Jacques Ellul, a 20th century French academic, is best known for his writing on technique, understood as both individual means to ends and an all-encompassing system that prioritizes efficient solutions to problems. While Ellul’s terminology is not necessarily familiar in contemporary educational technology research, it represents traditional understandings of individual tools as a subset of instructional systems and captures the field’s emphasis on finding more efficient and efficacious means of teaching and learning. However, Ellul’s writing on technique is largely skeptical, creating an opportunity for educational technology researchers to turn a more critical eye towards our emphasis on solutions to problems. This essay argues that while an increased emphasis problems over things is welcome, Ellul’s writing invites more careful consideration of what it means to solve problems. In particular, I draw on Ellul to ask three provocative questions: which problems should we solve, who should solve those problems, and is solving problems always good?

This chapter is an autoethnography of paradigm shift. By paradigm, I mean a collection of “understandings, methods, values, and expectations” (Kimmons & Johnstun, 2019, p. 631). Autoethnography is particularly well suited for exploring paradigm shift, given that its very practice is a deliberate argument for new scholarly paradigms (Ellis et al., 2011). Autoethnography’s philosophical underpinnings also explain my persistent interjection of the personal into what I myself expected to be a story about my professional life. To write an autoethnography is to accept that “canonical forms of doing and writing research” represent a “White, masculine, heterosexual, middle/upper-classed, Christian, able-bodied perspective” (Ellis et al., 2011, p. 3). It is, therefore, natural that I revisit my personal relationship with that perspective as I write this autoethnography about my professional life.

Deeper assumptions frequently shape the ways educational technology stakeholders collect and use data. This influence of assumptions on data decisions makes it critical that educational technology stakeholders engage with deeper assumptions as part of ethical considerations; indeed, they are key to ensuring that stakeholders engage with structural issues in education and educational technology rather than use ethical compliance as a superficial nod to questions of justice, harm, and power. In this chapter, I illustrate the relationship between deep assumptions and data ethics by considering assumptions related to four broad questions about the purpose of education, the purpose of educational technology, the determination of quality in educational (technology) research, and who has what say in these domains. Debates about data ethics are often better understood as debates about these deeper assumptions, which must be surfaced to consider data ethics in our field thoroughly.

Social media platforms have firmly established themselves as phenomena of interest for Learning Design and Technology (LDT) researchers, and the data accessible from these platforms provide new methodological possibilities. Given the number, diversity, and constant evolution of both social media platforms and research tools, it would be inappropriate to suggest that there is a single correct way to carry out LDT research with social media data. In contrast, this chapter introduces six broad, interconnected steps to social media research: Conducting Ethical Research; Framing the Research; Organizing the Research Process; Collecting Data; Analyzing Data; and Writing, Sharing, and Publicizing Research. Each step is associated with several considerations, careful attention to which will guide researchers toward a particular correct way to accomplish their specific research objectives.

Twitter and other social media have assumed important places in many educators’ professional lives by hosting spaces where new kinds of collegial interactions can occur. However, such spaces can also attract unwelcome Twitter traffic that complicates researchers’ attempts to explore and understand educators’ professional social media experiences. In this article, we define various kinds of spam that we have identified in our research on educators’ uses of Twitter. After providing an overview of the concept of spam, we evaluate the advantages and disadvantages of different approaches to addressing the presence of spam in educator-focused Twitter spaces. Then we suggest practical, holistic metrics that can be employed to help identify spam. Through secondary analyses of our past research, we describe the use of such metrics to identify and deal with spam in three specific cases. Finally, we discuss implications of spam and these suggested methods for teacher educators, instructional designers and educational technology researchers.

Although researchers have discovered a great deal about who uses Twitter for educational purposes, what they post about, when they post and why they participate, there has so far been little work to explore where participants in educational Twitter contexts are located. In this paper, we establish a methodological foundation that can support the exploration of geographical issues in educational Twitter research. We surveyed 46 participants in one educational Twitter hashtag, #michED, to determine where they lived; we then compared these responses to results from three digital methods for geolocating Twitter users (human coding, machine coding and GPS coding) to explore these methods’ affordances and constraints. Human coding of Twitter profiles allowed us to analyze more participants with higher levels of accuracy but also has disadvantages compared to other digital—and traditional—methods. We discuss the additional insights obtained through geolocating #michED participants as well as considerations for using geolocation and other digital methods in educational research.