Beyond Access and Opportunity: Increasing Equity in Elementary CS Education

Framework

My research follows a culturally responsive-sustaining education framework, drawing heavily from the Kapor Center’s Framework for Culturally Relevant-Sustaining Computer Science Education and the National Academies’ Equity Framework. These perspectives emphasize the importance of integrating students’ diverse cultural backgrounds into instruction, recognizing and addressing systemic inequities, and using education as a tool for social justice.

The research also embodies the critical pedagogy perspective, which focuses on empowering students to understand and challenge societal power dynamics, particularly in technology. By embedding these frameworks into computer science education, the research explores how teachers can provide equitable access, foster positive identification with the discipline, and encourage students to use their skills to drive social change. This theoretical approach ensures that equity is not just about access, but also about transforming educational practices to be more inclusive, empowering, and relevant to all students' lives and experiences.

Methods

My research explores how elementary school teachers integrate equity into computer science instruction. The study follows a qualitative research design using semi-structured interviews and classroom observations as primary data sources. The goal is to understand teachers’ perceptions of equity and the strategies they use to provide inclusive computer science instruction.

Design:
Participants: I selected 20 elementary school teachers from diverse school settings (urban, suburban, rural) who actively teach computer science as part of their curriculum. Participants were chosen using purposive sampling to ensure a range of experiences with equity in education and teaching computer science. Criteria for selection included a balance of gender, race/ethnicity, and teaching experience.

Data Collection:

Interviews: Each participant participated in a 60-minute semi-structured interview. Questions focused on how they define and implement equity in computer science, how they address barriers faced by underrepresented students, and their strategies for fostering positive identification with the subject.

Data Sources:

Interview transcripts
Teacher-provided lesson plans and materials

Methods of Analysis:
I used thematic analysis to analyze the data. The process involved:

Coding: I manually coded interview transcripts and observation notes, focusing on recurring themes such as "access," "cultural relevance," "student empowerment," and "barriers to equity."
Identifying Patterns: Codes were grouped into categories, revealing patterns in how teachers conceptualize equity and implement strategies. For example, teachers often discussed differentiated instruction as a key approach to addressing learner variability.
Cross-Case Analysis: I conducted a cross-case analysis to compare how teachers in different school settings approached equity, identifying common strategies and unique challenges based on school demographics.

This detailed approach provides a comprehensive understanding of how teachers incorporate equity into elementary computer science education, with findings grounded in authentic classroom practices. The study can be replicated by following the outlined steps for participant selection, data collection, and analysis methods.

Results

The results of my research, though still in progress, are expected to reveal a range of strategies that elementary school teachers use to integrate equity into computer science education. Based on preliminary data, several key themes are emerging:

1. Equity as Access and Opportunity:
Many teachers emphasize equitable access to technology and computer science content, often using differentiated instruction and scaffolding to ensure all students, regardless of their prior experience, can engage in coding and computational thinking. For example, teachers frequently offer alternative entry points into lessons based on students' skill levels.

2. Positive Identification with Computer Science:
Teachers are actively working to foster students’ identification with computer science as a discipline, particularly for underrepresented students. This involves highlighting diverse role models in tech, encouraging collaboration, and designing culturally relevant activities that resonate with students’ backgrounds and interests.

3. Addressing Power Dynamics in Technology:
A surprising and insightful theme is the intentional use of computer science as a tool to discuss social justice issues and power dynamics in technology. Several teachers are integrating lessons on AI bias, ethical hacking, and the role of technology in shaping society. This strategy not only teaches technical skills but also empowers students to critically engage with how technology affects marginalized communities.

4. Barriers and Challenges:
Teachers report facing institutional barriers, such as limited resources, lack of professional development, and systemic inequities that prevent full implementation of equitable practices. These barriers, while significant, are often addressed through teacher creativity and community-building within schools, where teachers seek support from peers to improve equity.

5. Expanding Cultural Perspectives:
Another emerging result is the intentional integration of culturally responsive teaching within computer science lessons. Teachers are developing assignments that connect coding to students’ lived experiences, whether through projects that address local community issues or storytelling through programming that reflects students' cultural backgrounds.

