| Autor: |
de Souza, Gilberto Nerino, de Deus, Daniel Felipe, Tadaiesky, Vincent, de Araújo, Igor Meireles, Monteiro, Dionne Cavalcante, de Santana, Ádamo Lima |
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| Zdroj: |
Soft Computing - A Fusion of Foundations, Methodologies & Applications; Oct2018, Vol. 22 Issue 20, p6811-6824, 14p |
| Abstrakt: |
Behavioral teaching procedures can be used to promote the individualized learning of reading skills for children, and computational processes can assist instructors in the generation of a set of tasks. However, the automatic generation of these tasks can be unfeasible due to the high-order search space for the possible combinations of tasks; this complexity increases when considering the possible constraints as well as adapting the tasks to the individual characteristics of each student. This paper presents a new method to automatically generate teaching matching-to-sample tasks, adapting the difficulty by using bio-inspired optimization metaheuristics. Genetic algorithms, ant colony optimization, and integer and categorical particle swarm optimization were evaluated to determine their stability and capacity to generate adapted tasks. A comparison of the results between the algorithms showed a better rate of convergence for the genetic algorithms, which were able to generate tasks at an adapted level of difficulty to students. These tasks were applied to a group of students at a Brazilian public school in the early stages of a literacy course indicating satisfactory effects in the individual learning process. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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