Work place: University of Brasília, Department of Computer Science, BRASÍLIA, BRAZIL Observatory of Public Spending, Department of Research and Strategic Information, Office of the Comptroller General, BRASÍLIA, BRAZIL
Research Interests: Computational Engineering, Software Development Process, Software Engineering, Data Mining, Data Structures and Algorithms
Dr. Rommel Novaes Carvalho finished his Postdoctoral at George Mason University (GMU) in the area of artificial intelligence, data mining, uncertainty, and knowledge discovery in May 2012. During the 3 years of his PhD, he was a Graduate Research Assistant in the Department of Systems Engineering and Operations Research at GMU, Virginia, USA. He received his Master in Computer Science and his Bachelor of Computer Science from University of Brasília (UnB), DF, Brazil, in 2008 and 2003, respectively. He has been working for the Brazilian Office of the Comptroller General (CGU) as an IT expert since 2005 and at UnB as a Professor on the Applied Computer Science Masters program since 2012, when it was created. From 2011 to 2012 he participated in the Transparency Portal team, where his key role was to be the main expert in Open Government Data (OGD). In the end of 2012 he started working as the leader of the Data Science team at the Department of Research and Strategic Information (DIE). One of the projects developed at DIE, the Reference Price Database, won the first place on the CONIP 2013 Excellence Award in the category Management and Geographical Information Systems. He has done research on fraud detection and prevention for the Brazilian Government and situation awareness for the U.S. Navy. With almost 11 years of experience in the area, he has produced more than 80 different technical outputs, including papers, book chapters, technical presentations, processes, among others. He is an artificial intelligence researcher with focus on data mining, uncertainty in the Semantic Web using Bayesian inference, software engineering, and both R and Java programming. Awarded programmer with experience in implementation of Bayesian network systems (UnBBayes), multi-entity Bayesian network (MEBN) and Probabilistic Web Ontology Language (PR-OWL), R packages, and various web-based applications.
DOI: https://doi.org/10.5815/ijmecs.2016.03.01, Pub. Date: 8 Mar. 2016
It’s worth noting that the present paper lies within the range of modeling the learner in adaptive educational system as a conceptual modeling of the learner. Thought they are several methods that deal with the learner model; like stereotypes methods or learner profile…, but they are likely unable to handle the uncertainty embedded in the dynamic modeling of the learner. The present paper aims at studding different models and approaches to model the learner in an adaptive educational systems, and coming up with the most appropriate method based on the dynamic aspect of this model.
The aim of this study is the argue that the learner model cannot be completely modeled based on one single method through the entire development process, but it needs a combination between several methods that will help for a complete modeling.
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