A Research-Practice Partnership for Developing Computational Thinking through Linguistically and Culturally Relevant CS Curriculum in Middle School

This project will develop a research-practice partnership to plan and pilot a linguistically and culturally relevant computer science curriculum in middle school with the goal of broadening the participation of emergent bilingual (or English learner) students and Latino/a students in computer science education.

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The University of Texas at El Paso (UTEP), together with El Paso Independent School District (EPISD), will develop a research-practice partnership (RPP) to plan and pilot a linguistically and culturally relevant computer science curriculum in middle school with the goal of broadening the participation of emergent bilingual (or English learner) students and Latino/a students in computer science (CS) education. The project will focus on the development of an RPP that can effectively help teachers use bilingual and culturally relevant tools to develop the computational thinking (CT) skills of middle school students in EPISD. By bringing together the promise of culturally relevant CS education and of dual language instruction, this project will seek an innovative solution to the problem of underrepresentation of Latinas/os and emergent bilingual students/English learners in CS education and careers. It does so through a research-practice partnership that ensures responsiveness to the needs of educational practitioners and facilitates the integration of prior NSF-funded research with existing classroom curriculum and practice. The project, together with future scaling work, potentially can serve as a model in at least two existing large networks-the NSF-funded National CAHSI INCLUDES Alliance and the New Tech Network-strengthening efforts in both to broaden participation and engagement of underrepresented students, with particular focus on CS. Through dissemination across the 60 CAHSI institutions, the proposed linguistically and culturally relevant approach could potentially contribute to broadening Hispanic and emergent bilingual participation much beyond the El Paso region. The curriculum developed collaboratively by the RPP would also be disseminated through the national New Tech Network repository of PBL curriculum, accessible to other NTN schools across the country. The model of integrating culturally responsive CT/CS instruction and linguistically responsive dual language instruction has potential to significantly advance efforts to reach, support, and engage more Hispanic youth in CS learning and careers.

The project builds upon research showing that culturally relevant CS education is a promising approach to broadening participation of minoritized students in CS and that dual language bilingual education is a successful approach to improving participation and academic achievement of emergent bilingual (or English learner) students by taking a culturally and linguistically relevant approach to CT/CS instruction for emergent bilingual and Latina/o students. Specifically, the project develops an RPP to plan, co-design, pilot, and refine a curriculum module that is bilingual (Spanish and English) and employs an existing NSF-funded culturally-relevant game-based learning platform, Sol y Agua (Akbar, et al., 2018), that uses locally familiar El Paso area geography and ecology to teach computational thinking. The project will address the following research questions: (1) In what ways and to what extent do teachers demonstrate understanding of computational thinking principles and components and of dual language principles and instructional strategies? (2) How do teachers implement a linguistically and culturally relevant PBL module using Sol y Agua game-based learning platform? And (3) In what ways and to what extent do students demonstrate learning of computational thinking principles and components during and after participating in a linguistically and culturally relevant PBL module using Sol Y Agua? The project will deploy a range of data collection including pre-post testing of teachers' knowledge and implementation of instruction, observation, video recordings of classrooms, and student written assessments and language tracking data from the software tool Sol y Agua. The research team will analyze the data using qualitative data analysis techniques as well as data mining and classification.

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