Introduction

The rapid advancement of technology has significantly impacted the way we learn and acquire knowledge. With the emergence of digital technologies, learning has been revolutionized, allowing for new and innovative forms of education. In recent years, scholars have increasingly turned their attention to understanding the implications of technology in learning and teaching. This chapter explores the role of posthuman theory and actor-network theory in undergirding teachers’ learning with technology, specifically in open digital spaces, and how digital ethnography might be used to study student learning. Posthuman theory acknowledges the entanglement of humans and technology, challenging traditional notions of the human as a separate and autonomous entity, whereas actor-network theory helps conceptualize the relations and connections between the different actors within a network. By examining the interplay between technology and its entanglement with human actants, this chapter contributes to a more comprehensive and nuanced understanding of the complex and evolving relationship between technology and learning.

Self-Directed Professional Development Among Educators

There has always been a small and steady stream of educators who seek out learning outside their work environments as they cannot find the support they need within their places of employment; these digital learning contexts encourage teachers to negotiate meaning and concepts across different teaching contexts (Wenger-Trayner et al., 2015) beyond their workplace and bring back this new understanding to their local work communities. By taking charge of their learning, educators can identify their professional needs, set personal goals, and select the most appropriate resources and activities to meet those needs.

Self-directed professional development (PD) is particularly important in today’s rapidly changing educational landscape, where teachers in higher education must be able to adapt to new technologies, teaching methods, and curriculum standards. Many institutions cannot keep up with demands for learning on topics, most recently seen with large language models (LLMs)^[1] such as Open AI’s ChatGPT and other artificial intelligence tools. As such, this chapter focuses on the results of the author’s doctoral research study, stemming from their own interest in learning online, and how teachers engage in self-directed professional growth, the importance of networks, and the role technology plays, and can potentially play, in learning and learning about learning. For the context of this study, teacher learning was conceptualized as learning skills, cognitive processes, reflective practice, and personal development (Richards & Farrell, 2005).

Study Design

Motivation

My research interest in examining teachers guiding their professional learning in digital spaces materialised because educational technology changed my own approach to teaching and learning as a mid-career teacher. This was challenging since I did not have an effective space to discuss my interests about educational technology over 10 years ago within the English language teaching (ELT) community. I noticed that many educators perceived technology as a tool used for learning, with human agency intact over its serviceability. When I started learning about educational technology, I found most of my learning took place online: first, from blogs; then, from the teacher-learning community on Twitter^[2]. I appreciated the collaborative inquiry aspect of learning to teach with technology from educational Twitter chats, which I could not replicate in face-to-face settings, and this piqued my scholarly interest in learning more about teachers’ learning: I wanted to examine whether there was something unique about teachers learning online and learning through technology that could not be duplicated in face-to-face interactions, but I was initially challenged to find a space where I could freely observe learning taking place without construing the environment for research purposes.

Research and Data Collection

The research this chapter draws from is part of a doctoral dissertation observing Twitter-based discussions among two English language teacher (ELT) Twitter chats, spanning a time frame of three months from autumn 2020 to early 2021. The study was a mixed-method netnography using descriptive statistics, social network analysis^[3], discourse analysis, and semi-structured interviews. By focusing on interactions and relationships between actors and technology, this fits best within a critical realist paradigm (Chapter 2). The research objectives aimed to uncover the way professional development occurs on Twitter, the distinguishing features between online Twitter chats and conventional face-to-face professional development prospects, and whether language teachers’ engagement on Twitter shapes their interests, methodologies, and pedagogical approaches.

Before I settled on netnography, I had examined phenomenology, ethnography, and narrative case studies as methods to explore my research questions. However, the digital space where I intended to conduct my research, then called Twitter, featured unique online characteristics which were important to consider within the methodology of the study. Netnography is a type of ethnographic research conducted online, where researchers analyse the interactions and behaviours of individuals within specific communities. It involves a combination of qualitative and quantitative research methods, which can include participant observation, interviews, surveys, and content analysis. Researchers typically observe and record online discussions, track user behaviours, and analyse the content of online postings to gain insights into the culture and norms of the online community being studied. Netnography provides a unique opportunity to study the behaviour of communities online, which can be challenging to observe in real-world settings.

The concept of conducting research on Twitter was built on two considerations: finding an open space where I could observe conversations taking place that were not for research purposes and having access to, and knowledge of, a platform. The Twitter platform allows individuals to create their own connections without the obligation of reciprocity and the data is publicly accessible. Because of the open access to Twitter and Twitter’s application programming interface (API), it was possible to observe chats taking place without requesting permission from closed groups or moderators; at the time of the study, it was also possible to collect Twitter data freely through Twitter’s API from the previous seven to 10 days.

