Application of Student-centered Learning Approach to English Listening and Speaking Class in Vocational Schools

Student-centered learning approach is focused on the students' demands and interests.Applying student-centered approach puts forward higher requirement to English teachers.This article first analyzes the theory of student-centered learning approach and compares teacher-centered approach with it.Based on the research information and teaching experience,the author summarizes four strategies about how to apply student-centered learning approach to English listening and speaking class in vocational schools.

A novel machine learning approach(svm Somatic) to distinguish somatic and germline mutations using next-generation sequencing data

DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain.

Fathers’Parenting Practices and the Children’s Approaches to Learning From Three Through Five Years;A Case of Mityana District,Uganda

The Approaches to Learning addresses how children learn-this includes children’s attitudes and interests in learning.This domain reflects behaviours and attitudes such as curiosity,problem-solving,maintaining attention and persistence.The research study focused on examining the fathers’parenting practices and the children’s approaches to learning from three through five years.The study used a cross sectional research design and data was generated using focal group discussions,interview guides and child behaviour rating scale on how fathers’parenting practices contribute to children’s approaches to learning.Results revealed that,Fathers’parenting practices and Children’s curiosity were found to have a very positive relationship(r=0.396,p<0.05).Fathers’parenting practices and children’s learning were found to have a significant positive relationship(r=0.420,p<0.05).Findings also indicated that fathers’parenting practices and children’s creativity were found to have an average positive relationship(r=0.379,p<0.05).Arising out of the findings,the study recommended that fathers’parenting programs be put in place to help them up bring the child in holistic manner.

FIRST LANGUAGE ACQUISITION AS A MODEL FOR SECOND LANGUAGE ACQUISITION: THE TOTAL PHYSICAL RESPONSE APPROACH IN SECOND LANGUAGE LEARNING AND TEACHING

L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages.

A Discussion On the Range of Meaning of English Words──An Approach to English Words Learning

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Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets,and Future Directions

In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

Data generative machine learning model for the assessment of outdoor thermal and wind comfort in a northern urban environment

Predicting comfort levels in cities is challenging due to the many metric assessment.To overcome these challenges,much research is being done in the computing community to develop methods capable of generating outdoor comfort data.Machine Learning(ML)provides many opportunities to discover patterns in large datasets such as urban data.This paper proposes a data-driven approach to build a predictive and data-generative model to assess outdoor thermal comfort.The model benefits from the results of a study,which analyses Computational Fluid Dynamics(CFD)urban simulation to determine the thermal and wind comfort in Tallinn,Estonia.The ML model was built based on classification,and it uses an opaque ML model.The results were evaluated by applying different metrics and show us that the approach allows the implementation of a data-generative ML model to generate reliable data on outdoor comfort that can be used by urban stakeholders,planners,and researchers.

Catering for Learner Diversity in Hong Kong Secondary Schools:Insights from the Relationships Between Students’Learning Styles and Approaches

Purpose:Catering for learner diversity is a key issue in the recent educational reforms in Hong Kong.The present study addresses this issue through an investigation of the relationships between students’learning styles and approaches to learning in Hong Kong secondary schools.Design/Approach/Methods:A total of 6,054 junior secondary students in Hong Kong responded to a questionnaire consisting of two instruments.A series of confirmatory factor analysis,two-way analysis of variance,and structural equation modeling analysis were conducted.Findings:The results identified three types of learning style among the students which are characterized by a cognitive orientation,a social orientation,and a methodological orientation.Some significant gender-and achievement-level differences were revealed.Compared with the socially oriented learning style,the cognitively and methodologically oriented learning styles were more extensively and strongly related to students’approaches to learning,even though these students showed a greater preference for the socially oriented learning style.Originality/Value:It is unwise to blindly cater for students’learning styles in classroom teaching and curriculum design.Teachers should adopt a comprehensive and balanced approach toward the design of curriculum and teaching which not only highlights the congruence between students’learning styles and teacher’s pedagogy but also integrates the constructive frictions between them into classroom teaching.

Chilean University Students Digital Learning Technology Usage Patterns and Approachesto Learning

Purpose:This study aims to explore Chilean students'digital technology usage patterns andapproaches to learningDesignlApproach/Methods:We conducted this study in two stages We worked with onesemester learning management systems(LMS),library,and students records data in the firstone.We performed a k-means cluster analysis to identify groups with similar usage patterns.Inthe second stage,we invited students from emerging dusters to participate in group interviews.Thematic analysis was employed to analyze them.Findings:Three groups were identified:ID digital library users/high performers,who adopteddeeper approaches to learning obtained higher marks,and used learning resources to integratematerials and expand understanding 2)LMS and physical library userslmid-performers,whoadopted mainly strategicapproaches obtained marks dlose to average,and used learning resources for studying in an organized manner toget good marks and 3)lower users of LMS andlibrarylmidlow performers,who adopted mainly a surface approach,obtained mid-to-lower-than-averagemarks,and used learning resources for minimum content understanding Originality/Value:We demonstrated the importance of combining learning analytics data withqualitative methods to make sense of digital technology usage patternss approaches to learningare associated with learning resources use.Practical recommendations are presented.

DEVELOPING LEARNERS' LANGUAGE AWARENESS: A NEW APPROACH TO LANGUAGE LEARNING AT THE TERTIARY LEVEL

This paper presents some practical language awareness activities, and discusses ways of implementing them in the tertiary ELT classroom. 1. Sample language awareness activities in vocabulary learning LANGUAGE awareness, as an approach, can be applied in all aspects of English language study: phonol-

Existing Problems and Solutions on Rural Primary School Language Education

At present,there are still many problems in language teaching in rural primary schools,which will affect the quality of teaching if we don't pay much attention to them.This article focuses on the existing flaws in current language teaching and provides some solutions.

The Supervised Learning Gaussian Mixture Model

The traditional Gaussian Mixture Model (GMM) for pattern recognition is an unsupervised learning method. The parameters in the model are derived only by the training samples in one class without taking into account the effect of sample distributions of other classes, hence, its recognition accuracy is not ideal sometimes. This paper introduces an approach for estimating the parameters in GMM in a supervising way.The Supervised Learning Gaussian Mixture Model (SLGMM) improves the recognition accuracy of the GMM. An experimental example has shown its effectiveness. The experimental results have shown that the recognition accuracy derived by the approach is higher than those obtained by the Vector Quantization (VQ) approach, the Radial Basis Function (RBF) network model, the Learning Vector Quantization (LVQ) approach and the GMM. In addition, the training time of the approach is less than that of Multilayer Perceptron (MLP).

Predicting density log from well log using machine learning techniques and heuristic optimization algorithm:A comparative study

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect data.This study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon volumes.It is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant challenge.To tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm Optimization(PSO),Imperialist Competitive Algorithms(ICA),and Genetic Algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log data.Remarkably,our results highlight that the Genetic Algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of 0.0013.In comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

Semisupervised Sparse Multilinear Discriminant Analysis

Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.