Analyzing Two Participation Strategies in an Undergraduate Course Community Francisco Gutierrez frgutier@dcc.uchile.cl Gustavo Zurita gzurita@fen.uchile.cl Sergio F. Ochoa sochoa@dcc.uchile.cl Nelson Baloian nbaloian@dcc.uchile.cl

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?

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

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.

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

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

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

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

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

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)

Results – Participation Metrics Group A Weeks 1 - 5 Weeks 6 - 10 Weeks 11 - 15 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 6 - 10 Weeks 11 - 15 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

Results – Network Analysis Group A (30 nodes) Weeks 1 - 5 Weeks 6 - 10 Weeks 11 - 15 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 6 - 10 Weeks 11 - 15 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)

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

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)

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

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

Analyzing Two Participation Strategies in an Undergraduate Course Community