This work proposes a Student Model (SM) oriented to predict causal effects that teaching and learning experiences produce on a student before their delivery. Our student modeling approach elicits concepts from domains that depict the educative program and the individual profile of the student, as content description and cognitive skills. The Cognitive Map sketches causal-effect relationships among the concepts involved by means of Fuzzy Rule Bases. Concepts and relations are fully described in an ontology. Based on the ontology, it is outcome a Cognitive Map for each option of teaching-learning experience. The analysis of the model depicted by the Cognitive Map is done through its activation. This process is a kind of simulation, which traces fuzzy causal inferences in order to estimate behaviors and final states for the concepts. The prediction of the causal results is achieved according to the interpretation of the evolution and final values of the concepts. So in Web-based Education Systems (WBES) that own several options for content, sequencing, and evaluation material, our student modeling offers a predictive support for student-centered education.
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Queen Mary Univ London, Dept Biol & Expt Psychol, London, England
Univ Fed ABC, Ctr Math Comp & Cognit, Campus Sao Bernardodo Campo Sala 259 Bloco Delta,A, BR-09606045 Sao Bernardo Do Campo, SP, Brazil
Natl Inst Sci & Technol Social & Affect Neurosci, Sao Paulo, BrazilQueen Mary Univ London, Dept Biol & Expt Psychol, London, England
Biazoli Jr, Claudinei E.
Sato, Joao R.
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Univ Fed ABC, Ctr Math Comp & Cognit, Campus Sao Bernardodo Campo Sala 259 Bloco Delta,A, BR-09606045 Sao Bernardo Do Campo, SP, Brazil
Hosp Israelita Albert Einstein, Big Data, Sao Paulo, BrazilQueen Mary Univ London, Dept Biol & Expt Psychol, London, England
Sato, Joao R.
Pluess, Michael
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Queen Mary Univ London, Dept Biol & Expt Psychol, London, England
Univ Surrey, Sch Psychol, Guildford, EnglandQueen Mary Univ London, Dept Biol & Expt Psychol, London, England
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NYU, Dept Phys, Ctr Cosmol & Particle Phys, 4 Washington Pl, New York, NY 10003 USANYU, Dept Phys, Ctr Cosmol & Particle Phys, 4 Washington Pl, New York, NY 10003 USA
Wang, Dun
Hogg, David W.
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NYU, Dept Phys, Ctr Cosmol & Particle Phys, 4 Washington Pl, New York, NY 10003 USA
NYU, Ctr Data Sci, New York, NY 10003 USA
Max Planck Inst Astron, Heidelberg, GermanyNYU, Dept Phys, Ctr Cosmol & Particle Phys, 4 Washington Pl, New York, NY 10003 USA
Hogg, David W.
Foreman-Mackey, Daniel
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Univ Washington, Dept Astron, Seattle, WA 98195 USANYU, Dept Phys, Ctr Cosmol & Particle Phys, 4 Washington Pl, New York, NY 10003 USA
Foreman-Mackey, Daniel
Schoelkopf, Bernhard
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Max Planck Inst Intelligente Syst, Tubingen, GermanyNYU, Dept Phys, Ctr Cosmol & Particle Phys, 4 Washington Pl, New York, NY 10003 USA
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Department of Computer and Electrical Engineering and Computer Science, College of Engineering, Florida Atlantic University, Boca Raton, FLDepartment of Computer and Electrical Engineering and Computer Science, College of Engineering, Florida Atlantic University, Boca Raton, FL
Agarwal A.
Baechle C.
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Department of Computer and Electrical Engineering and Computer Science, College of Engineering, Florida Atlantic University, Boca Raton, FLDepartment of Computer and Electrical Engineering and Computer Science, College of Engineering, Florida Atlantic University, Boca Raton, FL
Baechle C.
Behara R.S.
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Department of IT and Operations Management, College of Business, Florida Atlantic University, Boca Raton, FLDepartment of Computer and Electrical Engineering and Computer Science, College of Engineering, Florida Atlantic University, Boca Raton, FL
Behara R.S.
Rao V.
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Methodist University Hospital Transplant Institute, Memphis, TNDepartment of Computer and Electrical Engineering and Computer Science, College of Engineering, Florida Atlantic University, Boca Raton, FL