The structure of the four stages of data collection, illustrated in Figure 9.1, allowed me to observe the various actants through collecting Twitter data, observing chats, and using social network analysis to help visualize what was taking place on Twitter, with what and with whom. Additionally, digital objects (e.g. the network, hashtags, handles, connections, devices) were “interviewed” through a series of heuristics (Adams & Thompson, 2016) to acknowledge their agency and treat them with the same attention as human participants. In the second stage of data analysis, I examined the ways the Twitter platform itself offered opportunities for interaction as well as prevented exchanges from taking place. In addition to the hashtags and Twitter platform, it was important to examine the structure of Twitter chats and how they are used. At this stage the chat topics and their popularity were examined, which informed the third stage of the study that examined the discourse of the exchanges during the Twitter chats.

It is important to note that Twitter chat participants (n. 363) were not considered research participants until the fourth stage, when specific chat participants, totalling 11 final research participants, were invited to participate in semi-structured interviews. Anyone engaging in the chats who was not an interview participant was anonymised in the visual representation of networked data and was not identified in any way. This detail was clearly outlined in my ethics approval—that individuals who engaged in Twitter chat were not study participants; only the individuals I interviewed were study participants.

Theoretical Framework

Online Learning With a Posthuman Lens

Educational research of human and technological connections, like social media or devices, has a received or embedded view, according to Emke (2019). The first, the received view, is that technology and individuals can be researched as independent of each other; the second, the embedded view, is with humans as the subject of the research, and the technology is used and within the control of the human actant (Emke, 2019). These two views have kept research about social networks, like those on Twitter, to studies involving PLNs^[4] and the like (Nicholas et al., 2018; Lupasco, 2017).

Much like Emke, I find these two approaches lacking in research of online spaces involving human participants. Ascribing autonomy to the learner in digital contexts does not consider the agency of the network and the other nonhuman actants which can direct, suppress, or promote learning in digital networks. We need only to misplace our mobile phones and note how often we inadvertently reach for them to check notifications, whether the device has pinged us or not, to understand how reliant we are on technology and the agency it has over our actions. Relying on our mobile phones has even led cognitive scientists to suggest that we use our mobile devices as a physical extension of our cognition, especially in instances which require analytical thinking (Barr et al., 2015).

The emerging literature on posthumanism illustrates that human agency does not extend as far as many think on digital platforms. Posthuman literature (Hayles, 2006; Baraidotti, 2019) can be used to support the understanding of the discourse that takes place online through technology and examines the kind of humans we are becoming. Although posthumanistic approaches are found in various fields (Snaza et al., 2014) in addition to digital humanities (Braidotti, 2019), it is not often found in educational research. This study considers the impact that technology has on learning and does not privilege the learner as autonomous from the digital platform but as a collaborator with technology. As such, a posthuman lens to studying self-directed PD on a social media platform was chosen for this study, as the platforms, technology, devices, language, and access to digital communication networks all play a role in how we communicate and, thus, impact how we learn. However, a posthumanist lens does not account for the behaviour of networks; for this, I turned to actor-network theory.

Learning as Other Than Social Mediation; Networks Enacted

Actor-network theory (ANT) was incorporated into my theoretical framework to help reconceptualize the actants in the network, since the social network analysis tool I used only recognizes Twitter handles and connections between them and I wanted to examine how all actants, both social and material, have agency in the learning process. ANT affords all actants in the network the same agency, whether they are a device, technology, URL, or person; the network is not activated until an actant engages it (Latour, 2005). This can be a person sending out a tweet, the WiFi turning on, a hashtag being used, or a scheduled tweet being posted on Twitter.

Using this theory also directed me to ask:

• If one of the connections is affected, what else can be affected?
• Will movement or disruptions in the network impact the network negatively?

Through the observation of weak ties (Granovetter, 1973) and how individuals with few ties can disseminate information to a new network, all parts of the network become important and the interaction becomes the focus, not how “people view their activity on Twitter” (Bartosik, 2022, p. 21) as within their control. Learners engaging in PD on Twitter who were, for the most part, lurking may not be initially seen as learning or disseminating knowledge, but through the use of ANT to trace engagement and then through interviews with participants, I was able to trace and appreciate how silence can be measured and understood as learning.

