Some researchers have developed relevant and diverse proposals for improving the content quality of the learner model in Intelligent Tutoring Systems, mainly reducing its uncertainty. Following this aim, this paper proposes an open learner modeling approach using Bayesian networks, focusing on negotiation mechanism to solve detected cognitive conflicts that can emerge when the learner inspects information of his model inferred by the system. Therefore, we addressed some issues concerning the provision of inspectable model and negotiated updating of this model. Its contribution lies in the fact that the learners attempt to change the learner model is met with a challenge, leading to a decision if the learner claims to know more (or less) than the model represents.

1. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
An Open and Inspectable Learner Modeling with a
Negotiation Mechanism to Solve Cognitive Conflicts
in an Intelligent Tutoring System
Evandro Costa, Priscylla Silva,
Jonathas Magalh˜es and Marlos Silva
a
TIPS Group
Computing Institute
Federal University of Alagoas, Brazil
Federal University of Campina Grande, Brazil
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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2. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Research Context
Learner modeling tasks in ITS;
High level of uncertainty;
Probabilistic Learner Modeling in ITS;
Opening and Viewing Learner Model;
Presence of Cognitive Conflicts;
Mechanisms for dealing with conflicts;
Negotiating the open learner model.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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3. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Research Questions
Q1 : What approach should we adopt to deal with uncertainty
found in a learner model for ITS?
Q2 : What is an appropriate way to define and viewing OLM?
Q3 : How can we detect cognitive conflicts between the student
and the system concerning problem solving activities?
Q4 : How can we effectively address these conflicts?
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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4. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
How those Questions have been addressed?
With respect to Q1 – Representation and Maintenance:
Conati et al. [2];
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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5. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
How those Questions have been addressed?
With respect to Q2 – OLM and Visualization:
Zapata and Greer [5];
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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6. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
How those Questions have been addressed?
With respect to Q3 and Q4 – Conflicts detection and Negotiation:
Bull et al. [1];
Dimitrova [3];
Thomson and Mitrovic [4].
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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7. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Our General Approach
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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8. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
The Open Learner Model
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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9. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
The Open Learner Model
1 4
The system put a problem to the learner: 3 + 3;
He declares his belief:
Very unsure = 0.05;
Unsure = 0.25;
Almost sure = 0.5;
Sure = 0.75;
Very sure = 0.95.
Then, he submits a solution and the system evaluate it and
returns a grade [0,1].
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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10. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
The Open Learner Model
(a) The Ms . (b) The Mt .
Figure: Task-specific part of the Learner Model.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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11. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
The Open Learner Model
(a) The Ms . (b) The Mt .
Figure: Domain-general part of the Learner Model.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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12. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
The Open Learner Model
Figure: The Visualization of the Learner Model.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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13. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Negotiation Process
The negotiation mechanism depends on the learner’s credibility:
Figure: The DBN of the learner’s credibility.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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14. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Negotiation Process
When the learner wants to change the tutor’s belief
Credibility L’s belief < T’s belief L’s Belief > T’s belief
Low Persuasion Persuasion
Medium Persuasion Cooperation
High Persuasion Cooperation
When the tutor wants to change the student’s belief
Credibility L’s belief < T’s belief L’s belief > T’s belief
Low Support Contestation
Medium Support Contestation
High Support Contestation
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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15. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Prove Process
During the negotiation:
The system can request that the learner proves his knowledge, or;
The learner can request the opportunity of prove.
The proof process consists of:
Two problems and the learner has two chances to solve each
problem;
Then, his model is updated.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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16. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Demonstration Scenario
Figure: Example of Negotiation Dialogue Started by the Learner.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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17. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
We are we going next?
Improve the visualization, allowing the visualization of the two
parts of the model;
Put other evidences in the learner model: social characteristics,
CV-curriculum of the student, collaborative information;
Perform an experiment in a basic math classroom.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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18. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
References
Susan Bull, Paul Brna, and Helen Pain.
Extending the scope of the student model.
User Modeling and User-Adapted Interaction, 5(1):45–65, 1995.
Cristina Conati, Abigail Gertner, and Kurt Vanlehn.
Using bayesian networks to manage uncertainty in student
modeling.
User Modeling and User-Adapted Interaction, 12(4):371–417,
2002.
Vania Dimitrova.
Style-olm: Interactive open learner modelling.
International Journal of Artificial Intelligence in Education,
13(1):35–78, January 2003.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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19. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Referˆncias
e
David Thomson and Antonija Mitrovic.
Preliminary evaluation of a negotiable student model in a
constraint-based its.
Research and Practice in Technology Enhanced Learning,
5(1):19–33, 2010.
Juan-Diego Zapata-Rivera and Jim E. Greer.
Interacting with inspectable bayesian student models.
International Journal of Artificial Intelligence in Education,
14(2):127–163, 2004.
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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20. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
Thanks!!
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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21. Introduction
Related Work
Our Proposal
Demonstration Scenario
Final Considerations
References
For more information: http://tip.ic.ufal.br/site/
E. Costa, P. Silva, J. Magalh˜es and M. Silva
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