Editing Social network analysis (section)

===In computer-supported collaborative learning===
One of the most current methods of the application of SNA is to the study of [[computer-supported collaborative learning]] (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication.<ref name=":0">{{Cite journal|last1=Laat|first1=Maarten de|last2=Lally|first2=Vic|last3=Lipponen|first3=Lasse|last4=Simons|first4=Robert-Jan|date=2007-03-08|title=Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis|journal=International Journal of Computer-Supported Collaborative Learning|language=en|volume=2|issue=1|pages=87–103|doi=10.1007/s11412-007-9006-4|s2cid=3238474}}</ref> Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network.<ref name=":0" /> When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own.

====Key terms====
There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: '''density''', '''centrality''', '''indegree''', '''outdegree''', and '''sociogram'''.
* '''Density''' refers to the "connections" between participants.  Density is defined as the number of connections a participant has, divided by the total possible connections a participant could have.  For example, if there are 20 people participating, each person could potentially connect to 19 other people. A density of 100% (19/19) is the greatest density in the system.  A density of 5% indicates there is only 1 of 19 possible connections.<ref name=":0" />
* '''Centrality''' focuses on the behavior of individual participants within a network.  It measures the extent to which an individual interacts with other individuals in the network.  The more an individual connects to others in a network, the greater their centrality in the network.<ref name=":0" /><ref name=":1" />

In-degree and out-degree variables are related to centrality.
* '''In-degree''' centrality concentrates on a specific individual as the point of focus; centrality of all other individuals is based on their relation to the focal point of the "in-degree" individual.<ref name=":0" />
* '''Out-degree''' is a measure of centrality that still focuses on a single individual, but the analytic is concerned with the out-going interactions of the individual; the measure of out-degree centrality is how many times the focus point individual interacts with others.<ref name=":0" /><ref name=":1" />
* A '''sociogram''' is a visualization with defined boundaries of connections in the network. For example, a sociogram which shows out-degree centrality points for Participant A would illustrate all outgoing connections Participant A made in the studied network.<ref name=":0" />

====Unique capabilities====
Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers. This particular method allows the study of interaction patterns within a [[Networked learning|networked learning community]] and can help illustrate the extent of the participants' interactions with the other members of the group.<ref name=":0" /> The graphics created using SNA tools provide visualizations of the connections among participants and the strategies used to communicate within the group. Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.<ref>{{cite book |doi=10.4324/9780203763865-71 |chapter=Patterns of Interaction in Computer-supported Learning: A Social Network Analysis |title=International Conference of the Learning Sciences |year=2013 |pages=346–351 |isbn=9780203763865 }}</ref>

A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence,<ref name=":0" /> a greater regard for the recommendations of "central" participants,<ref>{{cite journal |last1=Martı́nez |first1=A. |last2=Dimitriadis |first2=Y. |last3=Rubia |first3=B. |last4=Gómez |first4=E. |last5=de la Fuente |first5=P. |title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions |journal=Computers & Education |date=December 2003 |volume=41 |issue=4 |pages=353–368 |doi=10.1016/j.compedu.2003.06.001 |citeseerx=10.1.1.114.7474 |s2cid=10636524 }}</ref> infrequency of cross-gender interaction in a network,<ref>{{cite conference|author1=Cho, H.|author2=Stefanone, M.|author3=Gay, G|name-list-style=amp|year=2002|title=Social information sharing in a CSCL community|conference=Computer support for collaborative learning: Foundations for a CSCL community|location=Hillsdale, NJ|publisher=Lawrence Erlbaum|pages=43–50|citeseerx=10.1.1.225.5273}}</ref> and the relatively small role played by an instructor in an [[asynchronous learning]] network.<ref>{{cite journal|author1=Aviv, R.|author2=Erlich, Z.|author3=Ravid, G.|author4=Geva, A.|name-list-style=amp|year=2003|title=Network analysis of knowledge construction in asynchronous learning networks|journal=Journal of Asynchronous Learning Networks|volume=7|issue=3|pages=1–23|citeseerx=10.1.1.2.9044}}</ref>

====Other methods used alongside SNA====
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,<ref name=":0" /> researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL. The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL.<ref>{{Cite book|title=Groupware: Design, Implementation, and Use|last1=Daradoumis|first1=Thanasis|last2=Martínez-Monés|first2=Alejandra|last3=Xhafa|first3=Fatos|chapter=An Integrated Approach for Analysing and Assessing the Performance of Virtual Learning Groups |date=2004-09-05|publisher=Springer Berlin Heidelberg|isbn=9783540230168|editor-last=Vreede|editor-first=Gert-Jan de|series=Lecture Notes in Computer Science|volume=3198 |pages=[https://archive.org/details/unset0000unse_i0a6/page/289 289–304]|language=en|doi=10.1007/978-3-540-30112-7_25|editor-last2=Guerrero|editor-first2=Luis A.|editor-last3=Raventós|editor-first3=Gabriela Marín|hdl=2117/116654|s2cid=6605 |chapter-url-access=registration|chapter-url=https://archive.org/details/unset0000unse_i0a6/page/289}}</ref> Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.<ref name=autogenerated1/>

A number of research studies have combined other types of analysis with SNA in the study of CSCL. This can be referred to as a multi-method approach or data [[Triangulation (social science)|triangulation]], which will lead to an increase of evaluation [[Reliability (statistics)|reliability]] in CSCL studies.
* Qualitative method – The principles of qualitative case study research constitute a solid framework for the integration of SNA methods in the study of CSCL experiences.<ref>{{Cite journal|last=Johnson|first=Karen E.|date=1996-01-01|title=Review of The Art of Case Study Research|jstor=329758|journal=The Modern Language Journal|volume=80|issue=4|pages=556–557|doi=10.2307/329758}}</ref>
** ''[[Ethnography|Ethnographic]] data'' such as student questionnaires and interviews and classroom non-participant observations<ref name=autogenerated1>{{cite journal|author1=Martínez, A.|author2=Dimitriadis, Y.|author3=Rubia, B.|author4=Gómez, E.|author5=de la Fuente, P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education. Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474|s2cid=10636524 }}</ref>
** ''[[Case study|Case studies]]'': comprehensively study particular CSCL situations and relate findings to general schemes<ref name=autogenerated1 />
** ''[[Content analysis]]:'' offers information about the content of the communication among members<ref name=autogenerated1 />
* Quantitative method – This includes simple descriptive statistical analyses on occurrences to identify particular attitudes of group members who have not been able to be tracked via SNA in order to detect general tendencies.
** ''Computer [[Logfile|log files]]:'' provide automatic data on how collaborative tools are used by learners<ref name=autogenerated1 />
** ''[[Multidimensional scaling|Multidimensional scaling (MDS)]]'': charts similarities among actors, so that more similar input data is closer together<ref name=autogenerated1 />
** ''[[Software]] tools:'' QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST<ref name=autogenerated1 />