This study investigates the relationship of learning strategies and styles on language learning and shows how different learning materials according to students＇ adoptions of them. And it especially focuses on analyz...This study investigates the relationship of learning strategies and styles on language learning and shows how different learning materials according to students＇ adoptions of them. And it especially focuses on analyzing and understanding how M.I. （Multiple Intelligences）, as a strategy, influence on language learning. For research method, this study adopts the observations of the classroom teachings and interviews with the students. As a result, the findings show that first, there are not enough clear explanation on relationship of strategies and styles, but overall, certain factors as verbal-linguistic and interpersonal intelligences help language learners improve their language ability. Second, the use of learning strategies has a positive effect on language learning. Third, the use of learning strategies can be trained and encouraged in language classrooms. Fourth, the appropriate learning materials should be selected according to learners＇ strategies and styles so that learning is more effective and successful. Finally, the implications of these findings are discussed.展开更多
To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of ...To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.展开更多
文摘This study investigates the relationship of learning strategies and styles on language learning and shows how different learning materials according to students＇ adoptions of them. And it especially focuses on analyzing and understanding how M.I. （Multiple Intelligences）, as a strategy, influence on language learning. For research method, this study adopts the observations of the classroom teachings and interviews with the students. As a result, the findings show that first, there are not enough clear explanation on relationship of strategies and styles, but overall, certain factors as verbal-linguistic and interpersonal intelligences help language learners improve their language ability. Second, the use of learning strategies has a positive effect on language learning. Third, the use of learning strategies can be trained and encouraged in language classrooms. Fourth, the appropriate learning materials should be selected according to learners＇ strategies and styles so that learning is more effective and successful. Finally, the implications of these findings are discussed.
基金The National Natural Science Foundation of China(No.71471135)
文摘To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.
