This study was designed to investigate how college English teachers perceived the impact of College English Test Band 4(CET 4) on their curricular planning and instruction. Findings from this study indicate that CET 4...This study was designed to investigate how college English teachers perceived the impact of College English Test Band 4(CET 4) on their curricular planning and instruction. Findings from this study indicate that CET 4 has an influential impact on teachers' curricular planning and instruction. However, this washback influence is quite superficial, that is, the washback may influence what they teach but not how they teach. The findings were expected to provide different levels of educational parties with information about how curriculum standards and examination objectives might be interpreted. More importantly, the findings should contribute to bridging the gaps between the educational parties and ultimately lead to an improvement of CET 4 design and college English education.展开更多
Reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as...Reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as cooperative Boston Dynamics robots.However,robust RL has been a challenging problem of reliable aspects due to the gap between laboratory simulation and real world.Existing efforts have been made to approach this problem,such as performing random environmental perturbations in the learning process.However,one cannot guarantee to train with a positive perturbation as bad ones might bring failures to RL.In this work,we treat robust RL as a multi-task RL problem,and propose a curricular robust RL approach.We first present a generative adversarial network(GAN)based task generation model to iteratively output new tasks at the appropriate level of difficulty for the current policy.Furthermore,with these progressive tasks,we can realize curricular learning and finally obtain a robust policy.Extensive experiments in multiple environments demonstrate that our method improves the training stability and is robust to differences in training/test conditions.展开更多
文摘This study was designed to investigate how college English teachers perceived the impact of College English Test Band 4(CET 4) on their curricular planning and instruction. Findings from this study indicate that CET 4 has an influential impact on teachers' curricular planning and instruction. However, this washback influence is quite superficial, that is, the washback may influence what they teach but not how they teach. The findings were expected to provide different levels of educational parties with information about how curriculum standards and examination objectives might be interpreted. More importantly, the findings should contribute to bridging the gaps between the educational parties and ultimately lead to an improvement of CET 4 design and college English education.
基金supported by the National Natural Science Foundation of China (Nos.61972025,61802389,61672092,U1811264,and 61966009)the National Key R&D Program of China (Nos.2020YFB1005604 and 2020YFB2103802).
文摘Reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as cooperative Boston Dynamics robots.However,robust RL has been a challenging problem of reliable aspects due to the gap between laboratory simulation and real world.Existing efforts have been made to approach this problem,such as performing random environmental perturbations in the learning process.However,one cannot guarantee to train with a positive perturbation as bad ones might bring failures to RL.In this work,we treat robust RL as a multi-task RL problem,and propose a curricular robust RL approach.We first present a generative adversarial network(GAN)based task generation model to iteratively output new tasks at the appropriate level of difficulty for the current policy.Furthermore,with these progressive tasks,we can realize curricular learning and finally obtain a robust policy.Extensive experiments in multiple environments demonstrate that our method improves the training stability and is robust to differences in training/test conditions.
