SEFoRA Lab Faculty of Psychology Alma Mater Studiorum (University of Bologna)

Transcription

1 Social Networks online. From web tracking to the analysis of participation and performance in collaborative web groups: the Cliques Participation Index SEFoRA Lab Faculty of Psychology Alma Mater Studiorum (University of Bologna)

2 Web 1.0 Web 2.0: the network relevance Why becoming a member of groups/communities?? Friendship, playing, social supporting, working and learning and constructing knowledge Question: About learning and knowledge construction, which is (if there is) the tie between the social activity made and the collective performance? 1 st problem: how we can collect data from web environment concerning collective activities?? A quantitative solution for collecting information about what a web group do is the Web Tracking. By the web tracking we can collect quantitative data about the individual actions made by a user during his/her navigation in a web systems (for ex. a web forum, a web platform, etc.). The sum of all the individual actions could be viewed as collective web activities of a specific group/community (Mazzoni, 2006).

3 Web 2.0: collective activities and performance An exhaustive analysis about group performance cannot be based only on individual indicators (messages opened, sent, received); the product of a group work derives from collective action of all the participants, so we need indicators that could collect collective action of groups/communities. This contribution starts from this reflection. For trying a possible answer to the initial question, we have analyze interaction between 26 adult web groups during a vocational online course performed with Synergeia platform.

4 SNA Relational data in web forum 2 nd problem: how could we construct an adjacency matrix based on web exchange? This question has an easy answer in Luca Paola Maria Marco

5 SNA Relational data in web forum 2nd problem: but how could we answer in a sequential web forum??

6 SNA Relational data in web forum 2nd problem: and in a threaded web forum??

7 SNA An empirical study Vocational course in Systems and Communities for Online Learning Faculty of Educational Science University of Florence. 153 subject 26 groups; Interaction by Synergeia platform; 2 initial phases of individual activities: Familiarization with the technology; Definition of personal aims. 3 phases of collective activities: Online socialization (generative phase); Collective design (constructive phase); Metacognitive Analysis. Activity results: projects elaboration, products conception, repository construction and problem solving and case study activities.

8 SLM Synergeia Log Miner Data coming from Synergeia web tracking (indicators of individual actions) are elaborated by Synergeia Log Miner SLM for obtaining: - SLM indicators: this indicators concern the productivity of a group and are automatically generated by SLM starting from tracked data (for ex. discussions depth (Wiley s index). - SNA matrix: for each group.

9 SNA Density, centralization and discussions depth of the web group Many studies concerning work psychology show that closed groups achieve a better productivity thanks to a better cooperation and to a good level of communication within these groups (Gergen e Gergen, 1990, pag. 403). Researches concerning the productivity in working group having different communicative structures show that a very central structure is more effective when groups have a lot of participant (Mulder e Stemerding, 1963) and when they are deal with easy tasks (Leavitt, 1951; Morrisette, 1966; Mulder, 1963). Instead, decentralized groups are better with complex tasks as, in this case, a solely central individual would be overwhelm by the quantity of information and responsibilities that he/she has to consider in his/her position (Gergen e Gergen, 1990; pag. 421).

10 SNA Cohesion Analysis Starting from the previous considerations, we made the following 1 st HYPOTHESIS: in small web groups having a complex objective to achieve, the high density (AD) and the decentralized structure (BC) will foster the discussions depth (pr4 and W), thanks to a better communication level and better management of the information.

11 SNA Cohesion Analysis Cohesion Indexes analyze the subgroups characterizing the principal group. In the SNA there are various level by which we can analyze this group feature: cliques, n-cliques, clan, n-clan,. In this study, we will focus on the cliques as index for measuring the groups cohesion. Cliques This index identify the number of subcomponent maximally closed (having the maximum index of density), composed by at least 3 subject, characterizing a network. We could think at cliques as the preferential neighborhood characterizing the network subjects, that is the neighbors with which they have contact with more probability rather than the others.

12 SNA Cohesion Analysis Aviv R., Zippy E., Ravid G., Geva A. (2003), Network Analysis of Knowledge Construction in Asynchronous Learning Networks, in Journal of Asynchronous Learning Networks (JALN), Vol. 7, n 3, p Study on the effects of different structures on the cohesion of a web forum in two different students web groups. Authors conclusions are: We found that in the structured ALN, the knowledge construction process reached a very high phase of critical thinking and developed cohesive cliques. We conclude that a well-designed ALN develops significant, distinct cohesion, and role and power structures lead the knowledge construction process to high phases of critical thinking. Consideration: the quantity of cliques represents, in some way, the richness of the discussions and of the different points of view characterizing the groups. the better the quality of so the more the cliques are >> the knowledge construction process will be

13 SNA Cohesion Analysis Cliques The performance of each group has been evaluated by two independent judges about the originality and the transferability of the products, using the following index: vpo = originality index ; vpt = transferability index. Starting from Aviv and other s considerations (2003), we draw the following hypothesis: If the high number of cliques could origins more possibilities for the comparison and more probability for meeting different points of view about the subject treated so it should also brings a substantial contribution to the richness of the argumentation proposed and thus to the quality of the groups performance in terms of originality of the final product.

14 SNA Cliques and Cliques Participation Index For testing the previous hypothesis, we could differentiate groups starting from the cliques quantity (high quantity and low quantity) and then using this distinction as factor for verifying the quality of groups performances (vpo and vpt indexes). Although the statistic correctness of this procedure, the cliques index is not completely convincing for testing the hypothesis: in fact, two groups could be characterized by a same number of cliques but having different dimensions. Further, the same two groups could be composed by different quantities of participant and this, evidently, will determine different levels of involvement in the subcomponent structuring the groups.

15 SNA Cliques e Cliques Participation Index The CPI represents a group dimension: in fact it is calculated by summing the participants composing the cliques of a group and then dividing this sum by the number of group participants. CPI = Sum of cliques participants Group subjects The CPI identifies the group mean involvement to the subcomponent, i.e. each group member in how many cliques is involved.

16 SNA Cliques Participation Index (CPI) hypothesis: Groups characterized by a high CPI achieve a better performance in terms of product originality vpo) rather than groups with a low CPI. Gruppi Quantità Rango medio U de Mann-Whitney Sig. CPI basso 12 9,21 CPI elevato 12 15,79 32,500 0,022 Outcome shows that group having a high CPI achieve a significantly better originality evaluation rather than groups with low CPI. This result points out the relevance of the preferential aggregation structure for the quality of collective activity.