Bayesian Networks to Predict Reputation in Virtual Learning Communities

Motiv. Def. Desi. Impl. Conclu. Biblio.
Bayesian Networks to Predict Reputation in
Virtual Learning Communities
Luis Chamba-Eras A, B
Ana Arruarte and Jon Ander Elorriaga B
A Carrera de Ingenier´ıa en Sistemas - Universidad Nacional de Loja (UNL),
Ecuador
B Computer Languages and Systems Department - University of the Basque
Country (UPV/EHU), Ga-Lan Group, Spain
”2016 IEEE Latin American Conference on Computational Intelligence”
Cartagena-Colombia: 2-4 November 2016
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Outline
Motivations
Deﬁnitions and related work
Design of the Bayesian Network
Implementation of prototype
Conclusions and future work
Bibliography
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Outline
Motivations
Bibliography
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Motivations
• With the Internet Of Things and the support given by the In-
formation and Communication Technologies, real time partic-
ipation and collaboration between individuals in diﬀerent geo-
graphical locations is a reality in e-learning.
• Currently there is great interest in predicting the indirect trust
or reputation among members of a Virtual Learning Commu-
nities (VLC): students and teachers. Any trust and reputation
model have been implemented in the ﬁeld of education, partic-
ularly in the VLC using aggregation algorithms, to estimate the
capture of previous interactions of the members on resources
or activities value reputation.
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This is the reality!
• ”I like” or ”I don’t like”
Figure 1: Sites that implement social computing
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Outline
Motivations
Bibliography
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Preliminary concepts
• Bayesian Networks (BN), known as probabilistic models or be-
lief networks, have been investigated due to a growing interest
in predicting future events, a BN in general is a relationships
network that uses statistical methods to represent probability
relationships between different nodes. It is a compact repre-
sentation of the joint probability distribution to reason under
uncertainty.
• Virtual Learning Communities (VLC), enabling members to pro-
duce knowledge, resulting from social interaction in a collabo-
rative learning process.
• Trust, concept is complex, so there are multiple definitions in
different contexts, initially, it was defined as the extent to which
an individual has confidence and is willing to interact with some-
one based on words, actions and decisions of others.
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• Reputation, it is defined as the opinion that someone has about
something or somebody collected through indirect experiences.
The reputation of a member of a VLC is the opinion that other
members have on him. This opinion is based on the record of
positive and negative interactions executed by them.
• Positive reinforcement, is the reward offered to the member of
the VLC after performing a desired behavior, thereby determin-
ing the presence of this reward increases the probability that a
behavior will occur.
• Negative reinforcement, is the result offered to the member
after the appearance of unwanted conduct.
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• Online reputation, mid systems and unmediated. The mid sys-
tems, from simple systems are the spaces type of review of
consumers using aggregation algorithms, for example as type
consumer review sites: Yelp, Amazon, eBay, TripAdvisor, Re-
alSelf.com, Menelaus, IMD, among others, or, to complex sys-
tems ratings as Moody. The unmediated systems are those in
which the information provided by community members ﬂows
freely between all of them while mid systems needs a third agent
that collects, stores, organizes and publishes. Examples of not
mediated systems are recommendation letters such as those in
LinkedIn, StackOverﬂow, reports Infojob, word of mouth net-
works such as Facebook or forums.
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Related work
Related work combining BN, trust and reputation:
• Trang Nguye et al.
• L´opez-Faican et al.
• Daniel et al.
• Qi et al.
• Li et al.
• Jøsang et al.
• Patel et al.
• Aciar et al.
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Outline
Motivations
Bibliography
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Previous deﬁnitions
Figure 2: Acronyms of the context
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Factors: direct experience and reputation
• Direct experience (F1): it is based on satisfaction concept and
it is a critical factor. Direct experience is obtained interact-
ing with the ISs in the VLC. This is not always equal since in
every society there are diﬀerent points of view. An aggrega-
tion algorithm adapted to the VLC area, calculates the direct
experience considering the interaction of members of the VLC
with resources and learning activities managed in an LMS. Con-
cretely, the algorithm considers the ”I like” actions (positive
reinforcement) and ”I don’t like” actions (negative reinforce-
ment) that each member performs on the resources/activities
used and managed by the LMS.
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Factors: direct experience and reputation
• Reputation (F2): in our proposal reputation factor (F2) is cal-
culated from past interactions achieved through direct experi-
ence (F1) between ISs in the VLC. The reputation of a member
is the opinion that the other members have on him. This opin-
ion is based on the history of positive and negative interactions
carried out by them. People trust more in those individuals
that have higher aﬃnity. This factor is useful when there is
little previous direct experience between the IS in the VLC.
