Existing problems that TVU have on learning support service system

There are some existing problems that TVU have on learning support service system. By analyzing the status quo of TVU learning support system, we raise problems such as current quality of personnel, teaching resources, part-time teacher management, teaching facilities, campus culture and other issues.

A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

Lecturers’Efforts in Building Rapport in the English-Medium Instruction(EMI)Context:Focus on the Use of Communication Strategies

Past studies reveal the prevalence of anxiety,coupled with low motivation and disengagement among students in English-medium instruction(EMI)programs.Given the detrimental impact these negative emotions can have on learning outcomes,it is imperative that teachers establish positive emotional rapport with their students.This study explores how experienced and highly rated EMI lecturers at a Chinese university’s overseas campus use communication strategies to build rapport with their students during interactive academic activities.It identifies the strategies used by these lecturers and examines how the strategies facilitate the teaching-learning process.The data,consisting of 10 hours of tutorials and 10 hours of supervisor-student supervision meetings,is analyzed using an adapted Conversation Analysis(CA)approach.The analysis reveals three types of communication strategies(CSs)frequently used by lecturers:back-channeling,codeswitching,and co-creation of messages.By employing these strategies,the lecturers established a strong rapport with the students,which created an encouraging and supportive learning environment.Consequently,this positive atmosphere facilitated students’learning of content knowledge through English.The findings of this study have implications for the training of lecturers who encounter difficulties in establishing rapport with multilingual students in the EMI setting.

Collaborative eye tracking based code review through real-time shared gaze visualization

Code review is intended to find bugs in early development phases,improving code quality for later integration and testing.However,due to the lack of experience with algorithm design,or software development,individual novice programmers face challenges while reviewing code.In this paper,we utilize collaborative eye tracking to record the gaze data from multiple reviewers,and share the gaze visualization among them during the code review process.The visualizations,such as borders highlighting current reviewed code lines,transition lines connecting related reviewed code lines,reveal the visual attention about program functions that can facilitate understanding and bug tracing.This can help novice reviewers to make sense to confirm the potential bugs or avoid repeated reviewing of code,and potentially even help to improve reviewing skills.We built a prototype system,and conducted a user study with paired reviewers.The results showed that the shared real-time visualization allowed the reviewers to find bugs more efficiently.

Patterns of Chromatin-Modifications Discriminate Different Genomic Features in Arabidopsis

Dynamic regulation and packaging of genetic information is achieved by the organization of DNA into chromatin. Nucleosomal core histones, which form the basic repeating unit of chromatin, are subject to various post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitinylation. These modifications have effects on chromatin structure and, along with DNA methylation, regulate gene transcription.The goal of this study was to determine if patterns in modifications were related to different categories of genomic features, and, if so, if the patterns had predictive value. In this study, we used publically available data（ChIP-chip）for different types of histone modifications（methylation and acetylation） and for DNA methylation for Arabidopsis thaliana and then applied a machine learning based approach（a support vector machine） to demonstrate that patterns of these modifications are very different among different kinds of genomic feature categories（protein, RNA,pseudogene, and transposon elements）. These patterns can be used to distinguish the types of genomic features.DNA methylation and H3K4me3 methylation emerged as features with most discriminative power. From our analysis on Arabidopsis, we were able to predict 33 novel genomic features, whose existence was also supported by analysis of RNA-seq experiments. In summary, we present a novel approach which can be used to discriminate/detect different categories of genomic features based upon their patterns of chromatin modification and DNA methylation.