the use of facebook and social network analysis (etf 2014)
TRANSCRIPT
The use of Facebook (FB) and Social
Network Analysis (SNA) to Predict
Students “At Risk” in Further
Education (FE)
Elaine Garcia@ela1negarc1a
About me
• Elaine Garcia
• Head of Blended Learning & Digital Development (currently)
• Plymouth College of Art
• Associate Lecturer in information systems / PhD student
• University of Plymouth
• Research interests: Web 2.0, teaching & learning, knowledge
management, social learning, CoP, blogs
What links me and Kevin Bacon?
BACKGROUND
Background
Background
Web 2.0 is becoming
increasingly popular amongst
students
Young people are forming
‘…. complex, invisible and
organic social networks.’
‘understanding how and why
students use the electronic social
network is important for
understanding how to build and
maintain relationships with
students and to increase retention
and success.’ (Amador & Amador, 2014)
81% of students have experience of
discussing course-related
problems on FB
59% say it is a reason to use FB
(Jong et al, 2014)
Strong and Weak Ties
A
BC
Six Degrees of Separation
Objectives
• To determine if open data from social networks (Facebook)
maintained by students online provide indicators of those
students who may be less likely to succeed or complete a
course of study.
• To investigate the manner in which analysis of Facebook and
SNA may provide information which will assist in the
development of teaching strategies and approaches to
classroom management which will result in improved
teaching and learning and greater success on course.
THE COURSE & STUDENTS
METHODOLOGY
Social Network Analysis (SNA)
• SNA is ‘the mapping and measuring of relationships and flows
between people, groups, organisations, computers, URLs, and
other connected information/knowledge entities’ (Orgnet, 2013)
• Allows researchers to visually see the network and make “sense” of
the data
• For this project only degree of centrality was considered
– Each individual is a node
– Nodes are connected and some will be the “hubs” of the network
• Used Open source SNA software - Gephi
Survey
• Used Survey Monkey
• Informed by past research
• 90 students were asked to participate
• 23 completed the survey – 25% return
RESULTS
Degree of Centrality
0
5
10
15
20
25
30
35
0 - 10 11 - 20 21 - 30 31 - 40 41 - 50
Tota
l Nu
mb
er
of
No
de
s (S
tud
en
ts)
Degree of Centrality
Degree of Centrality amongst nodes
Degree of separation
Total FB Friends
Age
Early Leavers
Gender
Attendance
Specific Early Leavers
Connected Leavers
Specific Early Leavers
The “Loners”
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
2 3 6 6 7 8 8 11 12 12 14 15 17 18 20 21 22 22 23 25 31 32 32Rat
ing
Degree Seperation within Network
I have achieved more in my studies because of Facebook
I have achieved more in my studies because of Facebook Linear (I have achieved more in my studies because of Facebook)
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
2 3 6 6 7 8 8 11 12 12 14 15 17 18 20 21 22 22 23 25 31 32 32
Rat
ing
Degree Seperation within Network
I like to ask questions in class
I like to ask questions in class Linear (I like to ask questions in class)
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
2 3 6 6 7 8 8 11 12 12 14 15 17 18 20 21 22 22 23 25 31 32 32Rat
ing
Degree Seperation within Network
I share the work I produced as part of my course on Facebook
I share the work I produced as part of my course on Facebook Linear (I share the work I produced as part of my course on Facebook)
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
2 3 6 6 7 8 8 11 12 12 14 15 17 18 20 21 22 22 23 25 31 32 32Rat
ing
Degree Seperation within Network
I would like my lecturers to be contactable through Facebook
I would like my lecturers to be contactable through Facebook Linear (I would like my lecturers to be contactable through Facebook)
DISCUSSION
Discussion
• Some students are “hubs” within the social network
• Total number of FB friends r
• Age a• Gender a• Attendance a
• Early Leavers a
• Cause or Effect ?
Research Model
Attendance
GenderAge
Early Leavers
Position in
Network
CONCLUSION
Conclusion
• Possible to identify students “at risk”
• Highlights need to include socialisation activities in course
• Benefits of using Facebook should be highlighted
• Lecturers shouldn’t make “friends” with students
• Trial needs to take this to identification before leaving
• Facebook not necessarily formal part of course
• “Hubs” can play a part in connecting the network
• Maximise weak ties
• Continue to test model – weighted centrality, betweeness centrality, closeness
centrality
• Compare with offline behaviour
• Track during the year (how to changes occur)
• Additional factors should be considered: Pathway, grades, etc
• Generalizability, reliability and validity
LETS TALK ABOUT KEVIN…
Six degrees of Kevin Bacon
Me!
Work with
Alan Lemin
Alan Lemin
Works with me
Worked with Brian Blagdon
Brian Blagdon
Worked with Alan Lemin
Worked with Alex Mackenzie
Alex Mackenzie
Worked with Brian Blagdon
Taught Charles Dance
Charles Dance
Taught by Alex Mackenzie
Worked with James McAvoy
James McAvoy
Worked with Charles Dance
Worked with Kevin Bacon
Kevin Bacon!
Worked with James McAvoy
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2
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5
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References / Picture Attribution
• Amador, P. & Amador, J. (2014) Academic advising via Facebook: Examining student help
seeking. Internet and Higher Education, No. 21, pp. 9-16.
• Jong, B., Lai, C., Hsia, Y., Lin, T. & Liao, Y. (2014) An exploration of the potential educational
value of Facebook, Computers in Human Behaviour, No. 32, pp201-211
• Orgnet (2013) Social Network Analysis: A brief introduction, Accessed 1st May 2014. Available at:
http://www.orgnet.com/sna.html
• Students in field: Kyle Spradley https://flic.kr/p/nE5xbX
• Facebook: MoneyBlogNewz https://flic.kr/p/92CvQF
• Gephi Logo http://en.wikipedia.org/wiki/File:Gephi-logo.png
• Kevin Bacon http://commons.wikimedia.org/wiki/File:KevinBacon07TIFF.jpg#file
• Charles Dance http://commons.wikimedia.org/wiki/File:Charles_Dance_%28July_2012%29.jpg
• James McAvoy http://commons.wikimedia.org/wiki/File:James_McAvoy.jpg