Who’s Really Leading Your Online Classroom?

Digital collaboration tools have transformed the way students learn. Social bookmarking platforms, shared maps, and online discussion spaces have made it possible for students to build knowledge together — beyond the walls of a traditional classroom. But when students interact online, who is actually driving the flow of information? Is it really everyone, or do certain students quietly dominate the network?

This study uses Social Network Analysis (SNA) to map the interaction structure of a digital learning community in a Greek high school. (Exarchou, Klonari, Lambrinos, & Vaitis, 2017)

Background

: What participation counts can’t tell you

As digital literacy education expands globally, tools like Diigo and Google Earth have become common fixtures in project-based learning. The goal is straightforward: get students collaborating, sharing, and learning from each other online.

But most existing research only measures how often students participate. It counts posts, comments, and interactions — and stops there. What it misses is the structure behind those interactions: who sends information to whom, who the knowledge hubs are, and who sits on the periphery with few connections.

Without a structural view, educators can’t tell whether their collaborative classroom is truly inclusive — or whether the same few students are shouldering most of the intellectual work.

Key Idea

: Instead of counting activity, map its direction

Traditional studies ask: “How much did each student participate?” This study asks something different: “Who sent information to whom, and what does that network look like?”

Social Network Analysis represents each student as a node and each online interaction as a directional link — an arrow pointing from the student who shared or commented to the student who received it. From that network, two measures stand out:

Outdegree refers to the number of connections a student sends out — how actively they share with others. Indegree refers to the connections they receive — how often other students reference or respond to their work.

A student with high indegree is functioning as a knowledge anchor: their contributions draw responses from peers. High outdegree signals a student who actively feeds information into the network. Both roles matter for the health of a learning community.

Solution

To quantify student interaction in this digital learning project, the researchers applied Social Network Analysis using NetMiner 4.0.

Step 1. Data collection

Sixteen grade-12 students at a senior high school in Mytilene, Greece participated in a geography project called “Europe in our lives.” Students used Diigo to bookmark and annotate online resources, and Google Earth to collaborate on map-based tasks. Every online interaction between students — shares, comments, responses — was recorded and prepared as the dataset for network analysis.

Step 2. Network construction

All interactions were modeled as a directed network using NetMiner, with students as nodes and their interactions as directed links. The resulting network had a density of 0.488, meaning nearly half of all theoretically possible connections between students were actually realized. This indicates a genuinely active collaborative environment.

Step 3. Centrality analysis

The researchers calculated degree centrality (Degree Centrality) for each student, separating outdegree and indegree to distinguish information senders from information receivers. This allowed them to identify which students were driving the network and which were less connected.

Step 4. Identifying key students

Two students, St19 and St21, stood out: each recorded 58 to 59 total exchanges and an outdegree of 14 — the highest in the network. They were functioning as clear information hubs, actively pushing knowledge out to peers. A third student, St28, logged 34 exchanges but occupied a different structural position within the network.

Findings

The network reveals a learning community with a clear structure

The analysis confirms that digital collaboration classrooms are not flat. Some students act as hubs — generating and distributing knowledge across the network — while others remain at the edges. Students with high outdegree are proactively sharing; those with high indegree are producing content that peers find valuable enough to reference.

The density of 0.488 shows that this classroom’s collaboration was real and widespread. At the same time, the concentration of connections around certain students is a signal: educators should consider whether less-connected students need additional support or a nudge to become more active participants.

This study is significant because it moves beyond asking “did students participate?” and instead asks “how did participation flow?” For teachers and instructional designers, SNA offers a practical lens for building more equitable and effective learning communities.

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References

• Exarchou, E., Klonari, A., Lambrinos, L., & Vaitis, M. (2017). Digital literacy integration in educational practice: Creating a learning community through a geographic project in Mytilene senior high school, Greece. Review of International Geographical Education Online (RIGEO), 7(3), 293–314.