Outtake: Computational Problem-Posing with Urban Latinx Youth: Make Science Teaching Great Again

Computational thinking, an “analytic approach to problem solving, designing systems, and understanding human behaviors” (Sengupta, Kinnebrew, Basu, Biswas, & Clark, 2013, p. 352), is regarded as a fundamental requirement of all science, technology, engineering, and mathematics (STEM) disciplines. Access to computer science education is limited for non-Asian minorities, students of low socioeconomic status (SES), and girls (Goode, 2007; Wilson, Sudol, Stephenson, & Stehlik, 2010). Thus, this becomes a profound social justice issue that privileges certain students more than others and creates segregation in computer science education that extends to the workplace.

To address this inequity, we are conducting a 3-year-long project that focuses on developing a reciprocal model for teaching and learning computational competencies. The project’s goal is to develop a model based on principles of culturally relevant pedagogy (Ladson-Billings, 2014) and to implement it in an extant after-school program for middle school Latinx students in an urban school district in the Southeastern United States. The reciprocal nature of the model involves after-school teachers (university preservice teachers) and the Latinx students learning from and teaching each other. The teachers learn to develop and implement culturally relevant computational experiences through seminar classes and field experiences in the after-school setting, and the Latinx students learn computational competencies.