The Nature and Use of Technology-Based Self-Regulated Learning Strategies Among EFL Students

This study explored the nature and use of technology-based self-regulated learning(SRL)strategies among the Chinese university students.A total of 20 undergraduate students in China's Mainland were invited to participate in a focus group interview.The students reported using four types of technology-based SRL strategies including cognitive,meta-cognitive,social behavioral,and motivational regulation strategies.Among the strategies,technology-based vocabulary learning was reported to be a dominant strategy by the students.This study opens a new window to understanding how English as a foreign language(EFL)students utilize different strategies to learn English in technology-based learning context.

Surveying on English Mobile Learning Among University Students: Current State and Influencing Factors

Mobile learning integrates mobile technology with digital learning,offering flexible,personalized content and portable equipment.It enables access to rich content and enhances learning efficiency.Therefore,it has become mainstream to utilize mobile devices for English learning among university students’English learning.The current study aims to examine the current situation and influencing factors of university students’English mobile learning.98 university students in one university of Shanghai participated the study and the questionnaire was used to collect the data.The results indicated that most university students already have electronic devices to support mobile learning.Personal factors,environmental factors,digital literacy,and technological capabilities are the main factors affecting university students’English mobile learning.The current study has implications for learners,teachers,and software developers.Learners should adjust their learning motivation,play an active role,and fully utilize the mobile platform to obtain resources and improve learning efficiency.Teachers should incorporate the advantages of mobile teaching and promote categorized and tiered teaching.Software developers should add new functions on the basis of meeting the basic needs of learners and continuously innovate the mobile learning platform.

Evaluation of Chinese Learning Efficiency of International Students in China:Comparing by Dimension

This paper introduces the Value Engineering method to calculate the value coefficient of Chinese learning efficiency for 377 international students by dimension.Results suggest that attention should be paid to male,European and American,rural,introverted,and“work or education needs”international students;Give full play to the driving role of the reference-type,explore the space for improving the learning efficiency of the improvement-type and attention-type,and pay attention to the problems of problem-type international students.

Evaluation of Chinese Learning Efficiency of International Students in China

This paper investigates the related factors of Chinese learning engagement and performance of 377 students from three universities in Guangzhou of China,employs the value engineering method,and calculates the value coefficient of learning efficiency.From the value coefficient of Chinese language learning,it proves that over 60%of international students studying in China have a higher efficiency in learning Chinese.

Efficacy of Online Learning and Influence on Skin and Eyes Among College Students

To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.

An Investigation on the Influence of Middle School Students'Grammar Learning Self-efficacy on Their Learning Performance

In middleschool English teaching,grammar instruction has always been an important and challenging aspect.Due to the strong influence of grammar explanation in Chinese,learning English grammar may be affected by negative transfer from their native language,which could pose certain obstacles to English acquisition.Self-efficacy,as a major emotional factor influencing learners'internal regulation strategies,has attracted wide attention from researchers both domestically and internationally for its impact on learning performance.High self-efficacy helps learners develop a more positive mindset towards learning,thereby contributing to improved learning performance.Therefore,this study focuses on investigating the relationship between grammatical self-efficacy and learning performance of English grammar among middle school students.This research contributes to a complementary understanding of grammatical self-efficacy among educators,enhancing their teaching skills,and effectively guiding students to learn English grammar with a more positive and healthy mindset,presenting a future grammar instruction in middle school English teaching.

Results of the virtual simulation teaching method on autonomous learning competencies of undergraduate nursing students

Background:With the continuous development of information technology,most universities use mobile teaching platforms for classroom teaching.With the help of the national virtual simulation experimental teaching project sharing platform,students can enhance self-directed learning through the virtual simulation operations of the project.Purpose:To explore the application of virtual simulation experiment in teaching the fundamentals of nursing practice based on the Platform of the National Virtual Simulation Experiment Teaching Project during the COVID-19 pandemic analyze the impact of this teaching method on the autonomous learning ability of undergraduate nursing students.Methods:Convenience sampling was used to select 121 nursing undergraduates from Y University’s School of Nursing;the online teaching of fundamentals of nursing practice was conducted to the students.After taking the course,questionnaires were distributed to the undergraduate nursing students to collect their perceptions regarding the use of the virtual simulation experiment platform and autonomous learning competencies.Results:Most students expressed their preference for the virtual simulation teaching platform,and their satisfaction with the project evaluation was high 83.05%.They hoped to promote the application in future experimental teaching.Undergraduate nursing students believed that the virtual simulation teaching platform was conducive to cultivating clinical thinking ability,could stimulate learning interest,enhanced autonomous learning competencies.Conclusion:During the pandemic,the virtual simulation teaching platform for a lecture on in nursing education has achieved good results in both the aspects of teaching and student learning.Teachers efficiently used their training time and reduced their teaching burden.Moreover,the laboratory cost was also reduced.For undergraduate nursing students,the system was conducive to cultivating clinical thinking ability,stimulating their interest in learning,enhancing their learning and comprehension abilities and learning initiative.