In the fourth stage of data collection, Twitter chat participants were selected based on the social network analysis data: the most influential and active chat participants were invited to an interview, in addition to those who were not very active at all. Some interview participants, who had been invited to be part of the study because the network analysis had identified them as lurkers, were in fact very active and influential in their teaching and learning communities and used what they had learned on Twitter in knowledge dissemination outside of the platform.

Framing Posthumanism and ANT to the Study

Actor-network theory was applied to understand the social-network analysis in the first stage of the study, seen in Figure 9.1. ANT provides a means to identify the actants in the network and their relationships to the other actants as well as identify network changes, but it does not provide clarity about behaviours within the network. The entanglement of the platform and other nonhuman trappings that teachers immerse themselves in on Twitter points to the lack of autonomy the teacher has from the technology as it coexists in learning. As chat and discourse analyses on the Twitter chats were conducted in stages two and three and participant interviews were analysed, the posthuman lens was used to understand how the learning being observed was socially constructed and entangled with the nonhuman elements through how hashtags were used, how well users knew the Twitter platform and understood its framing of their learning, and how users understood how to navigate the platform to personalize their learning. This was an essential stage in data analysis in order to identify how familiarity and knowledge of a digital platform, whether it is educational technology or a social media app, can impact learning. I provide some examples in the next section.

Study Results

A Look at Chat Participant Awareness of the (Socio)Material

In participant interviews, several chat lurkers were quick to indicate that they were not aware of all of Twitter’s affordances. However, as it turned out, even Twitter users who were not fully aware of Twitter’s capabilities in the study were aware of Twitter’s mechanisms and accepted the algorithmic suggestions because they seemed to know what was wanted, as seen in the quote below:

It seems like Twitter analytics knows what I get interested in and it keeps showing me stuff that I keep clicking on.

Others had very sophisticated and accurate impressions of how networks on Twitter worked:

I just have a visual of this huge, I don’t know, network, some parts like cogs and some parts like a spiderweb and it’s like everywhere, right? And there are all these connections…

Study participants stated that as they became familiar with how the Twitter platform operated, they began to edit the list of people they followed and made deliberate choices to round out their professional development. Even study participants who claimed they were unaware of how the Twitter platform influenced their learning expressed various strategies they used to counteract the agency of the nonhuman actants that influenced their learning, such as capitalizing on the power of hashtags, the “follow” option, and creating lists. However, there were also human actants that influenced how and when learning took place in Twitter chats, such as the chat moderators.

Chat moderators, or those with betweenness centrality^[5], play a major role in the continued existence of chat networks because almost all information and conversations in chats move through them. Chat hosts also have agency by deciding the direction of the chat. The chats related to Intersectionality and Race and Queerness in ELT were topics guided by the chat hosts, as confirmed by study participants. One moderator spoke about the agency their followers provide them:

Once I got to about 5000 (followers) I started to think: ok, you have a platform now and you need to take responsibility for the things that you say and use that platform change the way ELT works.

The role of the network, the actants within it, and the human and nonhuman actants all guided the direction of chats in addition to the topics chosen for the English language teacher chats. I mentioned earlier that learning within digital networks encourages movement across disciplines, which was evident in this study as well; teachers were engaging in social justice issues and their chats were influenced by current events taking place. This was evident in the hashtags they used and followed, such as #MeToo, #SustainabilityInELT, and #Intersectionality. These hashtags caught my interest as a researcher because, on the surface, they are not on the topic of English language teaching.

However, the chat moderators, aware of their impact, brought topics that were socially important into the discussions of English language teaching. When the hashtag #ELT is used with #Intersectionality, the two distinct hashtags, used together in one tweet, could provide those interested in either topic the opportunity to interact together if someone searched one or the other hashtag. Their public chat discussions and the learning they took from chats were then applied to their teaching contexts, which interview participants said they did regularly. Therefore, although human actants guided the chat topics because of their philosophy, the use of non-human actants, such as hashtags, guided who might engage with the chat if it piqued interest.

Learning (In)Action?

When asked about their reason for being on Twitter, some of the study participants spoke about looking for a place to “listen” and learn about topics they a) knew very little about; b) were interested in; c) did not have opportunities to learn about in their work contexts, and d) could learn about in a safe and welcoming environment, away from their work contexts. These reasons were evident even among those with more currency in the chats, who were active and encouraged others to engage but, in interviews, revealed that some topics they would not address publicly for fear of reprisals. Some of those “forbidden” topics included opinions about unpopular government initiatives in the Canadian English language teaching landscape, whereas others worried over their personal beliefs related to politics or social justice. One participant agonized over the simple act of clicking “like” on a tweet.

