1. Analyzing the students´
behavior and relevant
topics in virtual learning
communities
Llanos Tobarra, Antonio Robles-Gómez, Salvador Ros,
Roberto Hernández, Agustín C. Caminero
Computers in Human Behavior 31(2014) 659-669 , online
December (2013) JCR Q1
Departamento de Sistemas de Comunicación y Control
Universidad Nacional de Educación a Distancia (UNED)
{llanos,arobles,sros,roberto,accaminero}@scc.uned.es
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2. Outline
• Introduction
• Outcomes
2

3. Introduction
• UNED is a distance methodology
university.
• Need of some specific techniques
for monitoring and analysing the
information gathered by LMS.
• Learning Analytics is defined as:
The measurement, collection, analysis and
reporting of data about learners and
their contexts, for purposes of
understanding and optimising learning and3

4. Different Approaches
Type of Analytics
Educational data
mining
Academic Analytics
Who Benefits?
Course-level: social networks,
conceptual development,
discourse analysis, “intelligent
curriculum”
Learning Analytics
Level or Object of Analysis
Learners, faculty
Departmental: predictive
modeling, patterns of
success/failure
Institutional: learner profiles,
performance of academics,
knowledge flow
Learners, faculty
Administrators,
funders, marketing
Regional (state/provincial):
comparisons between systems
Funders,
administrators
National and International
National
governments,
education
authorities
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5. Learning Analytics Process
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6. Outline
• Introduction
• Outcomes
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7. Where do we focus?
• Forums
– Essential for negotation and exchange
of ideas.
– Collaborative learning
– High correlation of students
participation levels with positive
learning outcomes and knowledge
constructions
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8. Outcomes
• Provide and extensive analysis of the
student´s behaviour ia an on-line
learning community
• Propose a set of algorithms to
characterize in an automatic way the
most relevants topics of the
community
• How ?
– Students and faculty`interacction by
means of the messages in the forums have
been analyzed.
• Results
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9. Questions
• What are the students’ behavior
patterns during their interaction
and participation in the
asynchronous virtual discussion
forums of the virtual learning
community?
• What are the most relevant topics
and subtopics in the asynchronous
on-line discussion forums of the on-
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10. Input data
• Data from two academic years
2010-2011,2011-2012
• Forum Student
• Forum Activities 1-6
• Forum Activities 7-11
• Forum Faculty
• About 2000 messages
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11. Procedure
• Data collection and statistical
analysis
• Semantic analysis
• Calculation of stem networks
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12. Procedure
For each participant (Statistical
indicators)
• Number of published messages
• Number of replies
• Number of initiated conversations
• Number of initiated conversations
witout replies
• Number of conversations where the
participant has posted a mesage
• Number of forums where the
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13. Procedure
• Semantics
– Splitting message in basic tokens
– Remove stop-words
– Obtaim the token stem (Porter
algoritm)
– Calculate daily and global frecuencies
Apache Lucene Library, Snowball tool
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14. First Question
• What are the students’ behavior
patterns during their interaction
and participation in the
asynchronous virtual discussion
forums of the virtual learning
community?
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15. Student behaviour
modelling
• Students can be classified
depending of their pattern of
behaviour as:
– Producers
• Proactive
• Reactive
– Consumers
SIIE'12
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16. Second Question
• What are the most relevant topics
and subtopics in the asynchronous
on-line discussion forums of the online learning community?
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17. Topic Modelling Process
• The topics modelling process deals
with the detection of the most
relevant topics which are
employed in asynchronous
discussion forums of on-line
educational environments.
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18. Topic Dynamics
• First decomposition:
– Chatter topics, which are internally
driven, can be known as sustained
discussion topics. New thoughts on
chatter topics are published all days
at an educational community and some
members can react to previous ideas
posted.
– Spike topics, which are externally
induced, produce sharp rises in
postings.
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19. Topic Dynamics
• First decomposition:
– Chatter topics, which are internally
driven, can be known as sustained
discussion topics. New thoughts on
chatter topics are published all days
at an educational community and some
members can react to previous ideas
posted.
– Spike topics, which are externally
induced, produce sharp rises in
postings.
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20. Topic Dynamics (II)
• Second decomposition:
– Just spike. These topics have a very low
correlation with any chatter topic, but they are
very correlated to an external event, such as
congratulations for the new year or initial
introductions of participants. They are initially
inactive, although they become very active within
a particular time sub-window. After that, they
come back inactive.
– Spiky chatter. These topics have a high
correlation with a chatter level and,
additionally, they are very sensitive to external
events. The scores could be classified as a spiky
chatter subtopic due to its strong correlation
with the exam topic and its influence with an
external event (as the publication of the
participants’ scores is).
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– Mostly chatter. These topics are continuously

21. Selecting forum topics
• Three weigth functions
• Best fit Weighted frecuency
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22. Third Question
• Could they be characterized in an
automatic way?
• Two algoritms:
– One for mostly chatter
– Second spike chatter
• Results
– Topics and subtopics
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23. Example: topic modelling
result
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24. What else?
• Create Topic networks per Forum
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25. SIIE'12
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26. SIIE'12
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27. Thanks for your
attention!!!
¿any question?
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28. Topic Modelling: Chatter
SIIE'12
• The DumpTerms set
contains all terms
already detected as
irrelevant topics,
such as names or
surnames.
• Plural detection.
• Accumulated
frequency (f(ti)) is
computed for each
term.
• Then, they’re
ranked.
• As result we
obtained a set
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called Chatter.

29. Topic Modelling: Spikes
SIIE'12
• For each pair, ti
of T set and tj of
Chatter set, the
number of
appearances (si)
of both terms
together in any
message mk is
counted.
• Also, the
probability of
apparition of tj
given ti (cri) is
calculated.
• In case these
values are
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