It’s All in the Mix: Food as a Context for CS Education

Planning a dinner party, creating a playlist, or learning a foreign language represent real world examples of computational thinking. Computational Algorithmic Thinking (CAT), the ability to design, implement, and assess algorithms to solve a range of problems, focuses on understanding how learners understand a problem, articulate an algorithm or set of algorithms in the form of a solution to the problem, and evaluate the solution based on some set of criteria (Rankin & Thomas, 2016; Thomas, Rankin, Minor & Li, 2017). CAT, initially rooted in Mathematics to promote algorithmic thinking, is an important scaffolded on-ramp that enables students to develop more advanced computational thinking abilities (Rankin & Thomas, 2017). The key is to build upon students’ prior knowledge as a bridge to develop their CAT capabilities in an easily accessible, supportive learning environment.

Definition of Algorithm

All students have funds of knowledge or everyday experiences with eating or preparing food, ranging from the simple act of fixing a bowl of cereal for breakfast to cooking a main entrée for dinner (Moje et al., 2004). Leveraging students’ funds of knowledge or everyday experiences with food, I presented a paper (Rankin et al., 2019) at the 2019 ACM SIGCSE conference that demonstrated how the implementation of food-related activities in an introductory Computer Science (CS) course created an equitable learning environment for African American women who have little if any programming experience prior to matriculation into college. Having experienced higher student retention rates when integrating the set of food-focused activities into entry level programming courses, I now seek to adapt the food module to include age-appropriate activities that assist elementary students with learning how to write and apply well-defined algorithms (unambiguous, finite with a specific outcome) as both a problem-solving tool and a precursor to learning computer programming (Rankin et al., 2019; Rankin & Thomas, 2017; Rankin & Thomas, 2016).