Should I like this tweet? Because what if my employer sees that? Should I separate my political interests and my just human interests? Should I create another Twitter handle? I’m never sure about that and I kind of gave up on that…I sometimes withhold liking certain tweets when I think it might work against me in the future.^[6]

The silence during some of the very popular chats was not indicative of a lack of participation or people not learning. During the chat on Queerness in ELT, there was a high number of chat participants, demonstrated by the number of likes and retweets. However, there was little engagement beyond this, possibly because identifying with the queer community could result in job losses or even imprisonment in some of the countries participants were from. At the time of the study, privacy setting options on Twitter (X) differed from present settings, and no one on Twitter could not hide their “likes” unless their profile was private, which would not allow them to participate in a public chat. The chat participant avatars were often photos of themselves and their Twitter handles either partially or fully identified their real identities. Other chat participants may have been silent because they were lurking to learn more about Queerness in ELT.

The Material: The Tools, The Hashtag, and The Chats

Retweets as Learning

As much as learning was taking place among the interlocutors in chats, the learning was promoted, aided, or suppressed by various elements in the Twitter machine. For example, the retweet gave Twitter handles agency because, once a tweet is reshared, the results of retweeting are not within the control of the human who shared the tweet in the first place and the shared tweet can be shared to a different network. The power of a simple retweet was recognized by one interview participant as a powerful dissemination tool:

If you assume the role of the listener, and I think the sharing is another, just the fact that you listened, I think, and then you may share that with someone else, it’s already, you know, dissemination as well. You’re already spreading the word as well and sharing, but in a different way. Maybe not your own experience, maybe somebody else’s.

The Agency of Digital Tools: “Language” Use

The hashtag played a large role in various aspects of learning, both boosting and suppressing learning. For participants with more understanding about Twitter and hashtags, the hashtag played several roles in assisting learning. Knowledge about hashtag affordances impacted how well they were used. At the most basic level, they provided newcomers to chats with an “address” to arrive at and observe silently, either during or after a chat; a user could potentially click on a hashtag to join other conversations taking place with other groups and join two disparate groups together. Another more significant use was following hashtags in lieu of following handles to see what was current on a specific topic.

As one participant said so eloquently:

A hashtag is like a pebble that’s tossed into a pond because it ripples out and you can follow those paths. I’m mixing my metaphors here. But you know, it can lead you down some interesting paths.

Hashtags as portals to learning present unique ways for accessing information; exchanges can continue long after a chat has taken place and individuals who were not even present during the chat can trace back and see the whole exchange. These possibilities open thinking about nonhuman, or the material, assemblages in digital spaces and the opportunities they provide to learn at one’s pace and at the time of need.

Learning within and on a technology platform can be summed up in these ways:

• Learning can be dependent on familiarity of a digital platform;
• Those with more knowledge of a platform benefit from said knowledge;
• Silence is not an indication of disinterest or not learning;
• Learning in one digital space can be transferred to another space;
• Learner agency is not separate from technology—the learner and the nonhuman trappings are ensnared and trying to demarcate where one ends and the other begins does not acknowledge the agency of technology’s influence.

How can these study results be used for studying student learning with technology? I reflect on the possibilities in the next section.

Conclusion

A finding in this study highlighted that those with less knowledge about online platforms are less able to disseminate their knowledge. In stark contrast to lurkers, influencers possess a great deal of social capital (Adam & Roncević, 2003) and use the nonhuman actants in digital learning spaces, like expansive knowledge of a digital platform, to disseminate information or to signal areas of interest or expertise. Those with more social capital in this study also increased their knowledge on subjects outside their areas of expertise.

If we consider an educational setting, similar questions can be asked:

• How well do users know the digital platform they are using?
• Do they understand how it directs or suppresses their learning?
• Has the teacher shared that information with them?
• How do learners understand how to manage the platform they are learning on?

For example, high school students in Ontario, Canada, have been moving to Brightspace, a learning management system from the company D2L. Previously, the majority of teachers had been using Google Classroom. To what extent does the unfamiliarity with a new platform influence the teacher’s proficiency of its use and how does the teacher’s proficiency, or lack of, influence students’ learning?

As stated earlier, an embedded or received view of understanding technology in learning can no longer be the direction educational research takes. The sudden hum among educators and artificial intelligence is heightening this awareness. As Dr. Sarah Elaine Eaton (2023) says:

Hybrid writing, co-created by human and artificial intelligence together is becoming prevalent. Soon it will be the norm. Trying to determine where the human ends and where the artificial intelligence begins is pointless and futile.

