Not Everyone Has Access: How Teachers’ Computer Science Goals Relate to Equity

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Poster presentation

Dr. Melissa Toohey  
While educators, community members and the tech industry understand the need for a robust and diverse pipeline to fill future roles, professionally authentic experiences are the main academic focus for CS instruction. In this session, participants will examine research and collaborate to plan personally authentic computing experiences for their students.

Audience: Principals/head teachers, Teachers, Technology coordinators/facilitators
Attendee devices: Devices not needed
Topic: Computer science & computational thinking
Grade level: PK-5
Subject area: Computer science
ISTE Standards: For Coaches:
Learning Designer
  • Collaborate with educators to design accessible and active digital learning environments that accommodate learner variability.
For Educators:
  • Use technology to create, adapt and personalize learning experiences that foster independent learning and accommodate learner differences and needs.
  • Design authentic learning activities that align with content area standards and use digital tools and resources to maximize active, deep learning.
Additional detail: Graduate student
Related exhibitors:
Seesaw Learning

Proposal summary


Teachers are slowly becoming required to provide computer science instruction at elementary level, yet many are unprepared. If the goal of providing computer science instruction at younger ages is to garner and sustain interest to fill the pipeline with more candidates, providing meaningful and relevant computer science opportunities may be one way to increase interest and participation of underrepresented students. According to Scott, Sheridan, and Clark, (2015), in a review of over 50 computing programs, the vast majority focus on technical skills, and none mention issues of diversity, community, culture, or identity. Culturally Responsive Pedagogy (CRP) and Culturally Responsive Computing (CRC) provide frameworks for educators to guide their teaching, and their students’ learning.
CRC is closely related to CRP, as it draws upon the extensive work of CRP (Scott, Sheridan, and Clark, 2015). Culturally responsive pedagogical strategies can be applied to make technology education accessible to underrepresented populations by using asset-based approaches (Scott, Sheridan, and Clark, 2015). CRP is a pedagogical strategy that engages diverse youth and stands in stark contrast to traditional deficit models of thinking that fault students’ personhood, communities, backgrounds, and families (Scott, Sheridan, and Clark, 2015). CRP focuses on these factors as assets, and teachers generally employ CRP by using asset building approaches that include opportunities for reflection and connection (Scott, Sheridan, and Clark, 2015).
According to Scott, Sheridan, and Clark, (2015), culturally competent educators, “develop and openly demonstrate their own cultural competency about students’ identities, use this knowledge as the foundation on which to build lessons, develop meaningful and sustainable relationships with students predicated on the notion that they will succeed, and maintain a heightened sensitivity to the school’s sociopolitical context as a place that can emancipate or oppress”. Culturally responsive educators connect to their students in non-traditional ways (Scott, Sheridan, and Clark, 2015). Studies have suggested that CRP can improve black students’ self-image and self concept that are diminished in dominant culture (Ladson-Billings, 1994) CRP, and it’s computing-related framework, CRC, offer an approach to provide inclusive and meaningful computer science opportunities for underrepresented students.
The goals of CRC include the tenets of CRP but apply them to technology education. The focus of CRC includes:
“Motivate and improve science, technology, engineering, and math (STEM) learning experiences;
Provide a deeper understanding of heritage and vernacular culture, empowerment for social critique, and appreciation for cultural diversity;
Bring 1 and 2 together: to diminish the separation between the worlds of culture and STEM;” (Scott, Sheridan, and Clark, 2015)
And, “...This technology must not only respond to these identity issues, but also satisfy pedagogical demands of the curriculum” (Eglash et al. 2013).
The goals of CRC are framed to address the persistent gap in historically marginalized groups' participation and access to technology to increase the focus on technology education and the workforce. CRC teaching strategies can include peer-teaching, participatory tasks, clearly stated outcomes, and discourse involving problem-based activities and include four design principles: prior knowledge, cultural ways of knowing, engagement and motivation, and civic and social empowerment (Scott, Sheridan, and Clark, 2015). In addition, CRC emphasizes educators’ needs to reflect on their own identities, cultural backgrounds, and motivations (Scott, Sheridan, and Clark, 2015). By offering opportunities for educators to integrate CRP and CRC into their teaching practices, students may have more long term sustainment and interest in computer science opportunities. CRC will serve as the theoretical framework for this study.
In summary, the United States is facing a crisis in which the number of projected computing roles quickly outpaces the number of graduates to fill them. This economic and educational concern could be addressed by providing more students, specifically girls and underrepresented minorities, access to computing experiences in elementary school settings. Studies have shown that girls and underrepresented students are not represented in STEM career fields, or in the educational pipeline. One way to address this is to provide professionally and personally authentic experiences so that these students persist in their interest and participation in computing. By incorporating Culturally Relevant Teaching and Culturally Relevant Computing practices, teachers provide girls and underrepresented students of color the opportunity to positively identify with computer science. This literature review has highlighted the lack of research around personally authentic computing experiences for students in elementary school settings, with the majority of research focused on professional skills and knowledge outcomes.


