Work

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

Year: 2023

Type: article

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.

Source: Journal of Research on Technology in Education

DOI: 10.1080/15391523.2023.2266060

Authors: Zixi Chen, Kaitlin T. Torphy Knake, Hamid Karimi, Nicole Donzella

Institutions: Michigan State University, Utah State University, University of North Carolina at Chapel Hill

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Funders: National Science Foundation, William T. Grant Foundation, American Educational Research Association, Defense Advanced Research Projects Agency