We have intelligence agents built into our technology—image suggestions, auto-replies, and predictive text—that already guide and direct our responses. It is futile to think that a learner is in control of their learning and learning is socially mediated; learning is mediated by actants, both human and nonhuman, and sometimes, we cannot tell them apart. As teachers grapple with the use of generative AI in student work and how the AI tools themselves evolve almost daily, the importance of understanding how technology works and how students learn with this new technology in their hands cannot be ignored.

Finally, while the potential for networks to build social capital has been researched (Endicott, 2011; Fox & Wilson, 2015), this study provided insights into how autonomous choices on digital platforms used for learning are not always possible due to the nonhuman actants such as the platform’s restrictions, restrictions for communicating on the platform, and communication tools. These elements can also be examined in digital ethnographic research. The ethical considerations of using technology—such as its environmental impact, like with artificial intelligence; its lack of privacy, as data on specific users can be tracked; and its bias and inequity in the algorithmic results of prompts—all play a role in learning and may affect students’ learning negatively.

In the context of education, netnography has the potential to provide valuable insights into student learning by analysing the interactions that take place in online environments. One way that netnography can be used to study student learning is through examining the online discussions and collaborations that occur among students and instructors. This can include analysing the language and discourse used by participants as well as the types of knowledge and skills that are shared and developed through these interactions within a learning management system (LMS), such as which links students click after watching a video or where they pick up after leaving the LMS. Netnographic research could be digital in form and identify which apps students prefer to use, which article students spent a longer time with, or their engagement with other learners on the LMS platform. This type of analysis is different from LMS-offered algorithmic analytics, which focus on learner productivity and performance to identify “at risk” students (Currie, 2022). A narrow understanding of learning does not account for learners remaining silent or leaving the LMS platform for various reasons unrelated to “productive” learning. This perceived approach to learning as being productive can more easily be challenged by researchers as there are now easy tools and add-ins that can complete complex analyses in a short period of time.

One element study participants appreciated about Twitter was the ability to learn without being observed; traditional discussion boards on an LMS name students, which forces learners to publicly demonstrate their learning. Spaces outside the LMS, such as an anonymous Padlet, can provide the same calibre of interaction as a discussion board while also allowing learners to express their learning as it is taking place, without fear of being named or criticized in the process of their learning. Traditional learner analytics on an LMS examine learner engagement—but not the quality of it—with others. The analytical algorithms operate within the parameters the institution has enabled for the LMS and how the teacher uses it, which are digitally imposed restrictions. Learner analytics do not trace how language is used or what a learner clicks next after reading something outside the LMS.

In my study, the definition of learning used included reflective practice (Richards & Farrell, 2005); when interviewing study participants, they were given an opportunity to reflect on what they have learned from using Twitter. Providing learners with an opportunity to explain what influences or impacts their learning and why provides learners with an understanding of autonomy over their learning and gives them an idea of what they can do. No longer is technology being analysed in how it is used, but instead, the shift moves to the learner and how they interact and how their intentions are supported or thwarted by learning. The learner becomes aware of the various opportunities to act. Networked learning is not new but the agency of the nonhuman in the network and its impact on learning is undervalued.

Another way that netnography can be used is by analysing the online resources that students use to support their learning, such as hyperlinks or annotation apps like hypothes.is. This can include examining the types of websites, videos, and other digital materials that students access to learn about specific topics or concepts. In my study discussion, I examined hashtags as portals, language used, and silent learning—how can these apply to student learning with technology? By leveraging netnographic methods to study student learning, researchers can gain a better understanding of the ways in which technology is shaping education and how educators can effectively support student learning in digital environments.

Conducting digital ethnography can guide educational research in new directions. The digital platform where the netnography takes place is not as important as acknowledging the insights gleaned from examining the agency technology and technology-mediated learning have in both supporting and suppressing learning.

References

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Media Attributions

All images in this chapter have been created by the author, unless otherwise noted below.

Long Descriptions

Figure 9.1 Long Description: The four stages of data collection and research:

• Stage 1: Twitter data extraction
• Stage 2: Chat analysis
• Stage 3: Discourse analysis
• Stage 4: Participant interviews

Final interview participant demographics are:

• All have taught for more than 10 years
• Nationality:
  • 3 from the UK
  • 8 are from Canada
• Occupation:
  • 5 are in the settlement language sector
  • 2 are teacher trainers
  • 4 are in English language teaching for academic purposes
  • 2 are materials writers

[Return to Figure 9.1]