“Connectivism is based on the idea that knowledge is essentially the set of connections in a network, and that learning therefore is the process of creating and shaping those networks.” (Downes, 2018)
How does the connectivist cMOOC e-Learning 3.0 demonstrate the theory in which it is grounded? The premise of connectivism is that, following the unidirectional read-only (RO) Web 1.0 and the more interactive read/write (RW) Web 2.0, we are now “entering the third major phase of the world wide web” (Downes, 2018), one in which knowledge is not just shared and co-created but “distributed across a network of connections” (Downes, 2007). Beyond theory, how does connectivism behave in practice? Drawing on actor-network theory (Latour, 2005; Law & Hassard, 1999) and socio-material critique (Fenwick & Edwards, 2010; Fenwick & Landri, 2012), how do we demonstrate these complex communities, connections and relations between so many human and non-human actors, distributed across real and virtual networked spaces?
The starting point for #el30 is an open blog curated by Stephen Downes, presenting a course outline and week-by-week syllabus, supplemented by weekly videos recorded just-in-time featuring Downes and others. The course activity is focused on aggregating students' personal blog posts, and the commentary that ensues. The course feed aggregates just 22 blogs. It feels more familiar and authentic that the typical video-quiz-discussion structure of the standard MOOC. There's a high level of autonomy, and it does require a more proactive approach to consciously taking responsibility for your own learning. The sense of collegiality and community – perhaps because it is a specialised community of practice with a crucial shared interest (Lave & Wenger, 1991; Wenger, 1998) – feels strong, although the community is rather small. It also feels somewhat cliquey, as though everyone already knows everyone else.
Of course, these general comments only really account for the active participants though, even if [l]urking is a legitimate and valued for of participation.
Although not the sole – or even principal – method of delivery of #el30, I turned to social network analysis (SNA) to analyse Twitter conversations and community developing around the #el30 hashtag. A social network analysis of the hashtag reveals just 100 tweets and 20 retweets amongst 38 users across 11 related hashtags over the past week. Generated quickly (and somewhat crudely) using SocioViz, Figure 2 demonstrates relational connections between all users tweeting the #el30 hashtag. Figure 3 demonstrates connections between the 11 related hashtags.
While making visible, observable and quantifiable certain types of online behaviour, the distributed, connectivist structure of #el30 makes social and community interactions hard to locate and hard to study. How do we record, represent and visualise the complex networks of agential relations occurring between human and non-human actors… whether analysing Twitter conversations or blog comments or any other media facilitating online interaction? Looking at the surface analysis above, #el30 seems small, dislocated and disconnected, affectless and cold. Because it lacks living material warmth, a certain touch and affect which renders these interactions "emotional, affiliative and meaning-rich" (Kozinets, 2010, p. 28).
So how do we capture both the distance and the detail? How do we analyse the quantifiable surface (as I've done above) without losing the vast qualitative depth and richness of these agential relations? How do we read from a distance, but at the same time deeply, closely?