Distance education learning support service system is a two-way information exchange platform for teachers and students to communicate and learn, and it is based on modern multimedia information technology. With the construction of a learning society, people's distance learning service system is more widely used for reasons such as occupation or hobby. How to improve the learning service system of distance education is an important subject. We must have a full understanding of the learning service system of distance education and find ways to further improve it through case analysis and reference. Using the methods of field investigation, mathematical analysis, and experimental research, this paper focuses on the methods and means of evaluating distance education by using information technology and puts them into practice and designs and develops a model-based modern distance education quality evaluation system.
{"title":"A Case Study of a University Distance Education System Based on Multimedia Technology","authors":"Lianlian Yuan, Dongya Ji","doi":"10.4018/ijwltt.330021","DOIUrl":"https://doi.org/10.4018/ijwltt.330021","url":null,"abstract":"Distance education learning support service system is a two-way information exchange platform for teachers and students to communicate and learn, and it is based on modern multimedia information technology. With the construction of a learning society, people's distance learning service system is more widely used for reasons such as occupation or hobby. How to improve the learning service system of distance education is an important subject. We must have a full understanding of the learning service system of distance education and find ways to further improve it through case analysis and reference. Using the methods of field investigation, mathematical analysis, and experimental research, this paper focuses on the methods and means of evaluating distance education by using information technology and puts them into practice and designs and develops a model-based modern distance education quality evaluation system.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42757552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous advancement of curriculum reform, how to improve the classroom efficiency and optimize the learning classroom model has become a topic of increasing concern for scholars and teachers. With the development and popularization of computer network, distance education has been widely used. It is in this case that the resource library came into being, which provides rich resources for Liu Xiye to make multimedia courseware. The learning of courses plays a fundamental role in education, which is reflected in modern distance education, that is, how to make courseware better. At the same time, online courses are also getting more and more attention from people. Online educators have higher and higher requirements for the design of each part of online courses. How to design feedback in the learning of online courses is also a must for every designer. Based on artificial intelligence education courseware, this paper introduces some audio and video technologies used in making multimedia CAI (computer-assisted instruction) courseware.
{"title":"Application of Artificial Intelligence Technology and Network Technology in Multimedia Courseware Making Course Education","authors":"Rui Zhang","doi":"10.4018/ijwltt.329968","DOIUrl":"https://doi.org/10.4018/ijwltt.329968","url":null,"abstract":"With the continuous advancement of curriculum reform, how to improve the classroom efficiency and optimize the learning classroom model has become a topic of increasing concern for scholars and teachers. With the development and popularization of computer network, distance education has been widely used. It is in this case that the resource library came into being, which provides rich resources for Liu Xiye to make multimedia courseware. The learning of courses plays a fundamental role in education, which is reflected in modern distance education, that is, how to make courseware better. At the same time, online courses are also getting more and more attention from people. Online educators have higher and higher requirements for the design of each part of online courses. How to design feedback in the learning of online courses is also a must for every designer. Based on artificial intelligence education courseware, this paper introduces some audio and video technologies used in making multimedia CAI (computer-assisted instruction) courseware.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135150464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article is based on the application research of multimedia feature extraction technology in the development of vocal performance teaching in universities. Combining with the core image and sound modules in feature extraction technology, this article proposes an application model of multimedia feature extraction technology based on image HOG algorithm and Mel spectrum for vocal audio recognition in vocal performance teaching in universities. Experiments have shown that the features extracted by this method can not only effectively identify the styles of different types of singing works, but also recognize the personality characteristics of singers. At the same time, it can effectively reduce the misclassification rate caused by noise interference, thereby improving the recognition rate.
