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Optimizing tasks generation for children in the early stages of literacy teaching: a study using bio-inspired metaheuristics.
- Source :
-
Soft Computing - A Fusion of Foundations, Methodologies & Applications . Oct2018, Vol. 22 Issue 20, p6811-6824. 14p. - Publication Year :
- 2018
-
Abstract
- 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]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 22
- Issue :
- 20
- Database :
- Academic Search Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 132021997
- Full Text :
- https://doi.org/10.1007/s00500-018-3409-1