This study will be a qualitative study using an in-depth interview design from the perspective of teachers implementing computer science in elementary classrooms. Respondents will participate in two interviews. In both interviews, the respondent will provide a lesson plan document, in which they will walk through and reflect on with the interviewer. According to Creswell and Creswell (2018), a qualitative approach is used to seek meaning people give to a problem. The focus of this study is to explore how teachers implement computer science in the elementary classroom, and the ways in which they think about equitable practices in relation to their computer science instructional goals. This study seeks to understand how teachers implement and articulate strategies to make their practice equitable. The study seeks to understand the participants’ views and experiences and focuses on direct experiences of the participants in order to answer how elementary school teachers think about and implement equitable computer science instruction. Though it is possible to survey teachers, that would not provide in-depth data regarding how and why teachers made certain decisions around the equitable implementation of computer science instruction.

Site and Population Selection
I am interested in any site that has elementary school teachers actively teaching and implementing computer science in the United States. In the United States, elementary schools can vary in ranges from kindergarten - sixth grade. Teachers must be teaching in an elementary school setting, and actively implementing computer science instruction out of their own motivation, or as part of a school or district-wide initiative to provide consistent computer science instruction to students. Since computer science is not a mandated subject, and there are no comprehensive training programs for teaching computer science, any teacher that self-identifies will qualify.
I will interview a variety of teachers based on their experience and comfort level with computer science instruction. This way, the study can explore how any elementary teacher that teaches computer science considers and implements equitable practices in their computer science teaching practices. I’d like to interview at least 20 teachers.

Data Collection Methods
This study will utilize interviews and documents to explore computer science implementation at the elementary school level. This study will use an opportunity sample. I will begin by interviewing participants in my existing professional and personal network and will utilize a snowball sample. As part of my recruitment strategy, I will offer the opportunity to participate in my study by posting in various online communities, such as the community boards for a national professional network of computer science teachers. I will end the interview by asking participating teachers if they know of other elementary teachers that teach computer science who might be interested in participating in the study. I will continue to ask for snowball referrals until I reach the 20 participant sample size for the study.

Semi-structured Interviews
Participant teachers will engage in two parts of the study: two 45-60 minute long interviews. The first part of the study will consist of an interview that asks teachers to provide a lesson plan and reflect on their lesson plan and teaching practices. In the second interview, core components of the Kapor Center’s Reimagining Equitable Computer Science Framework will be provided to participants to reflect and comment on in relation to their lesson plan and teaching practices. All of the interviews will be semi-structured in an effort to create a dialogue and exploratory discussion, as opposed to a rigid question and answer format. The questions asked will pertain to the process of implementing computer science, implementation of equitable practices, if any, and challenges and/or successes of computer science implementation. Interviews will last approximately an hour, and will be hosted and recorded on Zoom. While the interview is being conducted, I will conduct the interview and take notes on the responses of my participants. Ideally, both interviews will occur within a two-week period of one another, but participants will have up to 4 weeks to participate in both interviews.

Lesson Review During the Interview
Before the initial interview, each teacher will be asked to provide one computer science lesson plan that they felt was most successful in their classrooms. Because the study focuses on equity and personally and professionally authentic experiences, a lesson that a teacher deems successful would speak to the interests and engagement of students in computer science. During the first interview, they will be asked to reflect on the lesson plan and provide thoughts and reflections on their instructional goals and how these goals relate to equity. The goal of the first interview will be to understand what teachers are trying to accomplish in their computer science lessons, and what part equity plays in their instruction.
In the second interview, participants will revisit the same lesson plan, but will be asked to read the six core components of the Kapor Center’s Reimagining Computer Science Education Framework. Participants will be told that this framework has been put together by a computer science organization that has promoted computer science education for many years, but this study purposefully examines their thoughts of the framework and how they do or do not apply to their computer science teaching practices, and if the framework pushes them to think differently about how they teach computer science. They will be prompted to share their thoughts as to how each core component could be applied to change and enhance equity in their existing lesson plan. As the participant reads a core component of the framework, they will be asked if the main idea of the core component could be addressed in the lesson plan. This allows participants to respond with adjustments and changes to their lesson plan, or express that they would not incorporate the idea into their teaching practices.

Data Analysis Methods
The audio and video recordings of the zoom interviews will be reviewed before transcription. Then, I will transcribe the interviews using While reviewing the transcripts, notes will be taken and organized by theme in a spreadsheet to start tracking potential categories and repeating themes. Open coding will be used initially to help finalize the categories. Table 1 displays potential responses from participants and how responses will be coded. Once categories are finalized, transcripts and recordings will be reviewed again to be coded again using online programs or Google sheets.
The interviews and lesson plans will be reviewed and coded for references to equity. Specifically, responses and annotations will be examined for how teachers view equity to mean in their teaching practices. These views can include: Equity as access and opportunity, equity as achievement and/or positive identification with computer science as a discipline, equity as expanding what constitutes computer science in the form of integrating alternative cultural perspectives, and equity as including computer science as part of justice movements (NASEM, 2021). In addition, the Kapor Center (2001) identifies specific courses of actions that reflect each core element of equity. Participants’ responses will be coded to fall into these six categories of equity:
Anti-racist teaching practices,
Identity as tools for inclusion and equity, rigorous and authentic pedagogy and curriculum,
Inclusion of student agency, voice, and self determination in computer science instruction,
Inclusion of families, communities, assets, and culture in curriculum, classrooms and learning opportunities,
Incorporation and identification of a diverse variety of experts as role models
Table 1 includes how responses will be coded to reflect the various themes in equity. I will capture the variability in how teachers think about equity by coding and categorizing participant’s responses, lesson/unit plans, and annotations.