Importance

The results of my research, though still in progress, are expected to reveal a range of strategies that elementary school teachers use to integrate equity into computer science education. Based on preliminary data, several key themes are emerging:

1. Equity as Access and Opportunity:
Many teachers emphasize equitable access to technology and computer science content, often using differentiated instruction and scaffolding to ensure all students, regardless of their prior experience, can engage in coding and computational thinking. For example, teachers frequently offer alternative entry points into lessons based on students' skill levels.

2. Positive Identification with Computer Science:
Teachers are actively working to foster students’ identification with computer science as a discipline, particularly for underrepresented students. This involves highlighting diverse role models in tech, encouraging collaboration, and designing culturally relevant activities that resonate with students’ backgrounds and interests.

3. Addressing Power Dynamics in Technology:
A surprising and insightful theme is the intentional use of computer science as a tool to discuss social justice issues and power dynamics in technology. Several teachers are integrating lessons on AI bias, ethical hacking, and the role of technology in shaping society. This strategy not only teaches technical skills but also empowers students to critically engage with how technology affects marginalized communities.

4. Barriers and Challenges:
Teachers report facing institutional barriers, such as limited resources, lack of professional development, and systemic inequities that prevent full implementation of equitable practices. These barriers, while significant, are often addressed through teacher creativity and community-building within schools, where teachers seek support from peers to improve equity.

5. Expanding Cultural Perspectives:
Another emerging result is the intentional integration of culturally responsive teaching within computer science lessons. Teachers are developing assignments that connect coding to students’ lived experiences, whether through projects that address local community issues or storytelling through programming that reflects students' cultural backgrounds.

