The document summarizes research on using Facebook and social network analysis to predict which students may be at risk of not completing a further education course. A study was conducted analyzing the Facebook networks and survey responses of 90 students. Key findings included identifying "hub" students with many connections, and correlations between centrality in the network and factors like gender, age, attendance, and early departure. The conclusion recommends continued testing of this model to help identify at-risk students earlier and maximize weak social ties.

1. The use of Facebook (FB) and Social
Network Analysis (SNA) to Predict
Students “At Risk” in Further
Education (FE)
Elaine Garcia
@ela1negarc1a
lainey@elainegarcia.uk

2. 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

3. What links me and Kevin Bacon?

4. BACKGROUND

5. Background

6. Background
Web 2.0 is becoming
increasingly popular amongst
students

8. Young people are forming
‘…. complex, invisible and
organic social networks.’

9. ‘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)

10. 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)

11. Strong and Weak Ties
A
BC
Six Degrees of Separation

12. 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.

13. THE COURSE & STUDENTS

22. METHODOLOGY

23. 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

24. Survey
• Used Survey Monkey
• Informed by past research
• 90 students were asked to participate
• 23 completed the survey – 25% return

25. RESULTS

26. Degree of Centrality
0
5
10
15
20
25
30
35
0 - 10 11 - 20 21 - 30 31 - 40 41 - 50
TotalNumberofNodes(Students)
Degree of Centrality
Degree of Centrality amongst nodes

27. Degree of separation

28. Total FB Friends

29. Age

30. Early Leavers

31. Gender

32. Attendance

33. Specific Early Leavers
Connected Leavers

34. Specific Early Leavers
The “Loners”

35. -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 32
Rating
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)

36. -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
Rating
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)

37. -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 32
Rating
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)

38. -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 32
Rating
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)

39. DISCUSSION

40. 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 ?

41. Research Model
Attendance
Gender
Age
Early Leavers
Position in
Network

42. CONCLUSION

43. 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

44. LETS TALK ABOUT KEVIN…

45. 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
1
2
3 4
5
6

46. Contact Details
Elaine Garcia
lainey@elainegarcia.uk
@ela1negarc1a

47. 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