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SNA in technology-enhanced learning:
a shift to personal network perspective
using clustered graphs
International Seminar ...
Background
• Learning analytics: data mining, information visualization, and
social network analysis to help instructors b...
Context
• 2 online courses: “HR” and “RS”
• Distributed students (9 universities)
• Heterogeneous students (121 students b...
Methodology
• (*) Ego-networks can result from:
– Ego Network Research Design (ENRD)
• referred in this summer course as “...
Methodology
• How did we get alters and ties among them?
– Online data: all the interactions were afforded
through the lea...
Methodology
• How did we get alters’ attributes?
– From the student list
• Age
• Degree
• University
– From course data
• ...
Methodology
• We work with “relational events” instead of
“relational states”
• What type of network model should we use?
...
Methodology
• Two-mode network model for indirect relations
RESOURCE /
CONVERSATION
8
Methodology
• Projection: two-mode  one-mode
two-mode network one-mode network
(*) Images from http://toreopsahl.com 9
Methodology
• Clustered graphs at individual level
10
Easy to visualize and compare docens of
personal networks at a glance
Methodology
• Clustered graphs at group level
∑i = 1
N
i
N
=
Personal networks of N members
belonging to a group
1 2 3
i-1...
Methodology
– If you would like to use the clustered graphs method in your
research, you should read:
• Method (mathematic...
Methodology
• “Manual” process to collect and analyze data
13
GraphML
GraphML
GraphML
Methodology
• Automatic process to collect and analyze data
14
Analysis of the personal networks in
different learning environments
• Students enrolled in online courses have
– to revie...
Analysis of the personal networks in
different learning environments
• A learning environment that
– supports connections ...
Analysis of the personal networks in
different learning environments
• Research questions:
– Are there significant differe...
Analysis of the personal networks in
different learning environments
• Three meta-representations:
1. HR students
2. RS st...
Analysis of the personal networks in
different learning environments
• Three meta-representations
1. HR students
19
 clas...
Analysis of the personal networks in
different learning environments
• Individual personal networks (HR)
20
Moodle PLE
Analysis of the personal networks in
different learning environments
• Individual personal networks (RS)
21
PLEMoodle
Analysis of the personal networks in
different learning environments
• Individual personal networks (HR_RS)
PLE
22
Moodle
Analysis of the personal networks in
different learning environments
23
HR_RS
RS
Moodle PLE
the average number of alters i...
Analysis of the personal networks in
different learning environments
• Summary
– The average student using Moodle tend to ...
Analysis of the personal networks in
different learning environments
• The larger the size of the personal network, the
be...
Analysis of the differences between the personal
networks of high- and low-performing students
• Research questions by Daw...
Analysis of the differences between the personal
networks of high- and low-performing students
• Meta-representation
Compo...
Analysis of the differences between the personal
networks of high- and low-performing students
• Composition
HIGH
performe...
Analysis of the differences between the personal
networks of high- and low-performing students
• Composition
PLE
HR course...
Analysis of the differences between the personal
networks of high- and low-performing students
• Structure
HIGH
performers...
Analysis of the differences between the personal
networks of high- and low-performing students
• Intra-class density
PLE
H...
Analysis of the differences between the personal
networks of high- and low-performing students
• Inter-class density
PLE
H...
Limitations
• The interaction that is established by other private
means (e.g., email) was excluded
• Projection: two-mode...
SNA in technology-enhanced learning:
a shift to personal network perspective
using clustered graphs
International Seminar ...