The expected results of this study will explore how teachers think about equity in relation to their computer science instruction, and provide concrete strategies for improving equtiable practices in computer science classrooms. Participants will reflect on the Kapor Center's Framework for Equitable CS and provide ways to increase equity in their classrooms.


The goal of my study is to understand how teachers view equity in relation to their goals for computer science instruction, and what strategies they articulate and implement to make their practice equitable. The need for this study stems from our current computing pipeline: the US is not producing enough computing graduates at the rates needed to fill vacant roles. Furthermore, the computing field is not diverse, and lacks representation by women and people from historically underrepresented populations. Computer Science is becoming more and more relevant in high school and middle school settings. If students are exposed to computing opportunities in elementary school, there might be more opportunities for girls and students of color to pursue computing careers.


2020_state_of_cs.pdf. (n.d.). Retrieved February 20, 2021, from
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.
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.
Bers, M. U. (n.d.). Http:// 20.
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.
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.
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.
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.
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
Darling-Hammond, L. (2006). Constructing 21st-Century Teacher Education. Journal of Teacher Education, 57(3), 300–314.
Download.pdf. (n.d.). Retrieved November 10, 2019, from
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.
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.
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.
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.
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.
Full Text PDF. (n.d.). Retrieved February 15, 2021, from
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.
González-Patiño, J., & Esteban-Guitart, M. (2019). Affinity online: How connection and shared interest fuel learning. Mind, Culture, and Activity, 26(4), 383–386.
Grover, S., & Pea, R. (2013). Computational Thinking in K–12: A Review of the State of the Field. Educational Researcher, 42(1), 38–43.
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.
Hidi, S., & Renninger, K. A. (2006). The Four-Phase Model of Interest Development. Educational Psychologist, 41(2), 111–127.
Hiebert, J., Morris, A. K., Berk, D., & Jansen, A. (2007a). Preparing Teachers to Learn from Teaching. Journal of Teacher Education, 58(1), 47–61.
Hiebert, J., Morris, A. K., Berk, D., & Jansen, A. (2007b). Preparing Teachers to Learn from Teaching. Journal of Teacher Education, 58(1), 47–61.
How Vocational Education Got a 21st Century Reboot—POLITICO Magazine. (n.d.). Retrieved November 10, 2019, from
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.
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.
Israel, M., & Lash, T. (2020). From classroom lessons to exploratory learning progressions: Mathematics + computational thinking. Interactive Learning Environments, 28(3), 362–382.
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.
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.
Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Computers in Human Behavior, 52, 200–210.
Kemph, J. P. (1969). Erik H. Erikson. Identity, youth and crisis. New York: W. W. Norton Company, 1968. Behavioral Science, 14(2), 154–159.
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.
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.
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.
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.
Margolis, J. (n.d.). Stuck in the Shallow End | The MIT Press. The MIT Press. Retrieved November 22, 2020, from
Margolis, J., & Fisher, A. (2002). Unlocking the Clubhouse | The MIT Press. The MIT Press.
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.
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.
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.
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.
National Academies of Sciences, E. (2021). Cultivating Interest and Competencies in Computing: Authentic Experiences and Design Factors.
National Academies of Sciences, E., and Medicine. (2018). Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments. The National Academies Press.
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.
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.
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.
Scott, K. A., Sheridan, K. M., & Clark, K. (2015). Culturally responsive computing: A theory revisited. Learning, Media and Technology, 40(4), 412–436.
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.
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.
Snapshot. (n.d.). Retrieved February 15, 2021, from
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.
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.
The Integrated Postsecondary Education Data System. (n.d.). Retrieved November 10, 2019, from
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.
Trends-in-the-state-of-computer-science-report.pdf. (n.d.). Retrieved November 22, 2019, from
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.
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.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
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.

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Dr. Melissa Toohey, Seesaw Learning

Melissa Toohey is the Curriculum Development Director at Seesaw. Her work focuses on ensuring teachers' success and promoting equity and access to CS education. She received her teaching credential and M. Ed from the UCSD, and in June 2022, earned her Doctorate in Educational Leadership from UCLA . Her dissertation focused on equity in CS. Melissa is a former teacher and has taught in private, charter, and public school settings. Her greatest accomplishments include establishing the first elementary STEAM program in Watts, being awarded a $100,000 grant, and helping her students discover their academic and non-academic strengths.