Computational Thinking With Urban Latinx Adolescents: A New Materialist Feminist Science Education

• In Event: Innovations and New Directions in Methods and Frameworks for Learning

Abstract

A Reciprocal Model for Teaching and Learning Computational Competencies (ARMTLCC) is a three-year long project that extends existing research in the field by propositioning a model to teach Computational Competencies (CC). As ARMTLCC’s goal is to develop a Relevant, Engaging, and Authentic Learning (REAL) model for science teacher educators to work with young urban Latino adolescents. These REAL experiences including “afterschool STEM programs have the potential to reach minority and typically underserved audiences in ways that schools and even museums cannot” (Center for Advancement of Informal Science Education, 2013).
Although, computational competencies include ability of working with computers and programming, it relies on “automation of abstraction” (Wing, 2006) as a way of thinking rather than a set of skills or a body of knowledge. In the after-school program students initiated Freirian (1968) Problem Posing Projects (PPP) and were engaged in series of computerless computing activities; e.g., developing pseducodes, as a way of thinking. In this paper we propose to analyze student-generated pseducodes as non-human actants that intra-act (Barad, 1996) with the socio-political contexts of their PPPs. Unlike mainstream science projects that remain focused on natural sciences, students posed a series of socio-political problems. For instance, many of them explored the rise of Donald Trump in 2016 as a social phenomenon. The pseducodes served as apparatuses to translate human/social issues to the machine. These translations, like any other translation, are not transparent and neutral. Informed by New Materialism/ New Empiricism, St. Pierre (2011) introduced the concept of “post qualitative inquiry” to destabilize what she called “conventional humanist qualitative inquiry.” Deleuze and Guattari (1987) wrote “a method is the striated space of the cogitatio universalis and draws a path that must be followed from one point to another” (p. 377). That is, method is a prescription or a recipe for thinking and practices that prohibits anything but the “normal science” (Kuhn, 1970) in which in which the language or discourse of interviews (as a very popular method of inquiry in conventional humanist qualitative research) were relations of power that undergirded language, discipline, thought, and speech. Deleuze and Guattari were not interested in a speaking subject. In other words, what people said in ordinary conversation was mostly a reflection of dominant discourse. Jackson & Mazzei (2011) challenged qualitative researchers to use theory to think with their data (or use data to think with theory) in order to accomplish a reading of data that is both within and against interpretivism. They argued that data interpretation and analysis does not happen via mechanistic coding, reducing data to themes, and writing up transparent narratives that do little to critique the complexities of social life; such simplistic approaches preclude dense and multi-layered treatment of data. This suggests a new flattened ontology in which data is not the foundation, but rather provocation in the New Materialist/ New Empiricist ontology. This ontology enables researchers to see non-human agency in this “flattened ontology” (Deleuze and Guattari, 1987).

Authors

• Rouhollah Aghasaleh, Georgia State University

• Patrick James Enderle, Georgia State University

• Anton Puvirajah, Georgia State University