Exploring affiliation network models
as a collaborative
filtering mechanism in e-learning
Miguel-Angel Sicilia
Salvador Sánchez-Alonso
Leonardo Lezcano
Information Engineering Research Unit
Computer Science Dept., University of Alcalá

AOP relationships
People work with
Nowadays Learners interact
such services, for
learning through different
learning about a
experiences are kinds of services
particular objective
setup around like newsgroups
(e.g. topic,
activities. and chats.
competency).
This kind of relationship activity-objective-people (AOP) is the basic material
for the empirical analysis of social interaction through technology enhanced
learning.
Analyzing Methods
Qualitative analysis Quantitative analysis
• Computing of actual social
• Intensive effort from the tutors
interaction indicators
to categorize and examine each
• Help tutors in decision making
of the interventions
• Processes large amounts of
• Exposed to subjectivity of tutors
communication events
• Social Network Analysis

Social Network Analysis (SNA)
General purposes in e-learning
I. Hypothesis testing or exploratory studies aimed to finding
correlations
II. The summative assessment of learners
I. Re-configuring the learning environment or undertaking
other actions based on the analysis data
Concretely, we approach AOP data in the form of an affiliation
network, considering that learners’ participation in activities can be
used to detect groups of common interest. Also, modeling data in that
way, make it possible to devise different forms of “collaborative
filtering”
The usual interpretation of collaborative filtering is that of recommendations or
ranking of information. Here we adopt a more general position, considering
collaborative filtering as any course of action taken on the basis of the analysis of the
social network structure.

Affiliation Network
Two disjoint
Required
sets defining
Preconditions
a bipartite
network
Threads planned
time must be
Discussion threads
similar
(events)
Tutors or learners Participation should not
(actors) be made mandatory
Each thread must have a clear topic or
Undirected ties that affiliate actors
objective, distinguishable from the rest
with events
The above preconditions guarantee, to the extent possible, that participation in discussion is a
function of interest, so the more the learner contributes to discussing a topic, the more she/he
shows an interest in the topic, thus allowing for a form of quantitative indicator

Filtering participants
The affiliation network can be used to implement

different strategies for the definition of subgroups.
Identify groups that are close or distant in their

interests.
Turn student groups into effective teams

(Oakley, 2004)
Test structural equivalence,
Compute the participation of (actors that have similar relations
actors in each of the topics, to the others) with block
and then examine modelling technique, which
relationships with a provides a way of doing this with
hypergraph. the help of automated algorithms.

Blockmodeling technique
Processing Steps
 Remove tutors and nodes with
degree lower than two
 Randomize learners’ and topics’ order
 Set the number of partitions
depending on the number of learners
and topics (<6,6>)
Learners
 Apply Random Block Modeling
Main Features
 Able to detect different kind of
structures (e.g. cohesion, centrality)
 Allows exceptions or errors on input
data (e.g. Empirical data)
 Effective only for small dense
networks
Topics

Instructor-led on-the-fly filtering
Very active learners
that show low interest
in practical topics
(computer tools)
Partitions of learners
Combining different
with no significant
interests to foster discussion or
activity
combining the same interest to
better focus those discussions.
Combining more active and
Active learners that
more passive groups, or filter
show low interest in
out the latter.
theoretical issues
This group shows attention
In general there is less
only to introductory
interest from topics T5
issues on e-learning
onwards
Introduce
reinforcement
activities

Changing course structure
Re-organize structure joining or splitting topics.

Topics that are connected with a high strength

can be joined together, or even be separated in
another course.
Enhanced
Modularity
Concepts that are more peripheral might be

removed, separated or re-arranged for future
editions of the same learning experience.
Therefore we need to identify highly related topics to a given
intensity and we’ll get it with the help of m-slices.

m-Slices
One-Mode valued The larger the
network edge value
between two topics
the stronger or
more cohesive
the common
interest
4-slice
16-slice
33-slice
An m-slice
is a maximal
subnetwork
containing the lines
Colours show with a multiplicity
the nesting of equal to or greater
the slices. Yellow than m and the
ones are also red vertices incident
and red ones are with these lines.
m-slice are nested
also blue.

m-Slices
T4H2 and T4H4 are about
4-slice IMS LD and poorly related to
the rest, so it could be 33-slice is
16-slice
reasonable to separate LD cohesive group of
contents to a second part of interest that
33-slice
the course includes the three
Introduction Topics,
so they could be
joined together
T6 is about IEEE
LOM and it is
closely related to
the rest.

Conclusions
Development of mathematical,
Use of affiliation models
for exploring on-line quantitative techniques for
filtering the environment
interaction in e-learning
Because there aren’t clear-cut thresholds for
automated structure settings, tutor should
take described techniques as an indicator to
aid in decision making over the learning
process
Further Work
 Evaluate indicators, regarding AOP data and their potential usages.
 Gather evidence to turn them into standard facilities in e-learning
platforms.
 Provide an advanced tool for the analysis of social interaction.