Reconciling the promise and pragmatics of enhancing computing pedagogy with data science

Abstract

Data science keeps growing in popularity as an introductory computing experience, in which students answer real-world questions by processing data. Armed with carefully prepared pedagogical datasets, computing educators can contextualize assignments and projects in societally meaningful ways, thereby benefiting students’ long-term professional careers. However, integrating data science into introductory computing courses requires that the datasets be sufficiently complex, follow appropriate organizational structure, and possess ample documentation. Moreover, the impact of a data science context on students’ motivation remains poorly understood. To address these issues, we have created an open-sourced manual for developing pedagogical datasets (freely available at https://think.cs.vt.edu/pragmatics). Structured as a collection of patterns, this manual shares the expertise that we have gained over the last several years, collecting and curating a large collection of real-world datasets, used in a dozen of universities worldwide. We also present new evidence confirming the efficacy of integrating data science in an introductory computing course. As a significant extension of our ongoing work, this study not only validates existing positive assessment, but also provides fine-grained nuance to the potential of data science as a motivational educational element.