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SNA in technology-enhanced learning: a shift to personal network perspective using clustered graphs

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SNA in technology-enhanced learning: a shift to personal network perspective using clustered graphs

  1. 1. SNA in technology-enhanced learning: a shift to personal network perspective using clustered graphs International Seminar on Personal Networks (2013) organized by The Personal Networks Laboratory (egolab-GRAFO), Department of Social and Cultural Anthropology, Autonomous University of Barcelona (UAB) Oskar Casquero Oyarzabal (oskar.casquero@ehu.es) Department of Systems Engineering and Automatic Control University of the Basque Country (UPV/EHU)
  2. 2. Background • Learning analytics: data mining, information visualization, and social network analysis to help instructors become aware of what is happening in the digital part of their classes. – Big data, MOOC, non formal learning • A shift from “whole network” to “personal network” perspective in educational literature. – Many blended or online courses do not work as traditional densely connected groups but as dispersed personal networks where students use a high variety of communication channels and shared spaces in order to connect and coordinate. • General research question: What type of (academic) personal networks do students develop when they interact online? 2
  3. 3. Context • 2 online courses: “HR” and “RS” • Distributed students (9 universities) • Heterogeneous students (121 students belonging to different degrees) • No pre-existing networks • Learning process entirely afforded by online interactions • Two groups in each course: – Moodle – Personal Learning Environment (PLE) based on iGoogle and Friendfeed • Around these two implementations, a diverse set of services: – forums – content management tools Blogger and Wikispaces – and digital resource repositories Delicious, Flickr, YouTube, Scribd and SlideShare 3
  4. 4. Methodology • (*) Ego-networks can result from: – Ego Network Research Design (ENRD) • referred in this summer course as “personal network research” Name generators, name interpreters, … – Full Network Research Design (FNRD) • Based on defined group (e.g.: a course, a department, ...) • Sub-networks of nodes and ties incident on the ego • For this research we use the FNRD approach (*) Borgatti, S. (2011). Personal networks and whole networks: a discussion. International Seminar on Personal Networks. Barcelona. 4
  5. 5. Methodology • How did we get alters and ties among them? – Online data: all the interactions were afforded through the learning environment (a set of web services) – Public interactions among students were mapped out from the web services using • database queries (e.g.: Moodle) • RSS feeds (e.g.: Blogger) • API queries (e.g.: Friendfeed) • screen scraping when no database, RSS or API interfaces are available (e.g.: Google Groups) 5
  6. 6. Methodology • How did we get alters’ attributes? – From the student list • Age • Degree • University – From course data • Academic performance • Participation – From an online survey at the end of the course • Sense of community (using Rovai’s scale) 6
  7. 7. Methodology • We work with “relational events” instead of “relational states” • What type of network model should we use? – What type of interactions do we have? • Direct interactions: those ones in which the sender and the receiver of a message can clearly and easily be identified (e.g., in a mail) • Indirect interactions: those that usually do not have a concrete addressee and that take place through a shared resource (e.g., a post in a blog, a message in a forum, an image in Flickr, a video in YouTube or an update in Facebook) 7
  8. 8. Methodology • Two-mode network model for indirect relations RESOURCE / CONVERSATION 8
  9. 9. Methodology • Projection: two-mode  one-mode two-mode network one-mode network (*) Images from http://toreopsahl.com 9
  10. 10. Methodology • Clustered graphs at individual level 10 Easy to visualize and compare docens of personal networks at a glance
  11. 11. Methodology • Clustered graphs at group level ∑i = 1 N i N = Personal networks of N members belonging to a group 1 2 3 i-1 i i+1 N-2 N-1 N […] […] The aggregated personal network describes the average composition and structure across all members of a group 11
  12. 12. Methodology – If you would like to use the clustered graphs method in your research, you should read: • Method (mathematical formulas for calculating the parameters of the clustered graphs): – Brandes, U., Lerner, J., Lubbers, M.J., Molina, J.L., & McCarty, C. (2008). Visual statistics for collections of clustered graphs. Paper presented at the 2008 IEEE Pacific Visualization Symposium, Kyoto, Japan. • Example application of the method: – Lubbers, M. J., Molina, J. L., Lerner, J., Brandes, U., Ávila, J., & McCarty, C. (2010). Longitudinal analysis of personal networks. The case of Argentinean migrants in Spain. Social Networks, 32(1), 91–104. • Visualization of the GraphML files (representing the clustered graphs) with Visone: – http://visone.info/wiki/index.php/Personal_networks_(tutorial) – But there is still a gap from the math to the GraphML files • NOTE: if you are following an ENRD and you are using EgoNet, EgoNet2GraphML fills this gap 12
  13. 13. Methodology • “Manual” process to collect and analyze data 13 GraphML GraphML GraphML
  14. 14. Methodology • Automatic process to collect and analyze data 14
