Enilda Romero-Hall edited the book Research Methods in Learning Design and Technology (LDT), which was recently published by Routledge (available for purchase here).
I co-wrote a chapter led by Spencer Greenhalgh on considerations for using social media data in LDT research (available open-access from the book site here).
I also wrote a chapter on making data science count in and for education with Michael Lawson, Daniel Anderson, Seth Jones, and Teya Rutherford. Here is a link to a pre-print for the data science chapter. The abstract is below:
New data sources and analytic techniques have enabled educational researchers to ask new questions and work to address enduring problems, yet there are challenges to those learning and applying these methods. In this chapter, we provide an overview of a nascent area of both scholarship and teaching, educational data science. We define educational data science as the combination of capabilities related to quantitative methods in educational research, computer science and programming capabilities, and teaching, learning, and educational systems. We demonstrate that there are two distinct—but complementary—perspectives on educational data science, in terms of being both in education (as a research methodology) and for education (as a teaching and learning content). We describe both of these areas in light of foundational and recent research. Lastly, we highlight three future directions for educational data science, emphasizing the synergies between these two perspectives concerning designing tools that can be used by both learners and professionals, foregrounding representation, inclusivity, and access as first-order concerns for those involved in the growing community, and using data science methodologies to study teaching and learning about data science. We highlight the potential for the growth of educational data science within Learning, Design, and Technology as situated with the wider data science domain and in education more broadly.
I think the chapter makes a few contributions. One is trying to offer a definition - specific to education - of data science. Another is that it (aims) to bring together literature from two different (both nascent) fields, data science research methods and data science education.
A link, again, is here. Thank you Enilda for editing this book and for the chance to share work (twice) in it.