{"title":"Analysis of Multimedia Feature Extraction Technology in College Vocal Performance Teaching Mode Based on Multimodal Multimedia Information","authors":"Weijuan Nie, Wan Ng","doi":"10.4018/ijwltt.329604","DOIUrl":"https://doi.org/10.4018/ijwltt.329604","url":null,"abstract":"This article is based on the application research of multimedia feature extraction technology in the development of vocal performance teaching in universities. Combining with the core image and sound modules in feature extraction technology, this article proposes an application model of multimedia feature extraction technology based on image HOG algorithm and Mel spectrum for vocal audio recognition in vocal performance teaching in universities. Experiments have shown that the features extracted by this method can not only effectively identify the styles of different types of singing works, but also recognize the personality characteristics of singers. At the same time, it can effectively reduce the misclassification rate caused by noise interference, thereby improving the recognition rate.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43230927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The test results show that the fast Fourier process with multiple time superposition and a dimension length of 40 is most beneficial to the accuracy of the model. The loss curve value of the convolutional recurrent network model (CRN) is much lower than the other three models. The music tone recognition model learns better. The accuracy rate value and recall rate value of the CRN are the highest, and the accuracy rates of the four music tone indicators are 94.6%, 92.4%, 93.5%, 92.5%, and the recall rates were 93.2%, 94.9%, 95.2%, and 88.6% respectively; the improved algorithm was the most accurate in terms of F1 values and is suitable for use in vocal music teaching courses. The results show that the algorithm can be broadly performed in the zone of music tone recognition and has a certain contribution to the development of the field of music tone recognition.
{"title":"Research on Musical Tone Recognition Method Based on Improved RNN for Vocal Music Teaching Network Courses","authors":"Kaiyi Long","doi":"10.4018/ijwltt.327948","DOIUrl":"https://doi.org/10.4018/ijwltt.327948","url":null,"abstract":"The test results show that the fast Fourier process with multiple time superposition and a dimension length of 40 is most beneficial to the accuracy of the model. The loss curve value of the convolutional recurrent network model (CRN) is much lower than the other three models. The music tone recognition model learns better. The accuracy rate value and recall rate value of the CRN are the highest, and the accuracy rates of the four music tone indicators are 94.6%, 92.4%, 93.5%, 92.5%, and the recall rates were 93.2%, 94.9%, 95.2%, and 88.6% respectively; the improved algorithm was the most accurate in terms of F1 values and is suitable for use in vocal music teaching courses. The results show that the algorithm can be broadly performed in the zone of music tone recognition and has a certain contribution to the development of the field of music tone recognition.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77766966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of information technology, blended learning has been widely used in the education field, and the evaluation of blended learning effect has become one of the research hotspots. Taking the automobile theory course as an example, a blended learning process with online and offline is designed, and the main learning behaviors that affect learning effect are analyzed. By extracting data on the main learning behaviors of students during the learning process, correlation and linear regression methods are used to analyze the influencing factors of blended learning effect, and a linear regression prediction model is established. The results show that students' online testing, classroom performance, unit testing, feature assessment, and experimental performance are key indicators for predicting learning performance. According to the analysis of influencing factors of blended learning, the countermeasures and suggestions for improving the effect of blended learning are proposed.
{"title":"Study on the Evaluation Method of Blended Learning Effect Based on Multiple Linear Regression Analysis","authors":"Peijiang Chen, Xueyin Yang","doi":"10.4018/ijwltt.327453","DOIUrl":"https://doi.org/10.4018/ijwltt.327453","url":null,"abstract":"With the development of information technology, blended learning has been widely used in the education field, and the evaluation of blended learning effect has become one of the research hotspots. Taking the automobile theory course as an example, a blended learning process with online and offline is designed, and the main learning behaviors that affect learning effect are analyzed. By extracting data on the main learning behaviors of students during the learning process, correlation and linear regression methods are used to analyze the influencing factors of blended learning effect, and a linear regression prediction model is established. The results show that students' online testing, classroom performance, unit testing, feature assessment, and experimental performance are key indicators for predicting learning performance. According to the analysis of influencing factors of blended learning, the countermeasures and suggestions for improving the effect of blended learning are proposed.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75372585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Tambunan, M. Silitonga, Nelson Sinaga, Tanggapan C. Tampubolon
The authors develop a teacher-parent partnership-based learning system and apply it to investigate through experimentation. Samples were taken by multistage random sampling and placed in two groups. The experiment group involved 56 elementary schools (899 students and 899 parents), and the control group (without using the system) was fifty-two schools (541 students). Describing student competency data using descriptive statistics and competence in the experimental group was tested through one-way ANOVA, Sig. = .05. The students' competence with the partnership-based learning system was better. Student competence in the group of parents' work type, educational level, and economic level appears to vary. The kind of parents' work interacted with parents' academic rank and parents' financial status levels. In conclusion, various parents' backgrounds play a crucial role in partnership learning through internet-based learning systems, which must be considered in learning system use.