An Analysis of College Students＇ Autonomous English Learning Ability under the Network Environment

Referring to the questionnaire survey and literature materials, this paper describes the current situation of college students＇ insufficient autonomous English learning ability and thefactors that influence this ability; it also suggests feasible suggestions under the network environment. Students （N= 178） from different grades, majors, universities and areas responded to a questionnaire survey, and the collected data were analyzed. The results indicate that complex external environment and insufficient internal subject cognition influence college students＇ autonomous English learning ability. Finally, this paper suggests that a good network environment is necessary for autonomous learners to enhance insufficient autonomous English learning ability and interaction, continuous thinking and cultural introduction are also indispensable to develop this ability.

Investigation and Research on College Students' English Learning Strategies in the Network Age

In the era of widespread use of the Internet, students are more active in English learning, and their use of learning concepts and learning strategies is an important factor in determining the success or failure of English learning. A university is a school that conducts higher education. It refers to a higher education institution that provides education, research conditions, and approves degree awards. The stage of studying in college is a great memory for many people in their lives. There is no pressure on college time compared to high school and high school. College students feel that English is not important, they do not want to learn. In the era when college students often go online, in order to encourage students' desire to learn, we must explore the use of the Internet, and to develop students' English learning habits, we must use the Internet. In recent years, education has gradually introduced the help of network technology, and students' learning methods are diversified and comprehensive. English is a very important skill learning focus for college students. Non-English majors can learn English through a wider range of English learning. This report is an English learning strategy for college students in the Internet age.

An Empirical Research on the Behavior and Influencing Factors of College Students Network Course Learning

＂Using technology to support learning＂ gradually becomes the main trend of technology application and technology-curriculum integration at home and abroad. The empirical research based on newly-established universities shows that college students＇ usage intention on network course and study behavior is not real. Study behavior of network course is passive and outside stimulation. They are more concerned about the usefulness and relative advantages of the learning mode in the network course learning; however, learning environment of the network course at present still is given on material and tool level. The institutions should, in compliance with the trend of higher education development in the world, actively build learning environment to meet the learning needs of students in the information age, deepen the reform of information technology application and curriculum based on the learner-centered concept, focus on the training of initiative learning of college students, put community impact into full play to promote the integration of the superiority of the material platform and initiative, and finally improve the efficiency and quality of teaching and education.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

On Reducing Students' Anxiety in English Listening through Cooperative Learning

Listening is an important means by which English learners can obtain information and communicate with others. Therefore, listening is an important skill of English learning and listening training is an indispensible part of language learning. However, listening can easily be affected by anxiety. Anxiety, an obvious psychological phenomenon in listening and speaking, has negative effect on language input, process and output. Although anxiety has negative influence on English listening, it can be overcome by Cooperative Learning (CL). CL can reduce anxiety, improve communication ability and strengthen learners' confidence, etc.

On Improving Students' Learning Efficiency in English Teaching

In our country,traditional English teaching attaches excessive importance to the explanation of vocabulary and grammar,causing students' disability in language practice.This paper provides some strategies to improve students' learning efficiency.

Investigation of English learning strategy employed by senior middle school students in Zhanjiang City： A comparison of students from urban and rural areas

After a review of learning strategy research in China and abroad, this paper made an investigation on the differences in use of learning strategies reported by urban and rural students from four middle schools in Zhanjiang city. The investigation revealed the following findings： urban students employ cognitive and social strategies more frequently than rural students; urban students reported a wider range of strategies compared with their rural peers; urban students of intermediate achievements employ more social strategies than their rural peers, while rural students use affective strategy significantly more often; urban and rural students reported different patterns of gender difference.

English Learning Strategies Employed by EFL Postgraduate Students in Southwest China

English learning strategies play an important role in the language proficiency for EFL learners. Both frequency and patterns of English learning strategies use were found to be significantly related to language proficiency. The present investigation aimed to investigate a) an overall strategy use of postgraduate students in southwest China; and b) to examine the relationship as well as patterns of variations in frequency of students' reported strategy use with reference to their gender and field of study. The subjects of this study were 200 postgraduates in the universities of southwest China. A written questionnaire based on the Strategy Inventory for Language Learning(SILL) was adapted and biodata was added for background information. The findings of the research showed that the postgraduates, on the whole, reported medium frequency of use of English learning strategies. The results of the data analysis also demonstrated that frequency of postgraduates' reported use of English learning strategies varied significantly in terms of gender and field of study. The implications, limitations and recommendations for further research were also discussed.

A Study on Left-behind Junior Students'Foreign Language Learning Anxiety

Since the 1960s, foreign language educators and researchers home and abroad have started to pay attention to the influence of the affective variables including language learning anxiety on foreign language learning. This thesis tried to find out the factors that may influence the foreign language learning anxiety level of left-behind students and put forward ways to help lower down the foreign language learning anxiety level of the left-behind junior middle school students.

The Analysis on the Role of Motivation in College Students’ English Learning

Foreign language teaching is a very complicated process that includes varied factors.Among those factors,the learner's factors are very important.Learner is the main part of a foreign language learning and teaching.In recent years,considerable research has been focused on the study of the foreign language learners,especially on their motivations.Learners'motivation plays a very important role in Chinese college students'English learning.

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

Strengthening network slicing for Industrial Internet with deep reinforcement learning

Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain.Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology.Guaranteeing robust network slicing is essential for Industrial Internet,but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions(NFs)composing the slice.Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties.Towards this end,we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements.State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution.Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.