Abstract:

Tangential to the efforts to bring computer science (CS) and computational thinking (CT) into K-12 education, amidst the world being awash with data from the pandemic, there has been increasing recognition of the critical role of data science (DS) in preparing future citizenry to be able to gather, analyze and represent data. Now more than ever, students need to engage in DS practices through a critical lens as they question assumptions and seek answers to questions such as: Who is collecting data? For what purpose? How is this data being displayed and what may/may not be misleading about this representation? What is the data that is not included/missing? Whose story is being told through this data? Whose story is missing? Yet, despite the acknowledgement that DS is critical, it is often a topic that is overlooked in preservice education and in K-12 classrooms, or one that is taught in isolation as a single, discrete topic rather than as an integration of mathematics, CS, CT, and science applied in relevant and meaningful ways.
We present our landscape analysis of the intersection of DS in preservice methods and content courses in order to address the capacity for CS education within preservice teacher pathways. Addressing DS as a door to CS and CT in math, science, and social studies pre-service teacher pathways may also expand access and opportunity to CS and CT in these classes.
This paper presents a framework through which DS can be applied to lesson planning and integrating into preservice teacher courses that weave in personal, cultural, and sociopolitical layers of DS into DS practices. Combined with algorithmic thinking, inferential thinking, and computational thinking, this framework is an approach that empowers teachers to engage students in significant DS learning across disciplines.

Keywords:

Data science, education, integration.