References

Asgari, S., Dasgupta, N., & Stout, J. G. (2012). When Do Counterstereotypic Ingroup Members Inspire Versus Deflate? The Effect of Successful Professional Women on Young Women’s Leadership Self-Concept. Personality and Social Psychology Bulletin, 38(3), 370–383. https://doi.org/10.1177/0146167211431968
Banilower, E., Smith, P. S., Malzahn, K., Plumley, C., Gordon, E., & Hayes, M. (2018). Report of the 2018 NSSME+. 442.
Bathgate, M., & Schunn, C. (2017). The psychological characteristics of experiences that influence science motivation and content knowledge. International Journal of Science Education, 39(17), 2402–2432. https://doi.org/10.1080/09500693.2017.1386807
Bers, M. U. (n.d.). Http://ecrp.uiuc.edu/v12n2/bers.html. 20.
Bullard, R. (1983). “Solid Waste Sites and the Black Houston Community”, Sociological Inquiry, 53, pp. 273-88. In Environmental Justice (pp. 25–40). Routledge.
Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, 44(8), 1187–1218. https://doi.org/10.1002/tea.20237
Charleston, L. J., George, P. L., Jackson, J. F. L., Berhanu, J., & Amechi, M. H. (2014). Navigating underrepresented STEM spaces: Experiences of Black women in U.S. computing science higher education programs who actualize success. Journal of Diversity in Higher Education, 7(3), 166–176. https://doi.org/10.1037/a0036632
Cheryan, S., Master, A., & Meltzoff, A. N. (2015). Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00049
Coenraad, M., Mills, K., Byrne, V. L., & Ketelhut, D. J. (2020). Supporting Teachers to Integrate Computational Thinking Equitably. 2020 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 1, 1–2. https://doi.org/10.1109/RESPECT49803.2020.9272488
College Board AP Program Summary Report, 2018 Calculus AB & BC, Computer Science A, Computer Science Principles (p. 1). (n.d.).
Computer and Information Technology Occupations: Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics. (n.d.). Retrieved November 10, 2019, from https://www.bls.gov/ooh/computer-and-information-technology/home.htm
Darling-Hammond, L. (2006). Constructing 21st-Century Teacher Education. Journal of Teacher Education, 57(3), 300–314. https://doi.org/10.1177/0022487105285962
Duncan, C., Bell, T., & Atlas, J. (2017). What do the Teachers Think?: Introducing Computational Thinking in the Primary School Curriculum. Proceedings of the Nineteenth Australasian Computing Education Conference on - ACE ’17, 65–74. https://doi.org/10.1145/3013499.3013506
Dwyer, H. A., Boe, B., Hill, C., Franklin, D., & Harlow, D. B. (2013). Computational Thinking for Physics: Programming Models of Physics Phenomenon in Elementary School. 133–136. https://www.compadre.org/per/items/detail.cfm?ID=13126
Eglash, R., Bennett, A., O’donnell, C., Jennings, S., & Cintorino, M. (2006). Culturally Situated Design Tools: Ethnocomputing from Field Site to Classroom. American Anthropologist, 108(2), 347–362. https://doi.org/10.1525/aa.2006.108.2.347
Eglash, R., Gilbert, J. E., Taylor, V., & Geier, S. R. (2013). Culturally Responsive Computing in Urban, After-School Contexts: Two Approaches. Urban Education, 48(5), 629–656. https://doi.org/10.1177/0042085913499211
Ehsan, H., Rehmat, A. P., & Cardella, M. E. (2020). Computational thinking embedded in engineering design: Capturing computational thinking of children in an informal engineering design activity. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-020-09562-5
Falkner, K., Vivian, R., & Williams, S.-A. (2018). An ecosystem approach to teacher professional development within computer science. Computer Science Education, 28(4), 303–344. https://doi.org/10.1080/08993408.2018.1522858
Franklin, D., Conrad, P., Aldana, G., & Hough, S. (n.d.). Animal tlatoque: Attracting middle school students to computing through culturally-relevant themes. In SIGCSE ’11.
Garvin, M., Killen, H., Plane, J., & Weintrop, D. (2019). Primary School Teachers’ Conceptions of Computational Thinking. Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 899–905. https://doi.org/10.1145/3287324.3287376
Gay, G. (n.d.). Culturally Responsive Teaching 9780807758762 | Teachers College Press. Retrieved March 23, 2022, from https://www.tcpress.com/culturally-responsive-teaching-9780807758762
Gomez, K., Lee, U.-S., & Amy, B. W. (2022). The Role of Teacher Beliefs, Goals, Knowledge, and Practices in Shaping Computer Science Education: Teacher Participation and Learning in the Codesign of Elementary Computer Science Curriculum. Teacher Learning in Changing Contexts: Perspectives from the Learning Sciences.
González-Patiño, J., & Esteban-Guitart, M. (2019). Affinity online: How connection and shared interest fuel learning. Mind, Culture, and Activity, 26(4), 383–386. https://doi.org/10.1080/10749039.2019.1680695
Goode, J., Chapman, G., & Margolis, J. (2012). Beyond curriculum: The exploring computer science program. ACM Inroads, 3(2), 47–53. https://doi.org/10.1145/2189835.2189851
