Using a genetic algorithm to determine optimal complementary learning clusters for ESL in Taiwan

This paper proposes a strategy for using students’ complementary competencies in cooperative learning to increase their English learning performance. The concept of complementary learning is based on the idea that teaching is learning. The foundation of the complementary learning concept is composed of three stages proposed to derive the optimal learning clusters—input stage, genetic algorithm (GA) stage, and output stage. In tests and verification of the feasibility of using optimal complementary learning clusters in increasing students’ English learning outcome, comparisons between the experimental group (the optimal complementary learning clusters) and the control group showed that students in the experimental group had higher performances in listening, speaking, and reading competencies than those in the control group. Finally, according to the respective importance weights of different English competencies in different learning objectives, the fuzzy linguistic terms were applied to derive optimal complementary learning clusters to maximize students’ learning outcome.