{"title":"Students Learning Outcomes Through the Teacher-Parent Partnership Learning System: Parent Background and School Type Impacts","authors":"H. Tambunan, M. Silitonga, Nelson Sinaga, Tanggapan C. Tampubolon","doi":"10.4018/ijwltt.327281","DOIUrl":"https://doi.org/10.4018/ijwltt.327281","url":null,"abstract":"The authors develop a teacher-parent partnership-based learning system and apply it to investigate through experimentation. Samples were taken by multistage random sampling and placed in two groups. The experiment group involved 56 elementary schools (899 students and 899 parents), and the control group (without using the system) was fifty-two schools (541 students). Describing student competency data using descriptive statistics and competence in the experimental group was tested through one-way ANOVA, Sig. = .05. The students' competence with the partnership-based learning system was better. Student competence in the group of parents' work type, educational level, and economic level appears to vary. The kind of parents' work interacted with parents' academic rank and parents' financial status levels. In conclusion, various parents' backgrounds play a crucial role in partnership learning through internet-based learning systems, which must be considered in learning system use.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77977154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distance education between the student and the teacher through online sessions can make it difficult for a student who does not understand a concept to ask for clarification. Lack of a physical campus or social pressure from peers can demotivate students from completing their assignments. The framework of multi-intelligence English teaching based on cloud technology (MIET-CT) is introduced to solve these kinds of issues. The method of blended learning (BL) combines in-person instruction with digital resources to improve distance and open education by examining the efficacy of a learning strategy, with an emphasis on collaborative and autonomous learning (CAL) by artificial intelligence (AI). Cloud technology can potentially encourage students' independent learning as a cognitive tool by providing a cloud platform and multimedia instruction by domain modeling. As a result, various English teaching styles have been shown to increase student's motivation to learn and provide more impressive classroom results than conventional methods.
{"title":"Multi-Intelligence English Teaching Model Based on Distance and Open Education","authors":"Jinjin Chu, Maciej Szlagor","doi":"10.4018/ijwltt.325617","DOIUrl":"https://doi.org/10.4018/ijwltt.325617","url":null,"abstract":"Distance education between the student and the teacher through online sessions can make it difficult for a student who does not understand a concept to ask for clarification. Lack of a physical campus or social pressure from peers can demotivate students from completing their assignments. The framework of multi-intelligence English teaching based on cloud technology (MIET-CT) is introduced to solve these kinds of issues. The method of blended learning (BL) combines in-person instruction with digital resources to improve distance and open education by examining the efficacy of a learning strategy, with an emphasis on collaborative and autonomous learning (CAL) by artificial intelligence (AI). Cloud technology can potentially encourage students' independent learning as a cognitive tool by providing a cloud platform and multimedia instruction by domain modeling. As a result, various English teaching styles have been shown to increase student's motivation to learn and provide more impressive classroom results than conventional methods.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89974789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, this paper proposes an English teaching ability estimation algorithm based on big data fuzzy K-means clustering. Firstly, the article establishes a constraint parameter index analysis model. Secondly, quantitative recursive analysis is used to evaluate the capabilities of big data information models and achieve entropy feature extraction of capability constrained feature information. Finally, by integrating big data information fusion and K-means clustering algorithm, the article achieves clustering and integration of indicator parameters for English teaching ability, prepares corresponding teaching resource allocation plans, and evaluates English teaching ability. The experimental results show that using this method to evaluate English teaching ability has good information fusion analysis ability and improves the accuracy of teaching ability evaluation and the efficiency of teaching resource application.