Goode, J., & Margolis, J. (2011). Exploring Computer Science: A Case Study of School Reform. ACM Transactions on Computing Education, 11(2), 12:1-12:16. https://doi.org/10.1145/1993069.1993076
Gorski, P. C. (2009). Insisting on Digital Equity: Reframing the Dominant Discourse on Multicultural Education and Technology. Urban Education, 44(3), 348–364. https://doi.org/10.1177/0042085908318712
Grover, S., & Pea, R. (2013). Computational Thinking in K–12: A Review of the State of the Field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Gürbüz, H., Evlioğlu, B., Erol, Ç. S., Gülseçen, H., & Gülseçen, S. (2017). “What’s the Weather Like Today?”: A computer game to develop algorithmic thinking and problem solving skills of primary school pupils. Education and Information Technologies, 22(3), 1133–1147. https://doi.org/10.1007/s10639-016-9478-9
Hidi, S., & Renninger, K. A. (2006). The Four-Phase Model of Interest Development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep4102_4
Hiebert, J., Morris, A. K., Berk, D., & Jansen, A. (2007a). Preparing Teachers to Learn from Teaching. Journal of Teacher Education, 58(1), 47–61. https://doi.org/10.1177/0022487106295726
Hiebert, J., Morris, A. K., Berk, D., & Jansen, A. (2007b). Preparing Teachers to Learn from Teaching. Journal of Teacher Education, 58(1), 47–61. https://doi.org/10.1177/0022487106295726
How Vocational Education Got a 21st Century Reboot—POLITICO Magazine. (n.d.). Retrieved November 10, 2019, from https://www.politico.com/magazine/story/2019/09/12/vocational-tech-school-training-policy-228049
Howard, N. R. (2019). EdTech Leaders’ Beliefs: How are K-5 Teachers Supported with the Integration of Computer Science in K-5 Classrooms? Technology, Knowledge and Learning, 24(2), 203–217. https://doi.org/10.1007/s10758-018-9371-2
Hynes, M. M., Moore, T. J., Cardella, M. E., Tank, K. M., Purzer, S., Menekse, M., & Brophy, S. P. (2016, June 26). Inspiring Computational Thinking in Young Children’s Engineering Design Activities (Fundamental). 2016 ASEE Annual Conference & Exposition. https://peer.asee.org/inspiring-computational-thinking-in-young-children-s-engineering-design-activities-fundamental
Israel, M., & Lash, T. (2020). From classroom lessons to exploratory learning progressions: Mathematics + computational thinking. Interactive Learning Environments, 28(3), 362–382. https://doi.org/10.1080/10494820.2019.1674879
Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers and Education, 82, 263–279. https://doi.org/10.1016/j.compedu.2014.11.022
Jones, B. D., Ruff, C., & Paretti, M. C. (2013). The impact of engineering identification and stereotypes on undergraduate women’s achievement and persistence in engineering. Social Psychology of Education, 16(3), 471–493. https://doi.org/10.1007/s11218-013-9222-x
Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200–210. https://doi.org/10.1016/j.chb.2015.05.047
Kapor Center. (2021). EquitableCS. Kapor Center. https://www.kaporcenter.org/equitablecs/
Kemph, J. P. (1969). Erik H. Erikson. Identity, youth and crisis. New York: W. W. Norton Company, 1968. Behavioral Science, 14(2), 154–159. https://doi.org/10.1002/bs.3830140209
Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2020). Teacher Change Following a Professional Development Experience in Integrating Computational Thinking into Elementary Science. Journal of Science Education and Technology, 29(1), 174–188. https://doi.org/10.1007/s10956-019-09798-4
Kong, S., Chiu, M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127. https://doi.org/10.1016/j.compedu.2018.08.026
Legewie, J., & DiPrete, T. A. (2014). The High School Environment and the Gender Gap in Science and Engineering. Sociology of Education, 87(4), 259–280. JSTOR.
Leonard, A. E., Dsouza, N., Babu, S. V., Daily, S. B., Jörg, S., Waddell, C., Parmar, D., Gundersen, K., Gestring, J., & Boggs, K. (2015). Embodying and Programming a Constellation of. 11(2), 30.
Lim, M., & Barton, A. C. (2006). Science learning and a Sense of Place in a Urban Middle School. Cultural Studies of Science Education, 1(1), 107–142. https://doi.org/10.1007/s11422-005-9002-9
Luo, F., Antonenko, P. D., & Davis, E. C. (2020). Exploring the evolution of two girls’ conceptions and practices in computational thinking in science. Computers & Education, 146, 103759. https://doi.org/10.1016/j.compedu.2019.103759
Margolis, J. (n.d.). Stuck in the Shallow End | The MIT Press. The MIT Press. Retrieved November 22, 2020, from https://mitpress.mit.edu/books/stuck-shallow-end
Margolis, J., & Fisher, A. (2002). Unlocking the Clubhouse | The MIT Press. The MIT Press. https://mitpress.mit.edu/books/unlocking-clubhouse
Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424–437. https://doi.org/10.1037/edu0000061
Master, A., Cheryan, S., Moscatelli, A., & Meltzoff, A. N. (2017). Programming experience promotes higher STEM motivation among first-grade girls. Journal of Experimental Child Psychology, 160, 92–106. https://doi.org/10.1016/j.jecp.2017.03.013
