Personal Learning Environments are used more and more by the academic community. They can coexist with formal courses as a communication and collaboration channel. In this paper, an application of learning analytics into HOU2LEARN, a Personal Learning Environment set by Hellenic Open University is discussed. The present part of research focuses on the social network analysis as a branch of learning analytics, along with formal grading system. Since it is an ongoing research, this paper presents the preliminary results of the study of the correlation between the social network metrics and the formal grades, through a test case course, the PLH42.

1. Leuven, Belgium, 12/04/2013
LAK13
CONSIDERING FORMAL ASSESSMENT
IN LEARNING ANALYTICS WITHIN A
PLE: THE HOU2LEARN CASE
1 Eleni Koulocheri, Ph.D. Candidate
Michalis Xenos, Associate Professor
Hellenic Open University
School of Sciences and Technology

2. SITUATING THE WORK:
12/4/2013
The application of Learning Analytics in an
environment of Hellenic Open University
Eleni Koulocheri – HOU
Looking for methods to “exploit” students with
high impact.
The environment: HOU2LEARN
The techniques: Learning analytics with Social
@bekoul
Network Analysis (SNA) and activity metrics
The “formal” parameter: Formal grades 2

3. LEARNING ANALYTICS
12/4/2013
Siemens & Long five axes:
1. Course level: social network analysis (SNA),
Eleni Koulocheri – HOU
discourse analysis, learning trails.
2. Educational data mining: pattern recognition
and predictive modeling.
3. Intelligent curriculum: development of
semantically defined curriculum resources
4. Adaptive content: provision of adaptive content
@bekoul
using recommendation procedures, based on
learner behaviour
5. Adaptive learning: social interaction and 3
learner support as an adaptive learner process.

4. SNA IN A NUTSHELL
12/4/2013
 A methodological
analysis of social
networks.
Eleni Koulocheri – HOU
 Social network analysis
views social
relationships in terms
of network theory
 nodes (representing
individual actors within
the network)
 edges (which represent
@bekoul
relationships between the
individuals, such as
friendship, organizational
position, etc.) 4
 SNA metrics
 indegree/outdegree/betweeness centrality

5. HOU2LEARN:
OPEN EDUCATIONAL PLATFORM
12/4/2013
Users:
 M.Sc. Students of HOU (N=76)
 Main supportive environment for PLH42 Course, ’11-’12
Eleni Koulocheri – HOU
 Open to all who are interested in
Objectives:
 Openess promotion (content creation and sharing in a
stressless way)
 Communication enhancement in a less formal way (sharing
common interests)
 Socialization promotion among members endorsing ideas
@bekoul
and experiences exchange
Formalities:
 6 assignments 5
 1 final exams
http://hou2learn.eap.gr/

6. HOU2LEARN:
OPEN EDUCATIONAL PLATFORM
Set by Hellenic Open
University,
12/4/2013
since September 2010.
For research and
Eleni Koulocheri – HOU
educational purposes.
Based on Elgg Platform.
Supports:
•Informal Environment.
@bekoul
•Social Connections
•Material creation &
sharing.
6
http://hou2learn.eap.gr/

7. HOU2LEARN:
OPEN EDUCATIONAL PLATFORM
Informal
12/4/2013
Learning
Eleni Koulocheri – HOU
Learning 2.0
Learning 2.0
Learning 2.0
Social
networking
Networking
@bekoul
Networking
7
http://hou2learn.eap.gr/

8. HOU2LEARN:
PROFILING FEATURES
It covers all
12/4/2013
three aspects of
a social
network:
Eleni Koulocheri – HOU
Personal
Profile
Networking
Content
@bekoul
8
http://hou2learn.eap.gr/

9. HOU2LEARN:
NETWORKING FEATURES
It covers all
12/4/2013
three aspects of
a social
network:
Eleni Koulocheri – HOU
Personal
Profile
Networking
Content
@bekoul
9
http://hou2learn.eap.gr/

