goals and Perceptions of Computing Predicts Students’ Sense of Belonging in Computing.Proceedings of the International Computer Science Education Research Workshop. Toronto, Canada.
- Isenegger, K., George, K. L., Bruno, P., & Lewis, C. M. (2023). Goal-Congruity Theory Predicts Students’ Sense of Belonging in Computing Across Racial/Ethnic Groups. ACM SIGCSE Proceedings. 54(1).
- Can we convince students that computing can be used to help society? Maybe! Students see that computing can help society (Isenegger, et al., 2021), but a strategy that worked in another discipline didn’t change students’ perceptions of computing’s ability to help society (Isenegger & Lewis, 2024).
- Isenegger, K., Birhane, Y., Ojha, V., Coxe, C., and Lewis, C. M. (2021). Understanding and Expanding College Students’ Perceptions of Computing’s Societal Impact. RESPECT Conference.
- Isenegger, K., & Lewis, C. M. (2024, October). Replicating a Goal-Congruity Intervention. 33rd Annual Rocky Mountain CCSC. Flagstaff, AZ.
- What shapes students’ interest in majoring in CS? Among our participants this centered around their ability, fit, enjoyment, utility, and opportunity cost. And in particular students’ perception of CS as requiring innate ability shaped their perception of their ability.
- Lewis, C. M., Yasuhara, K., & Anderson, R. E. (2011). Deciding to Major in Computer Science: A Grounded Theory of Students’ Self-Assessment of Ability. Proceedings of the International Computer Science Education Research Workshop. Providence, RI. 3-10.
- What shapes students’ feeling of fit with computing? Students varied in whether they thought particular characteristics of computer scientists (obsessed, male, competitive, asocial) as requirements or just patterns.
- Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016). “I don’t code all day”: Fitting in computer science when the stereotypes don’t fit. Proceedings of the International Computer Science Education Research Workshop. 23-32.
- Are there patterns in students’ confidence (i.e., self-efficacy) in computing education? Yes! Unsurprisingly, more computing experience positively predicts computing self-efficacy. “Analyzing over 31,000 student survey responses, we found that identifying as Asian, Black, Native, Hispanic, non-binary, and/or a woman were statistically significantly associated with lower computing self-efficacy.” This won a Best Paper Award!
- Ojha, V., West, L., & Lewis, C. M. (2024). Computing Self-Efficacy in Undergraduate Students: A Multi-Institutional and Intersectional Analysis. ACM SIGCSE Proceedings. https://doi.org/10.1145/3626252.3630811
- What might be pushing people away from AI and cybersecurity? Some perceptions of AI and cybersecurity “reinforce existing stereotypes about computing and may disproportionately affect the participation of students from groups historically underrepresented in computing.”
- Ojha, V., Perdriau, C., Lagesse, B., & Lewis, C. M. (2023). Computing Specializations: Perceptions of AI and Cybersecurity Among CS Students. ACM SIGCSE Proceedings. 54(1).
- How do students challenge the idea that some students are a “diversity hire”? Students attribute such comment to jealousy as well as ignorance about hiring practices and ignorance of the historical and current day context that justifies affirmative action programs.
- Perdriau, C., Ojha, V., Gray, K. T., Lagesse, B., & Lewis, C. M. (2024). The Diversity-Hire Narrative in CS: Sources, Impacts, and Mitigation Strategies. ACM SIGCSE Proceedings. Portland, OR. https://doi.org/10.1145/3568812.3603479
Research Area 4: CS Learning/Thinking
- What is a notional machine? It is a “a pedagogic device to assist the understanding of some aspect of programs or programming.” Just an explanation? Yep! It could involve spoken words, text, diagrams, physical objects, and/or simulations! Our paper has a bunch of examples and a review of previous work on Notional Machines!
- Fincher, S., Jeuring, J., Miller, C.S., Donaldson, P., du Boulay, B., Hauswirth, M., Hellas, A., Hermans, F., Lewis, C., Mühling, A., Pearce, J.L., & Peterson, A. (2020). Notional Machines in Computing Education: The Education of Attention. In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education (pp. 21-50).
- What can we learn from education research outside of CS? Lots! Read this chapter to see some important connections and open questions in connecting CS learning with math, CS, and logic!
- Lewis, C. M., Clancy, M., & Vahrenhold, J. (2019). Student knowledge and misconceptions. In S. Fincher & A. Robins, The Cambridge Handbook of Computing Education Research. Cambridge University Press.
- Does attempting (as researchers) to identify markers of expertise distract from our ability to understand and facilitate learning? I think probably! I think we should look at the potentially fruitful strategies students use to understand code and try to build on those. We should expect that awesome strategies will sometimes get students the wrong answer if they don’t have all the content knowledge they need.
- Lewis, C. M. (2023). Examples of Unsuccessful Use of Code Comprehension Strategies: A Resource for Developing Code Comprehension Pedagogy. International Computing Education Research (ICER) Conference. Chicago, IL. Honorable Mention, Best Paper Award
- Do students who appear to cheat on programming assignments get lower exam grades? Yes! You probably didn’t need the answer to this question, but maybe your students do!
- Chen, B., Lewis, C. M., West, M., & Zilles, C. (2024). Plagiarism in the Age of Generative AI: Cheating Method Change and Learning Loss in an Intro to CS Course. Learning at Scale 2024.
- How can I use stuffed animals to teach Java? Have I got the papers for you! Check out CSTeachingTips.org/3D
- Lewis, C. M. (2021). Physical Java Memory Models: A Notional Machine. ACM SIGCSE Proceedings. 52(1).
- Lewis, C. M., Hernandez, M., Kuo, A., McDowell, H., Roller, H. (2025). Experience Report: Physical Models of Java Inheritance. ACM SIGCSE Proceedings. 56(1). Pittsburg, PA.
- How do students debug programs? One part of that is learning to paying attention to the right part of the computer state. Want to hear the backstory of this paper? Check out Lewis (2019) below!
- Lewis, C. M. (2012). The Importance of Students’ Attention to Program State: A Case Study of Debugging Behavior. Proceedings of the International Computer Science Education Research Workshop. Auckland, NZ. 127-134.
- How do students trace recursive code? There are at least four specific strategies that might each have different affordances for students to understand, write, and trace recursive code.
- Lewis, C. M. (2014). Exploring variation in students’ correct traces of linear recursion. Proceedings of the International Computer Science Education Research Workshop. Glasgow, UK. 67-74.
- What makes Big-O hard for students? Logarithms in Big-O definitely seem to be a problem!
- Parker, M., & Lewis, C. M. (2014). What makes Big-O analysis difficult: Understanding how students understand runtime analysis. The Journal of Computing Sciences in Colleges. 29(4), 164-174.
Research Area 5: Qualitative Methods
- How do you write a paper about just 5-minutes of video of a student debugging? It is harder that it might seem – and this chapter tells the honest, retrospective backstory about how I wrote and revised Lewis (2012) over multiple years!
- Lewis, C. M. (2019). A Case Study of Qualitative Methods. In S. Fincher & A. Robins, The Cambridge Handbook of Computing Education Research. Cambridge University Press.