Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-06-22T22:29:53Z No. of bitstreams: 2 everton_oliveira_paiva.pdf: 3688473 bytes, checksum: 00c67bcc4d4564b69bb64a0b596743fc (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-06-23T13:21:09Z (GMT) No. of bitstreams: 2 everton_oliveira_paiva.pdf: 3688473 bytes, checksum: 00c67bcc4d4564b69bb64a0b596743fc (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-06-23T13:21:09Z (GMT). No. of bitstreams: 2 everton_oliveira_paiva.pdf: 3688473 bytes, checksum: 00c67bcc4d4564b69bb64a0b596743fc (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016 / O uso sistemas computacionais como complemento ou substitui??o da sala de aula ? cada vez mais comum na educa??o e os Sistemas Tutores Inteligentes (STIs) s?o uma dessas alternativas. Portanto ? fundamental desenvolver STIs capazes tanto de ensinar quanto aprender informa??es relevantes sobre o aluno atrav?s de t?cnicas de intelig?ncia artificial. Esse aprendizado acontece por meio da intera??o direta entre o STI e o aluno que ? geralmente demorada. Esta disserta??o apresenta a inser??o da metaheur?sticas Lista Tabu e GRASP com o objetivo de acelerar esse aprendizado. Para avaliar o desempenho dessa modifica??o, foi desenvolvido um simulador de STI. Nesse sistema, foram realizadas simula??es computacionais para comparar o desempenho da tradicional pol?tica de explora??o aleat?ria e as metaheur?sticas propostas Lista Tabu e GRASP. Os resultados obtidos atrav?s dessas simula??es e os testes estat?sticos aplicados indicam fortemente que a introdu??o de meta-heur?sticas adequadas melhoram o desempenho do algoritmo de aprendizado em STIs. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / Using computer systems as a complement or replacement for the classroom experience is an increasingly common practice in education and Intelligent Tutoring Systems (ITS) are one of these alternatives. Therefore, it is crucial to develop ITS that are capable of both teaching and learning relevant information about the student through artificial intelligence techniques. This learning process occurs by means of direct, and generally slow, interaction between the ITS and the student. This dissertation presents the insertion of meta-heuristic Tabu search and GRASP with the purpose of accelera ting learning. An ITS simulator was developed to evaluate the performance of this change. Computer simulations were conducted in order to compare the performance of traditional randomized search methods with the meta-heuristic Tabu search. Results obtained from these simulations and statistical tests strongly indicate that the introduction of meta-heuristics in exploration policy improves the performance of the learning algorithm in ITS.