Building a nationally representative sample of teachers’ online and offline

Social Media
Teachers
Sample design
Publication
Author

Zixi Chen

Published

November 9, 2023

Building a nationally representative sample of teachers’ online and offline: the Public Instructional Network of School Resources

In this study, we collected a U.S. national sample of teachers presented on Pinterest with high external validity at the school and district level. You can find the preprint version here. Taylor & Francis Online also provided free online copies while capped at 50.

Abstract: The emerging big data allows educational studies to examine teaching and learning behaviors over time and at scale. Less available is population-representative big data. This paper builds the first nationally representative sample of teachers’ online curation on a social media platform (i.e. Pinterest), the Public Instructional Network of School Resources (PINSR). This effort includes developing a big-rich data sampling framework, integrating social media data with administrative and census “ground truth” sources, and validating the population representativeness. Finally, we employ PINSR and present a worked example of teachers’ social media curation behavioral patterns across regions and time.

Cite this work

Chen, Z., Torphy Knake, K. T., Karimi, H., & Donzella, N. (2023). Building a nationally representative sample of teachers’ online and offline: the Public Instructional Network of School Resources. Journal of Research on Technology in Education, 1-25.