scholarly journals The College Students’ Oral English Education Strategy Using Human-Computer Interaction Simulation System From the Perspective of Educational Psychology

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping Zhou ◽  
Xiaoliang Wu ◽  
Hui Xu ◽  
Guan Wang
Keyword(s):  
College Students ◽  
Human Computer Interaction ◽  
Educational Psychology ◽  
English Education ◽  
Current Situation ◽  
Simulation System ◽  
Support Vector ◽  
English Learning ◽  
Learning Anxiety ◽  
Computer Interaction

The role of the human–computer interaction (HCI) system in college students’ oral English learning is discussed to analyze the current situation of college students’ oral English based on the HCI simulation system. The purpose is to study the oral education of college students. First, the theories of educational psychology, the HCI system, and the current situation of college students’ oral English learning are elaborated. Meanwhile, in oral English teaching, teachers use support vector machines and multimodal fusion intention perception methods in set theory to realize the interactive teaching between students and machines; then, the HCI simulation of oral English is explained. The current situation of college students’ oral English learning is analyzed by a questionnaire from the perspective of educational psychology. Finally, the HCI system in college students’ oral English learning is explored based on the learning level detection. The results show that 12% of college students are unqualified in oral English; 25% of them think their oral English level is medium; most of college students’ English learning anxiety is related to English progress anxiety; 18% of the students believe that they will study oral English for life; 32% of the students think that they have more opportunities to learn English at ordinary times; and most of the students learn English through English movies and songs outside of class. What attracts college students to learn oral English through the HCI system is that learning is not limited by time and space. Most students believe that their English level is good and hope that learning anxiety can be reduced through HCI systems. The strategies of college students’ oral English education with an HCI simulation system are evaluated based on the perspective of educational psychology, providing a research basis for oral English education in other regions and even the whole country to facilitate the better development of oral English education.

SUPPORT VECTOR LEARNING FOR GENDER CLASSIFICATION USING AUDIO AND VISUAL CUES

2003 ◽  
Vol 17 (03) ◽  
pp. 417-439 ◽  
Author(s):  
L. WALAVALKAR ◽  
M. YEASIN ◽  
A. NARASIMHAMURTHY ◽  
R. SHARMA
Keyword(s):  
Human Computer Interaction ◽  
Visual Signal ◽  
Support Vector ◽  
Gender Classification ◽  
Demographic Information ◽  
Data Set ◽  
Equal Distribution ◽  
Audio Features ◽  
Speech Data ◽  
Computer Interaction

Computer vision systems for monitoring people and collecting valuable demographic information in a social environment is an important research problem. It is expected that such a system will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system can provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates gender classification using Support Vector Machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principle component analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment. The performance of the SVM was compared with two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. To further understand the robustness issues, the proposed approach has been applied on a large balanced (roughly equal distribution of gender, ethnicity and age group) data-base consisting of 8000 faces collected in real world environment. While, the results are very promising it indicates more to be done to make a statistically meaningful conclusion.

Design and Implementation of Human-Computer Interaction Systems Based on Transfer Support Vector Machine and EEG Signal for Depression Patients’ Emotion Recognition

2021 ◽  
Vol 11 (3) ◽  
pp. 948-954
Author(s):  
Xiang Chen ◽  
Lijun Xu ◽  
Ming Cao ◽  
Tinghua Zhang ◽  
Zhongan Shang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Human Computer Interaction ◽  
Transfer Learning ◽  
Field Data ◽  
Interactive Systems ◽  
Classification Model ◽  
Support Vector ◽  
Target Domain ◽  
Emotional Changes ◽  
Computer Interaction

At present, the demand for intelligentization of human-computer interaction systems (HCIS) has become increasingly prominent. Being able to recognize the emotions of users of interactive systems is a distinguishing feature of intelligent interactive systems. The intelligent HCIS can analyze the emotional changes of patients with depression, complete the interaction with the patients in a more appropriate manner, and the recognition results can assist family members or medical personnel to make response measures based on the patient’s emotional changes. Based on this background, this paper proposes a sentiment recognition method based on transfer support vector machines (TSVM) and EEG signals. The ER (ER) results based on this method are applied to HCIS. Such a HCIS is mainly used for the interaction of patients with depression. When a new field related to a certain field appears, if the new field data is relabeled, the sample is expensive, and it is very wasteful to discard all the old field data. The main innovation of this research is that the introduced classification model is TSVM. TSVM is a transfer learning strategy based on SVM. Transfer learning aims to solve related but different target domain problems by using a large amount of labeled source domain data. Therefore, the transfer support vector machine based on the transfer mechanism can use the small labeled data of the target domain and a large amount of old data in the related domain to build a high-quality classification model for the target domain, which can effectively improve the accuracy of classification. Comparing the classification results with other classification models, it can be concluded that TSVM can effectively improve the accuracy of ER in patients with depression. The HCIS based on the classification model has higher accuracy and better stability.

scholarly journals College English Teaching Status and Individualized Teaching Design in The Context of Mobile Learning

2019 ◽  
Vol 14 (12) ◽  
pp. 85
Author(s):  
Yuan Zhang ◽  
Lu Zuo
Keyword(s):  
College Students ◽  
Mobile Learning ◽  
English Education ◽  
Scientific Basis ◽  
Three Dimensions ◽  
English Learning ◽  
College English ◽  
English Teaching ◽  
The Status ◽  
Individualized Teaching

This paper aims to optimize the design of individualized classroom teaching for mobile English learning in colleges, and find a scientific and effective English teaching mode and strategy for mobile learning. For this purpose, the application and evolution of mobile learning in English teaching was investigated, and the concept and theoretical bases of mobile learning were introduced in details. Through literature review, questionnaire survey and face-to-face interview, the status of mobile English learning among college students was analysed qualitatively and quantitatively from three dimensions, namely, the mobile device, learning resource and learning attitude. Then, several strategies for individualized teaching reform were presented through status analysis. Finally, the application of individualized mobile English learning was demonstrated through case studies on public platforms like Weibo and WeChat. The research findings shed new light on the individualized English learning among college students and lay a scientific basis for the application of mobile learning in college English education.

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