bayesian networks to predict reputation in virtual learning communities

Motiv. Def. Desi. Impl. Conclu. Biblio.

Bayesian Networks to Predict Reputation inVirtual Learning Communities

Luis Chamba-Eras A, B

Ana Arruarte and Jon Ander Elorriaga B

A Carrera de Ingenierıa en Sistemas - Universidad Nacional de Loja (UNL),Ecuador

B Computer Languages and Systems Department - University of the BasqueCountry (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 trustor reputation among members of a Virtual Learning Commu-nities (VLC): students and teachers. Any trust and reputationmodel have been implemented in the field of education, partic-ularly in the VLC using aggregation algorithms, to estimate thecapture of previous interactions of the members on resourcesor 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 interestin predicting future events, a BN in general is a relationshipsnetwork that uses statistical methods to represent probabilityrelationships between different nodes. It is a compact repre-sentation of the joint probability distribution to reason underuncertainty.

• 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 indifferent contexts, initially, it was defined as the extent to whichan 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 aboutsomething or somebody collected through indirect experiences.The reputation of a member of a VLC is the opinion that othermembers have on him. This opinion is based on the record ofpositive and negative interactions executed by them.

• Positive reinforcement, is the reward offered to the member ofthe VLC after performing a desired behavior, thereby determin-ing the presence of this reward increases the probability that abehavior will occur.

• Negative reinforcement, is the result offered to the memberafter 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 ofconsumers using aggregation algorithms, for example as typeconsumer 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 inwhich the information provided by community members flowsfreely between all of them while mid systems needs a third agentthat collects, stores, organizes and publishes. Examples of notmediated systems are recommendation letters such as those inLinkedIn, 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.

• Lopez-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 andit is a critical factor. Direct experience is obtained interact-ing with the ISs in the VLC. This is not always equal since inevery society there are different points of view. An aggrega-tion algorithm adapted to the VLC area, calculates the directexperience considering the interaction of members of the VLCwith resources and learning activities managed in an LMS. Con-cretely, the algorithm considers the ”I like” actions (positivereinforcement) and ”I don’t like” actions (negative reinforce-ment) that each member performs on the resources/activitiesused 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 memberis the opinion that the other members have on him. This opin-ion is based on the history of positive and negative interactionscarried out by them. People trust more in those individualsthat have higher affinity. This factor is useful when there islittle 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 negativereputation.

Figure 8: Estimation of positive and negative reputation for individualmember 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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https://goo.gl/dOd6gO



The prototype enables two options ”I like” or ”I don’t like” on the LAand LR, which will be evaluated by each member based objectivelyon the contribution of these to their learning.

Figure 9: Implementation of ”I like” or ”I don’t like” in the forum activityby direct experience

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When the member is on the VLC the ”trustmodel” block presentssummary information of the scores that the member has achieved.

Figure 10: Trust data based on the TM for each member in the VLC byreputation

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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 fortwo month.

4. Interaction and participate with members in VLC.

5. Recollected dataset about reputation factor in prototype usingthe ”trustmodel” block.

6. Comparison the dataset with the BN training values implementin BayesiaLab.

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Using real scenario in VLC

The values calculated by positive reputation compared to estimatesreputation using the BN with the software BayesiaLab, identifiedthat there are small differences between the values calculated withthe 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 andimplement 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 experiencebetween members of a VLC. It has been implemented in theMoodle LMS.

• In the work presented here the eBay aggregation algorithmadapted to the VLC area has been implemented. It calculatesthe direct experience considering the interaction of membersof the VLC with resources and learning activities managed inthe Moodle. The algorithm considers the behavioral Psychol-ogy with ”I like” actions as positive reinforcement and ”I don’tlike” 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 differentfeatures present in a VLC and combining the BN with other Ar-tificial Intelligence techniques as Natural Language Processingto identify through forums reputation.

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Outline

Motivations





Bibliography

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Bibliography

• Aguilar, J. (2015). Confianza y reputacion 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 Paıs 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.

• Garcıa, A. (Ed.), Ruiz, C. y Domınguez, F. (2007). De la educacion

a distancia a la educacion virtual. Barcelona: Ariel.

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(Creative Commons) BY-NC-SA

Thanks,Researchers: Luis Chamba-Eras, Ana Arruarte and Jon A. Elorriagaemail: [email protected], [email protected], [email protected] Group: Ga-Lan, http://galan.ehu.esTwitter: @lachamba

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mailto:[email protected]



http://galan.ehu.es

https://twitter.com/lachamba

bayesian networks to predict reputation in virtual learning communities

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