McLoughlin, C. (1999). Culturally responsive technology use: Developing an on-line community of learners. British Journal of Educational Technology, 30(3), 231–243. https://doi.org/10.1111/1467-8535.00112
Menekse, M. (2015). Computer science teacher professional development in the United States: A review of studies published between 2004 and 2014. Computer Science Education, 25(4), 325–350. https://doi.org/10.1080/08993408.2015.1111645
Migus, L. H. (n.d.). Broadening Access to STEM Learning through Out-of-School Learning Environments. 17.
Naphan‐Kingery, D. E., Miles, M., Brockman, A., McKane, R., Botchway, P., & McGee, E. (2019). Investigation of an equity ethic in engineering and computing doctoral students. Journal of Engineering Education, 108(3), 337–354. https://doi.org/10.1002/jee.20284
National Academies of Sciences, E. (2021). Cultivating Interest and Competencies in Computing: Authentic Experiences and Design Factors. https://doi.org/10.17226/25912
National Academies of Sciences, E., and Medicine. (2018). Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments. The National Academies Press. https://doi.org/10.17226/24926
Rich, K. M., Yadav, A., & Larimore, R. A. (2020). Teacher implementation profiles for integrating computational thinking into elementary mathematics and science instruction. Education and Information Technologies, 25(4), 3161–3188. https://doi.org/10.1007/s10639-020-10115-5
Rodriguez, S. L., & Lehman, K. (2017). Developing the next generation of diverse computer scientists: The need for enhanced, intersectional computing identity theory. Computer Science Education, 27(3–4), 229–247. https://doi.org/10.1080/08993408.2018.1457899
Ryoo, J. (n.d.). Pedagogy that Supports Computer Science for All. ACM Transactions on Computing Education, 19(4), 1–23.
Santo, R., DeLyser, L. A., Ahn, J., Pellicone, A., Aguiar, J., & Wortel-London, S. (2019). Equity in the Who, How and What of Computer Science Education: K12 School District Conceptualizations of Equity in ‘CS for All’ Initiatives. 2019 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 1–8. https://doi.org/10.1109/RESPECT46404.2019.8985901
Scott, K. A., Sheridan, K. M., & Clark, K. (2015). Culturally responsive computing: A theory revisited. Learning, Media and Technology, 40(4), 412–436. https://doi.org/10.1080/17439884.2014.924966
Searle, K. A., & Kafai, Y. B. (2015). Boys’ Needlework: Understanding Gendered and Indigenous Perspectives on Computing and Crafting with Electronic Textiles. Proceedings of the Eleventh Annual International Conference on International Computing Education Research, 31–39. https://doi.org/10.1145/2787622.2787724
Sengupta, P., & Farris, A. V. (2012). Learning kinematics in elementary grades using agent-based computational modeling: A visual programming-based approach. Proceedings of the 11th International Conference on Interaction Design and Children, 78–87. https://doi.org/10.1145/2307096.2307106
Stets, J. E., Brenner, P. S., Burke, P. J., & Serpe, R. T. (2017). The Science Identity and Entering a Science Occupation. Social Science Research, 64, 1–14. https://doi.org/10.1016/j.ssresearch.2016.10.016
Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26(1), 3–20. https://doi.org/10.1007/s10798-015-9304-5
Terry, L. (2011). Mathematical counterstory and African American male students: Urban mathematics education from a critical race theory perspective. Journal of Urban Mathematics Education, 4(1), 23–49.
The Integrated Postsecondary Education Data System. (n.d.). Retrieved November 10, 2019, from https://nces.ed.gov/ipeds/
Toom, A. (2017). Teachers’ Professional and Pedagogical Competencies: A Complex Divide between Teacher Work, Teacher Knowledge and Teacher Education. In D. Clandinin & J. Husu, The SAGE Handbook of Research on Teacher Education (pp. 803–819). SAGE Publications Ltd. https://doi.org/10.4135/9781526402042.n46
Vogel, S., Santo, R., & Ching, D. (2017). Visions of Computer Science Education: Unpacking Arguments for and Projected Impacts of CS4All Initiatives. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, 609–614. https://doi.org/10.1145/3017680.3017755
Warped Reality: Joy Buolamwini: How Do Biased Algorithms Damage Marginalized Communities? (2021). In TED Radio Hour (Podcast). NPR.
Waterman, K. P., Goldsmith, L., & Pasquale, M. (2020). Integrating Computational Thinking into Elementary Science Curriculum: An Examination of Activities that Support Students’ Computational Thinking in the Service of Disciplinary Learning. Journal of Science Education and Technology, 29(1), 53–64. https://doi.org/10.1007/s10956-019-09801-y
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Yadav, A., Krist, C., Good, J., & Caeli, E. N. (2018). Computational thinking in elementary classrooms: Measuring teacher understanding of computational ideas for teaching science. Computer Science Education. https://www.tandfonline.com/doi/abs/10.1080/08993408.2018.1560550