Affiliation networks as a collaborative filtering mechanism in elearning
Exploring affiliation network models
as a collaborative
filtering mechanism in e-learning
Information Engineering Research Unit
Computer Science Depf. , University of Alcalé
AOP i‘: -:| c-iilonships
People work with
such services, for
learning about a
kinds of services
(e. g. topic,
This kind of relationship activity-objective-people (AOP) is the basic material
for the empirical analysis of social interaction through technology enhanced
- Intensive effort from the tutors
to categorize and examine each
of the interventions
- Exposed to subjectivity of tutors
Qualitative analysis J L Quantitative analysis
- Computing of actual social
- Help tutors in decision making
- Processes large amounts of
«- Social Network Analysis
Social Network Analysis (SNA)
General purposes in e-learning
I. Hypothesis testing or exploratory studies aimed to finding
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
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.
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TW° ‘“‘.1°. "‘* j:7 :7 ; , t 1 a 7?7"~~3* i Required
sets defining , T w . ; r — _ - i r _; V : __ P d. t.
a '~ ’. .«u<: > 7:’ V, ’ ' 7 if t " ' < :1, ' thith — ' >"« ~—1ZI~'- I
network T --
. , . T t _ Threads planned
»| EbiS: » A , » - rso. -. _
Discussion threads f ‘ T T ‘ . t'me, m.ust be
(events) T l‘ i. 5'”‘''a'
M ‘ ‘Q h ‘I . .
Tutors or learners , _ 7 Participation should not
0' (actors) "‘~"*r: :,. -= . m_, W= : » be made mandatory
1 Undirected ties that afﬂliate actors Each thread m“5t have a dear t°pi° °'
with events objective, distinguishable from the rest
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
0 The affiliation network can be used to implement
different strategies for the deﬁnition of subgroups.
0 Identify groups that are close or distant in their
0 Turn student groups into effective teams
Compute the participation of
actors in each of the topics,
and then examine
relationships with a
Test structural equivalence,
(actors that have similar relations
to the others) with block
modelling technique, which
provides a way of doing this with
the help of automated algorithms.
i g ‘i'€Cl'li’i| C;UC-I
V Remove tutors and nodes with
degree lower than two
V Randomize learners’ and topics’ order
V Set the number
depending on the number of learners
and topics (<6
V Apply Random Block Modeling
Able to detect different
meme. mm. _
" kind of
structures (e. g. cohesion, centrality)
" Allows exceptions or errors on input
data (e. g. Empirical data)
.0. w s
cw t . l
cl P. D.
F. n 0
Insiruc ‘oi'—| :-: c5 on—rn
Very active learners
that show low interest
in practical topics
interests to foster discussion or
combining the same Interest to
better focus those discussions.
“Combining more active and
more passive groups, or ﬁlter
out the latter.
This group shows attention
only to Introductory
issues on e-learning
Partitions of learners
with no signiﬁcant
Active learners that
show low interest in
In general there is less
interest from topics T5
Changing course s'ii'uc: "i‘ure
O Re-organize structure joining or splitting topics.
0 Topics that are connected with a high strength
can be joined together, or even be separated in
0 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 gel it with the help of m-slices.
One-Mode valued The larger the
network 0 edge value
between two topics
the stronger or
. . . . An m-sIice
2; f 7’ ‘ is a maximal
I ' subnetwork
xx‘ containing the lines
Colours show ‘- with a multiplicity
the nesting of 9 equal to or greater
the slices. Yellow 77 than m and the
ones are also red vertices incident
and red ones are m_s| ice are nested with these lines.
T6 is about IEEE
LOM and it is
closely related to
T4H2 and T4H4 are about
IMS LD and poorly related to
the rest, so it could be 33-slice is
reasonable to separate LD cohesive group of
contents to a second part of interest that
the Course includes the three
so they could be
Use of afﬁliation models Development of mathematical,
for exploring on-line ------------ '- quantitative techniques for
interaction in e-learning filtering the environment
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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
»' Further Work
V Evaluate indicators, regarding AOP data and their potential usages.
V Gather evidence to turn them into standard facilities in e-learning
'/ Provide an advanced tool for the analysis of social interaction. /v