Learner autonomy of English majors in out-of-class English reading program

Learner autonomy plays a vital role in effective out-of-class English reading. The purpose of the research is to find ways of improving leamer autonomy of English majors in out-of-class reading program based on the problem found from the data of a survey among tertiary students in a university in China. Learning portfolio and cooperative reading within the framework of reactive autonomy were introduced by the author as two tentative solutions of promoting learner autonomy in out-of-class English reading program.

On the Out-of-class Activities in EFL Teaching

EFL is very popular in China,but in-class learning is limited by many factors,such as time,space and teaching media,etc. This paper focuses on the out-of-class activities in EFL teaching,which aims at enhancing in-class English teaching as a supplement.

A Qualitative Study of Non-English Majors' Learner Autonomy in Out-of-class English Learning Contexts

The aim of the study was to explore non-Engish majors' actual practice of out-of class English learning activities and the relationship between out-of-class English learning and language proficiency. Data were gathered through qualitative methods by semi-structured interviews and observation with 6 non-English majors from Tianjin Foreign Studies University. Students' CET-4 scores were collected as well. Out-of-class observation was conducted with two participants who represent respectively high and low scores in CET. The results indicated that high scorers generally showed higher level of learner autonomy in out-of-class learning context. than low scorers. Most of the participants hoped to obtain more guidance and recommendations for learning materials and learning strategies so as to improve their language skills.

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.