Collaborative Network of Grades 3-5 Educators for Computational Thinking for English Learners

University Of California-Irvine, Irvine CA

Investigators

Abstract

Though Hispanics and English learners constitute two of the fastest growing segments of the K-12 student population, little is known about effective ways of teaching computational thinking to students in these groups, especially at the elementary school level. In the first stage of this project, the research team developed an innovative curriculum that included standards matching, language scaffolds, and culturally-relevant pedagogy to meet the needs of Hispanics and English learners and piloted a wide range of measures to assess the impact on learning processes and outcomes. Building on this successful Research-Practice Partnership between the University of California, Irvine, and Santa Ana Unified School District, the project will iteratively research and develop an approach for teaching computational thinking to SAUSD's large numbers of students who are Hispanic and English learners. The project will further develop the curriculum, refine the related professional development, scale up the project to 40 additional fourth grade teachers in 10 SAUSD schools, and collect a wide range of qualitative and quantitative data to iteratively improve the project and evaluate its impact on learning processes and outcomes. The project is among the first to examine the linguistic and sociocultural processes that underlie English learners' success in mastering computational thinking, as well as the role of computational thinking in an English language arts curriculum. Materials developed are based on California computer science and English language arts standards and will be actively disseminated to other districts in the state, making them available for use in a state that has the largest amount of Hispanic students (54%) and English learners (20%) in the nation. A team of graduate and undergraduate student researchers in the project, all of whom are Hispanic and/or female, will receive training in diverse research methods for CS education. Information about the project will also be integrated into courses in UC Irvine's Education Sciences Major, Master of Arts in Teaching, CalTeach, and PhD in Education programs, which together serve more than 1000 students per year, the majority of whom are underrepresented minorities and first-generation college students, and the majority of whom continue on to become K-12 or college faculty. By further developing the curriculum and professional development and implementing it in 40 additional classrooms, the project team will carefully assess the most effective instructional practices in aiding students' computational thinking, developing identity with the field of computer science, and developing academic language proficiency. The curriculum and professional development materials developed and refined through this project will be tailored to the needs of Hispanics and English learners through explicit teaching of CS language functions, inclusion of culturally relevant stories to read and create, and instruction based on collaboration, conversation and inquiry. The RPP team will address research questions focused on teaching such as the challenges and use of the curriculum units and the implications for the professional development; learning including the affects on students' attitudes and knowledge development; and the partnership including describing the ways to enhance the RPP to better address common goals and needs. Using mixed methods, the project will document the scale-up efforts and the development of new tools to support scaling up. Qualitative data will include transcripts from observations and interviews and notes from the design group. The team will analyze these data using grounded theory approaches. The team will collect pre-post quantitative data on students' computational thinking, literacy development, and identification and attitudes toward computer science. Because the data will be both continuous and ordinal, the team will use a range of analytic methods including paired t-tests, Mann-Whitney U tests, analyses of covariance, regressions, and hierarchical linear models to address the research questions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.