1. Analyzing Two Participation Strategies in an Undergraduate Course Community
Francisco Gutierrez
Gustavo Zurita
Sergio F. Ochoa
Nelson Baloian

2. How do users react to different participation strategies
How do users react to different participation strategies? Does it change the structures of community
Partially virtual communities usually suffer from a lack of user participation at their initial stages of their life cycle.
It turns out necessary to motivate people to contribute using different strategies that may help the community reach a minimum number of users and content, in order to ensure its sustainability over time.
How do users react to different participation strategies?
How do these strategies impact the structure of the community?

3. Outline Case Study Scenario
Measuring User Participation and Interaction
Analyzing the Network Structure
Conclusions and Future Work

4. Case Study Scenario Student interaction in an online discussion board
2 groups: active over a period of 15 weeks each
Group A (30 students): Quantity of contributions
Group B (48 students): Quantity and Quality of contributions
Students are grouped in three categories according to their participation level: HIGH, MEDIUM, LOW participation
3 milestones, placed monthly in a Competitive Scenario
Contribute with recent news found in diverse media related to the topics covered in the lecture sessions.
Students had to select news, cite their respective sources, and write a short personal opinion on it.
Once this contribution is made available in the platform, other students may rate and comment on the contribution.
Participation is measured in terms of one of the proposed strategies.
Community members are sorted and then categorized: High, Medium, Low (Cheng & Vassileva)
In each milestone: number of contributions, perceived quality by others, number of published comments, number of received comments.

5. Participation is measured in terms of one of the proposed metrics.
Community members are sorted and then categorized: High, Medium, Low (Cheng & Vassileva)

6. Participation is measured in terms of one of the proposed metrics.
Community members are sorted and then categorized: High, Medium, Low (Cheng & Vassileva)
Evaluation is enabled once a comment is posted

7. Participation Strategies
Group A:
Number of published articles (A)
Number of published comments (PC)
Group B:
Perceived quality (Q)
Number of received comments (RC)
P = A + PC
Differences: A priviledges the number of contributions made by the user while B the impact of the contribution in the community
P = A x Q/2 + RC

8. Modeling the Interaction Network
Alice
Bob
Author 3 1
Number of comments 4
Bob posted 4 to Charlie
While Charlie posted 1
to Bob
Software: iGraph (build the graph from data) + Gephi (network analysis + visualizations) open source libraries
Charlie

9. Relevant Metrics Interaction Network Structure Community Detection
Indegree
Weighted Indegree
Outdegree
Weighted Outdegree
Community Detection
Modularity
Indegree = number of edges that arrive to a node (# of students who write to the node)
Weighted indegree = number of edges that arrive, weighted by the number of comments (# of received comments)
Outdegree = number of edges that emerge from a node (# of students the node writes to)
Weighted outdegree = number of edges that emerge, weighted by the number of comments (# of published comments)
Modularity = division of the networks into subgroups (minimize intra-group distance, and maximize inter-group distance

10. Relevant Metrics Social Cohesion: 3-node motif distribution
There are 13 different isomorphic 3-node motifs representing the interaction patterns in triads in a directed graph.
7 of these triads are complete (they tend to form 3-cliques)
6 are partially complete (they represent the interaction of only 2 out of the 3 nodes)

11. Results – Participation Metrics
Group A
Weeks 1 - 5
Weeks
Weeks
Number of articles
11.31
6.41
12.17
Perceived quality
5.89 / 7.00
5.92 / 7.00
5.95 / 7.00
Comments
28.97
13.00
31.97
Perceived quality better in Group B: may be a positive response to the participation strategy
Number of published articles higher in Group A, even if there were more students in Group B
Group B
Weeks 1 - 5
Weeks
Weeks
Number of articles
3.20
7.15
12.41
Perceived quality
6.22 / 7.00
6.45 / 7.00
6.27 / 7.00
Comments
14.00
23.35
20.15

12. Results – Network Analysis
Group A (30 nodes)
Weeks 1 - 5
Weeks
Weeks
Edges
292
282
321
Average degree
9.73
9.40
10.70
Avg. weighted degree
17.03
25.13
34.53
Modularity
0.12
0.14
Group B (48 nodes)
Weeks 1 - 5
Weeks
Weeks
Edges
436
662
429
Average degree
9.08
13.79
8.94
Avg. weighted degree
13.42
22.38
19.31
Modularity
0.28
0.19
0.40
Despite the difference in the number of nodes, both average degree and weighted degree remain similar.
During the last 5 weeks: noticeable changes
Higher average weighted degree in Group A (snowball effect due to competitive participation) -> information overload
Higher modularity in Group B (the community tended to split into subgroups)

13. Results – Network Visualization
Group A (modularity = 0.14)
Group B (modularity = 0.40)
During weeks 11-15
Size of nodes = weigthed indegree
Node colors = modularity classes
Edges thickness = weigthed outdegree
Pink cluster in Group B  students who tended to form an independent subgroup

14. Results – Network Cohesion
Group A: the interaction patterns tended to close the group (motifs 12 – 13)
Group B: partially connected 3-node motifs (3, 7, 8)
By analyzing the histogram of 3-node motifs it is possible to get an overview of how well connected is a community
Alternative tool for monitoring the evolution of the community (the other one being the visualization)

15. Conclusions
In Group A, the community tended to follow a snowball effect regarding participation.
In Group B, the community tended to break into smaller subgroups
It is possible to analyze in real time the interaction patterns in a community and generate visualizations that help monitor how the community is evolving
Even if we observed some particular phenomena regarding the communities structures, we need to perform more repetition of this study in order to claim significant conclusions

16. Future Work
We are currently analyzing the participation and network metrics of a third group
Some variables to study in the future:
Anonymity and Pseudo-anonymity of contributors
Competitive vs Collaborative scenario
Understand the relationship between the activity of leaders (central nodes) and the structure of the community for a given participation strategy

17. Analyzing Two Participation Strategies in an Undergraduate Course Community