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.
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Acknowledgements
We would like to express our thanks to the team from the Edson Luis School for their assistance in teaching matters, in particular Mrs. Sandra Nazaré Parente de Oliveira. This study was partially funded by the Brazilian Agency for the Support and Evaluation of Post-Graduate Education (PGPTA) Nº 59/2014.
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de Souza, G.N., de Deus, D.F., Tadaiesky, V. et al. Optimizing tasks generation for children in the early stages of literacy teaching: a study using bio-inspired metaheuristics. Soft Comput 22, 6811–6824 (2018). https://doi.org/10.1007/s00500-018-3409-1
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DOI: https://doi.org/10.1007/s00500-018-3409-1