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Bayesian Network Model
Figure 3: BN factor by reputation of the TM
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Formalization Bayesian Network
Bayes Theorem:
Figure 4: Bayes Theorem
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Initial training values: estimates for positive/negative reputation
Figure 5: Training values BN
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Initial training values: positive reputation estimated values
Figure 6: Positive reputation estimated values
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Initial training values: negative reputation estimated values
Figure 7: Negative reputation estimated values
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BN obtains for each member the probability of positive and negative
reputation.
Figure 8: Estimation of positive and negative reputation for individual
member in VLC
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Outline
Motivations
Bibliography
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Implementation details in Moodle
• LMS Moodle 2.8.2.
• Moodle Core, new modules and plugins.
• Implement the direct experience and reputation.
• Show resources: https://goo.gl/dOd6gO
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The prototype enables two options ”I like” or ”I don’t like” on the LA
and LR, which will be evaluated by each member based objectively
on the contribution of these to their learning.
Figure 9: Implementation of ”I like” or ”I don’t like” in the forum activity
by direct experience
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When the member is on the VLC the ”trustmodel” block presents
summary information of the scores that the member has achieved.
Figure 10: Trust data based on the TM for each member in the VLC by
reputation
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Using real scenario in VLC
The prototype in real scenario for VLC, the process was as follows:
1. Start and management the virtual community for learning ”Math-
ematical Software” in Web.
2. 24 members enrolled in VLC, two teachers and 22 students.
3. Teachers tutoring and sharing activities/resources in VLC for
two month.
4. Interaction and participate with members in VLC.
5. Recollected dataset about reputation factor in prototype using
the ”trustmodel” block.
6. Comparison the dataset with the BN training values implement
in BayesiaLab.
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Using real scenario in VLC
The values calculated by positive reputation compared to estimates
reputation using the BN with the software BayesiaLab, identiﬁed
that there are small diﬀerences between the values calculated with
the prototype and BayesiaLab.
Figure 11: Comparative reputation result
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Outline
Motivations
Bibliography
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• The main contributions of this paper are the development and
implement the BN for predicting reputation of members of VLC.
• In this paper, we present the design of a BN that predicts repu-
tation from past interactions achieved through direct experience
between members of a VLC. It has been implemented in the
Moodle LMS.
• In the work presented here the eBay aggregation algorithm
adapted to the VLC area has been implemented. It calculates
the direct experience considering the interaction of members
of the VLC with resources and learning activities managed in
the Moodle. The algorithm considers the behavioral Psychol-
ogy with ”I like” actions as positive reinforcement and ”I don’t
like” actions as negative reinforcement.
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• In our work the past interaction is gathered from direct experi-
ence factor and use to predict value reputation factor.
• As future work, consider the views of members with diﬀerent
features present in a VLC and combining the BN with other Ar-
tiﬁcial Intelligence techniques as Natural Language Processing
to identify through forums reputation.
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Outline
Motivations
Bibliography
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Bibliography
• Aguilar, J. (2015). Confianza y reputación en Sistemas Multi-Agentes.
Universidad de los Andes.
• Chamba-Eras, L. (2011). Modelo de Confianza para Objetos de
Aprendizaje en Comunidades Virtuales. Master Thesis. Universidad
del Pa´ıs Vasco.
• Esfandiari, B. and Chandrasekharan, S. (2001). On how agents make
friends: Mechanisms for trust acquisition. In Proceedings of the
Fourth Workshop on Deception, Fraud and Trust in Agent Societies,
pages 27-34, Montreal, Canada.
• Gambetta, D. (1990). Trust: Making and Breaking Cooperative Rela-
tions, chapter Can We Trust Trust?, pages 213-237. Basil Blackwell,
Oxford.
• Garc´ıa, A. (Ed.), Ruiz, C. y Dom´ınguez, F. (2007). De la educación
a distancia a la educación virtual. Barcelona: Ariel.
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(Creative Commons) BY-NC-SA
Thanks,
Researchers: Luis Chamba-Eras, Ana Arruarte and Jon A. Elorriaga
email: lachamba@unl.edu.ec, a.arruarte@ehu.eus, jon.elorriaga@ehu.eus
Research Group: Ga-Lan, http://galan.ehu.es
Twitter: @lachamba
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