Our Approach

UCLA STEM+CS is an innovative teacher residency program that integrates a secondary single-subject credential in math or science and courses for supplementary authorization in computer science. Our approach to computer science is grounded in social justice and equity as we strive to disrupt dominant notions of neutrality in both STEM and CS fields. Instead, we critically analyze our world through a humanizing lens within the CS and STEM fields. The STEM+CS program evolved from the STEM+C3 project that was established in 2020 and is organized around the 3Cs: Computational Thinking for equity in a Community Of Practice toward a new vision of Computer Science, one that is more inclusive and focused on justice.

Our Model

The UCLA STEM+CS program is situated within the UCLA TEP urban teacher residency model focused on equity and justice. The STEM+C3 model includes several structural innovations that strengthen the residency vision of STEM teacher preparation with a focus on developing and integrating the three C’s (see Our Approach section above). The focus on computational thinking for equity allows for the critical thinking to be made visible by articulating and verbalizing the metacognitive practices involved in critical thinking. The two-year program combines academic experiences with professional development across five distinct learning spaces illustrated by the diagram below that details the key activities and outcomes for teacher candidates. Continue reading in The Research section below to learn more about specific parts and outcomes of the model, documented through research projects carried out alongside program implementation.

The Research

Over five years (2019-2024), the UCLA STEM+CS team conducted research along the following strands of focus:

Computational Thinking (CT) Practices for Equity

We articulated and defined what “computational thinking (CT) for equity” embodies in the STEM+CS program. CT for equity empowers students to use problem-solving design skills that are informed by computing to explore, egress, critique, and create artifacts about the world around them. CT practices for equity are situated in the community and connect asset- and belong-based pedagogies. These practices reflect an equity-centered lens that integrates data and bridges the gap between content practices and Computer Science. The following figure illustrates how the program’s vision centers on equity, and most importantly, as it relates to the contexts and experiences of the students, community, and the world.

Perez, L., Clark, H.F., Hadad, R., Nava, I., Giannotti, M., 2023. In: Tierney, R.J., Rizvi, F., Erkican, K. (Eds.), International Encyclopedia of Education, vol. 11. Elsevier.

Computational Thinking/Computer Science (CT/CS) Integration for Equity

This study documents STEM+CS program science and math teacher candidates developing pedagogical approaches to integrating CT and data into their courses to support educational equity and social justice. We identified a small but powerful set of core practices that the residency teachers used to support learning outcomes, including integrating data on locally and socially relevant issues. We present group-level trends and three classroom stories, or profiles of practice, to illustrate the generative ways teachers blended priorities from the three frames in instruction. The diversity in the teachers’ conceptions and practices deepens understandings of asset-based pedagogies in CT by shining light on the rich and varied ways that math and science teachers meet the needs of their minoritized students.

Clark, H. F., Gyles, S. A., & Nava-Landeros, I. (2023). Teacher Candidates’ Conceptions and Practices of Computational Thinking for Equity. Journal of Computer Science Integration, 6(1): 6, pp. 1–16.

An Observation Rubric for Assessing CT

In this study, we developed and tested a new observation rubric intended to assess and provide formative feedback to improve teachers’ instructional practices in CT in science and math, developed as part of the STEM+CS project. The CT rubric guided reflection on teaching practice and raters valued the rubric’s focus on social justice, allowing them to reflect on how to make their practice more attuned to students’ interests as well as highlighting areas where data analysis and real-world applications of science could be more effectively linked to CT. It helped raters critically assess their teaching practices, align their instruction with CT principles, and enhance the relevance of their lessons.

Counternarratives Toward Humanizing CS and STEM

STEM+CS program teacher candidates come from diverse disciplines and work in urban Title I schools, serving predominantly Black and Brown students in historically low-income communities in Southern California. In solidarity with the communities we serve, we aspire for a critical and humanizing approach to data science, informed by critical race theory, critical studies, and abolitionist tools in computing education. We delved deeply into our students’ assignments, including reflections and projects, to examine their dispositions and shifts in computing pedagogy and practices. Their presentations exceeded our expectations by effectively addressing discourse and actions to dismantle racial and socio-political injustices through humanizing data and CS practices. We were also encouraged by their written reflections on the project, which revealed that they felt it positively impacted their philosophy and pedagogy in teaching CS.

Kim, J., Nava-Landeros, I., Shriner, M. (2023) Pedagogy of Healers STEM+CS Teachers Repairing STEM Identities. Multicultural Education

STEM+CS Program Evaluation Report

Conducted by the STEM+CS (formerly “STEM+C3”) program’s external evaluator, Center For Research On Evaluation Standards And Student Testing (CRESST), this report examines results for Computational Thinking Tasks administered to teacher residents and mentor teachers. Using monthly, bi-annual, and annual meetings, the evaluation team and program stakeholders were able to maximize learning and use about the integration of CT in the teacher residency. This guided the adjustment of CT coursework and support for participants across time. The evaluation also helped to surface the difficulty of taking practices that are already strongly related to math and science instruction and making them explicit.

Assessing STEM+C3 Teacher Resident and Mentor Knowledge of Computational Thinking: Longitudinal Analysis (CRESST, 2024)

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