This paper gives a detailed annlysis of the learner-based teaching in EFL （English as a Foreign Language）. It deals with how we arrive at learner-based teaching, what is the role of teachers and the textbooks, and t...This paper gives a detailed annlysis of the learner-based teaching in EFL （English as a Foreign Language）. It deals with how we arrive at learner-based teaching, what is the role of teachers and the textbooks, and the advantages and potential problems of learner-based teaching in EFL. The main principle in learner-based teaching is that all class activities can be done in using information that the learners themselves bring into the class. What is novel about learner-based teaching is the idea that all activities can be based on that wealth of experience, their grammar exercises, or translations, etc.. In learner-based teaching, we should know how to help students to teach themselves in learning English. In using a learner-based approach, the learners themselves are responsible for the information input, thereby ensuring its relevance and topicality for each particular group. The basic procedure has two stages First, learners prepare materials which are designed to practice. Second, these materials are passed to other learners in the class who carry out the activities. In this way, students obtain valuable language practice, not only while they are using the materials, but also while they are preparing them as well.展开更多
This article analyzes the real connotation of learner-based teaching in language teaching. It deals with how we arrive at learner-based teaching, what is the role of the teacher and the coursebooks, the advantages and...This article analyzes the real connotation of learner-based teaching in language teaching. It deals with how we arrive at learner-based teaching, what is the role of the teacher and the coursebooks, the advantages and potential problems in learner-based teaching.展开更多
Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental h...Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental health of the people.Nowadays,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative influence.The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy.To overcome these issues,the proposed methodology utilizes a Modified Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectified Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its meaning.The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting.The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions.It also doesn’t stop the learning process when the values are negative.The MCNN computes a specified score value using a novel integrated anomaly detection tech-nique.Based on the score value,the anomalies are identified.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered.In this way,the performance of the model is enhanced in terms of classification accuracy.The efficiency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,specificity,recall,and precision.The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.展开更多
文摘This paper gives a detailed annlysis of the learner-based teaching in EFL （English as a Foreign Language）. It deals with how we arrive at learner-based teaching, what is the role of teachers and the textbooks, and the advantages and potential problems of learner-based teaching in EFL. The main principle in learner-based teaching is that all class activities can be done in using information that the learners themselves bring into the class. What is novel about learner-based teaching is the idea that all activities can be based on that wealth of experience, their grammar exercises, or translations, etc.. In learner-based teaching, we should know how to help students to teach themselves in learning English. In using a learner-based approach, the learners themselves are responsible for the information input, thereby ensuring its relevance and topicality for each particular group. The basic procedure has two stages First, learners prepare materials which are designed to practice. Second, these materials are passed to other learners in the class who carry out the activities. In this way, students obtain valuable language practice, not only while they are using the materials, but also while they are preparing them as well.
文摘This article analyzes the real connotation of learner-based teaching in language teaching. It deals with how we arrive at learner-based teaching, what is the role of the teacher and the coursebooks, the advantages and potential problems in learner-based teaching.
文摘Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental health of the people.Nowadays,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative influence.The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy.To overcome these issues,the proposed methodology utilizes a Modified Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectified Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its meaning.The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting.The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions.It also doesn’t stop the learning process when the values are negative.The MCNN computes a specified score value using a novel integrated anomaly detection tech-nique.Based on the score value,the anomalies are identified.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered.In this way,the performance of the model is enhanced in terms of classification accuracy.The efficiency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,specificity,recall,and precision.The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.