10. HOU2LEARN: CONTENT
SHARING/CREATING
It covers all
12/4/2013
three aspects of
a social
network:
Eleni Koulocheri – HOU
Personal
Profile
Networking
Content
@bekoul
10
http://hou2learn.eap.gr/

11. OUR RESEARCH…
Α. Social Network Analysis Software
12/4/2013
Eleni Koulocheri – HOU
B. HOU2LEARN database schema (PLH42).
@bekoul
11
http://hou2learn.eap.gr

12. METRICS DESIGN
12/4/2013
C. Metrics Design according the course needs and set up
 topics that each user has
uploaded on Group Discussion.
Eleni Koulocheri - HOU
 comments on topics of Group
Discussion
 new blogposts in Group Blog
 comments on blogposts in Group
Blog
 comments on wireposts of other
Group Members
 uploads of new files on Group
Files page
 comments on files uploaded by
other Group members
 new bookmarks in Group
 comments on bookmarks
uploaded by other Group 12
members

13. SOCIAL NETWORK ANALYSIS
12/4/2013
D. Social Network Analysis Diagram – Shot 1
Eleni Koulocheri – HOU
@bekoul
13
15/12/2011 - 22 Connections

14. SOCIAL NETWORK ANALYSIS
12/4/2013
D. Social Network Analysis Diagram – Shot 2
Eleni Koulocheri – HOU
@bekoul
14
18/03/2012 - 73 Connections

15. SOCIAL NETWORK ANALYSIS
12/4/2013
D. Social Network Analysis Diagram – Shot 3
Eleni Koulocheri – HOU
Node Groups
Arrangement
Algorithms Revealing
Algorithms
(i.e. Fruchterman
Reingold, Force (i.e. Wakita –
Atlas, etc.) Tsurumi, etc.)
@bekoul
15
16/05/2012 - 404 Connections

16. GRADES VS SNA BASIC METRICS (1/4)
12/4/2013
Eleni Koulocheri – HOU
Nodes’ size
@bekoul
proportional to the
(normalised) final
grades. 16

17. GRADES VS SNA BASIC METRICS (2/4)
Nodes’ size
12/4/2013
proportional to the
Indegree centrality
Eleni Koulocheri – HOU
i.e. the number of the total
number of connections
linked to a node.
It presents the number of
co-members a member is
followed by.
@bekoul
This metric, in fact,
measures the popularity of
17
the node.

18. GRADES VS SNA BASIC METRICS (3/4)
Nodes’ size
12/4/2013
proportional to the
Outdegree centrality
Eleni Koulocheri – HOU
i.e the number of edges
(arrows) that point
outward to other nodes.
It depicts the number of
members, a member
follows.
@bekoul
It counts the tension of the
node to be connected to
18
other nodes and to be
aware of their activities.

19. GRADES VS SNA BASIC METRICS (4/4)
12/4/2013
Nodes’ size
proportional to the
Betweeness centrality
Eleni Koulocheri – HOU
i.e. measures the
brokering capability
of a node.
It measures how much
removing a person
@bekoul
would disrupt the
connections between
other nodes in the
19
network.

20. PRELIMINARY RESULTS
12/4/2013
 The highest indegree centrality node had
lower grade than the average one.
Eleni Koulocheri – HOU
 The highest outdegree centrality node had one
of the highest grades and also had the highest
betweeness centrality.
 Students with high
@bekoul
grades have to increase
betweeness centrality.
20

21. FUTURE WORK
12/4/2013
 HOU2LEARN Running for 2nd year
 Further deployment of Learning Analytics
Eleni Koulocheri – HOU
 Further deployment of Metrics
 Combination of them
 Integration of experiments (assignments with
groups)
@bekoul
21

22. Ευχαριστώ!
ekoulocheri@eap.gr
@bekoul
Hellenic Open University 22