  15. 15. Analysis of the personal networks in different learning environments • Students enrolled in online courses have – to review a great amount of learning materials – to participate in a great number of conversations and collaborative works • When the activities of a course are organized around external multiple services, all those learning materials, conversations and collaborative works get dispersed and become less observable by the students. • As a result, there is a risk of a drop in the potential connections and interactions the student could develop. 15
  16. 16. Analysis of the personal networks in different learning environments • A learning environment that – supports connections between students in the same course and in different courses, – integrates institutional and external services, – and places resources and conversations together under a single interface makes easier for that student to consider all the elements in the learning process. • Therefore, it is more likely that students will – use them, – connect with higher number of peers inside and outside their primary learning context, – and participate and give feedback with more frequency. 16
  17. 17. Analysis of the personal networks in different learning environments • Research questions: – Are there significant differences in network composition and structure between the students using a PLE and those using a Moodle? – Do the students using a PLE develop larger social networks than the students using a Moodle? 17
  18. 18. Analysis of the personal networks in different learning environments • Three meta-representations: 1. HR students 2. RS students 3. Students enrolled in both HR and RS (HR_RS) 1 2 3 18
  19. 19. Analysis of the personal networks in different learning environments • Three meta-representations 1. HR students 19  class “HR wg”: alters from the ego’s workgroup  class “HR”: alters from HR but who do not belong to the ego’s workgroup  class “RS”: alters from RS  class “HR_RS”: alters from HR_RS
  20. 20. Analysis of the personal networks in different learning environments • Individual personal networks (HR) 20 Moodle PLE
  21. 21. Analysis of the personal networks in different learning environments • Individual personal networks (RS) 21 PLEMoodle
  22. 22. Analysis of the personal networks in different learning environments • Individual personal networks (HR_RS) PLE 22 Moodle
  23. 23. Analysis of the personal networks in different learning environments 23 HR_RS RS Moodle PLE the average number of alters in a class with respect to the size of that class HR
  24. 24. Analysis of the personal networks in different learning environments • Summary – The average student using Moodle tend to focus his/her attention on the workgroup. – The average student using the PLE is more likely to maintain “cross-class” and “cross-course” exchanges. – PLE students develop larger networks than Moodle students. 24
  25. 25. Analysis of the personal networks in different learning environments • The larger the size of the personal network, the better the grade achieved by the student VLE PLE 25
  26. 26. Analysis of the differences between the personal networks of high- and low-performing students • Research questions by Dawson (2010) – Are there significant differences in personal network composition between high- and low-performing students? – Do high-performing students have larger personal networks than their low-performing peers? • The clustered graphs method allows us to pose a new research question: – Are there significant differences in personal network structure between high- and low-performing students? 26
  27. 27. Analysis of the differences between the personal networks of high- and low-performing students • Meta-representation Composition variables:  performance (high/low)  participation (high/low) 27
  28. 28. Analysis of the differences between the personal networks of high- and low-performing students • Composition HIGH performers LOW performers HIGH performers LOW performers PLE Moodle HR course RS course 28
  29. 29. Analysis of the differences between the personal networks of high- and low-performing students • Composition PLE HR course RS course r = .986 r = .976 r = .999 r = .990 29 Moodle
  30. 30. Analysis of the differences between the personal networks of high- and low-performing students • Structure HIGH performers LOW performers HIGH performers LOW performers PLE HR course RS course 30 Moodle
  31. 31. Analysis of the differences between the personal networks of high- and low-performing students • Intra-class density PLE HR course RS course r = .972 r = .980 r = .975 r = .986 31 Moodle
  32. 32. Analysis of the differences between the personal networks of high- and low-performing students • Inter-class density PLE HR course RS course r = .788 r = .673 r = .986 r = .966 32 Moodle
  33. 33. Limitations • The interaction that is established by other private means (e.g., email) was excluded • Projection: two-mode  one-mode – Problem: projection generates to many fully-connected cliques – Solution: to analyze the “nominal” network instead of the “reply” network ??? • Problem: this requires content analysis over the 3000 messages (posts + comments) that were generated… • No triangulation – Solution: • mixed data sources (online data + ¿online surveys?) • mixed methods (*) Images from http://toreopsahl.com 33
  34. 34. SNA in technology-enhanced learning: a shift to personal network perspective using clustered graphs International Seminar on Personal Networks (2013) organized by The Personal Networks Laboratory (egolab-GRAFO), Department of Social and Cultural Anthropology, Autonomous University of Barcelona (UAB) Oskar Casquero Oyarzabal (oskar.casquero@ehu.es) Department of Systems Engineering and Automatic Control University of the Basque Country (UPV/EHU) Thanks !!!

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