SNA - UNISA

Transcript

SNA - UNISA

Social Networks … online.
From web tracking to the analysis of
participation and performance in
collaborative web groups: the Cliques
Participation Index
[email protected]
SEFoRA Lab – Faculty of Psychology
Alma Mater Studiorum (University of Bologna)
Elvis
MAZZONI
Reti sociali ... in rete
Web 1.0 Æ 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?
1st 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).
Elvis
MAZZONI
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.
Elvis
MAZZONI
SNA Æ Relational data in web forum
2nd problem: how could we construct an adjacency matrix based on
web exchange? This question has an easy answer in e-mail …
Luca
Paola
Maria
Marco
Elvis
MAZZONI
2nd problem: but how could we answer in a sequential web forum ??
Elvis
MAZZONI
2nd problem: and in a threaded web forum ??
Elvis
MAZZONI
SNA Æ An empirical study

Vocational course in “Systems and Communities for Online Learning” –
Faculty of Educational Science – University of Florence.

153 subject Æ 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.
Elvis
MAZZONI
SLM Æ 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.
Elvis
MAZZONI
SNA Æ
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).
Elvis
MAZZONI
SNA Æ Cohesion Analysis
Starting from the previous considerations, we made the following
1st 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.
Elvis
MAZZONI
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.
Elvis
MAZZONI
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. 1-23.
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.
so the more the cliques are
>>
the better the quality of
the knowledge
construction process will be
Elvis
MAZZONI
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.
Elvis
MAZZONI
SNA Æ 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.
Elvis
MAZZONI
SNA Æ 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.
Elvis
MAZZONI
SNA Æ 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
CPI basso
Quantità
12
Rango
medio
U de
Mann-Whitney
9,21
32,500
CPI elevato
12
Sig.
0,022
15,79
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.