{"title":"The Evaluation Algorithm of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering","authors":"Lili Qin, Weixuan Zhong, Hugh C. Davis","doi":"10.4018/ijwltt.325348","DOIUrl":"https://doi.org/10.4018/ijwltt.325348","url":null,"abstract":"In response to the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, this paper proposes an English teaching ability estimation algorithm based on big data fuzzy K-means clustering. Firstly, the article establishes a constraint parameter index analysis model. Secondly, quantitative recursive analysis is used to evaluate the capabilities of big data information models and achieve entropy feature extraction of capability constrained feature information. Finally, by integrating big data information fusion and K-means clustering algorithm, the article achieves clustering and integration of indicator parameters for English teaching ability, prepares corresponding teaching resource allocation plans, and evaluates English teaching ability. The experimental results show that using this method to evaluate English teaching ability has good information fusion analysis ability and improves the accuracy of teaching ability evaluation and the efficiency of teaching resource application.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86967646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the difficulties of speech signal processing, there is still a considerable gap between the ability of machines to correctly process and that of human beings. In order to overcome the defects of isolated learning and noise sensitivity of SOM, this paper proposes a new time self-organization model (TSOM) from the perspective of deep learning. On the basis of self-organizing mapping network, time enhancement mechanism is introduced to improve the system performance. This method makes up for the fixed spatial topology of the original self-organizing mapping network and the neglect of the time factor, which is crucial to the voice signal. At the same time, this paper makes full use of computer-aided technology and rich network resources to provide a comprehensive and systematic English pronunciation learning database and establish learners' pronunciation files. Once learners understand and master the operation of voice analysis software, they can conduct self-assessment and judgment to find out their blind spots and weaknesses in voice acquisition.
{"title":"The Reform of Pronunciation Teaching in Colleges and Universities by Praat Software From the Perspective of Deep Learning","authors":"Khuselt It","doi":"10.4018/ijwltt.325225","DOIUrl":"https://doi.org/10.4018/ijwltt.325225","url":null,"abstract":"Due to the difficulties of speech signal processing, there is still a considerable gap between the ability of machines to correctly process and that of human beings. In order to overcome the defects of isolated learning and noise sensitivity of SOM, this paper proposes a new time self-organization model (TSOM) from the perspective of deep learning. On the basis of self-organizing mapping network, time enhancement mechanism is introduced to improve the system performance. This method makes up for the fixed spatial topology of the original self-organizing mapping network and the neglect of the time factor, which is crucial to the voice signal. At the same time, this paper makes full use of computer-aided technology and rich network resources to provide a comprehensive and systematic English pronunciation learning database and establish learners' pronunciation files. Once learners understand and master the operation of voice analysis software, they can conduct self-assessment and judgment to find out their blind spots and weaknesses in voice acquisition.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80330940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a stakeholder group in the promotion of basic education informatization, parents' attitudes towards children's informatization learning is an important factor affecting the smooth development of school informatization teaching. Based on the classic convolutional neural network and CK+ dataset, this paper proposes a convolutional neural network model to evaluate the attitude of parents to children's information-based learning in the context of large-scale online learning and multimedia technology. It aims to provide an important reference for promoting the informatization teaching reform in the basic education stage in the post-pandemic era. The experiment shows that the convolution neural network model proposed in this paper can accurately identify the facial information of learners in the live classroom. Based on learners' emotional changes, teachers can adjust teaching strategies in time to improve the teaching process.
{"title":"The Attitudes of Students' Parents Towards Their Children's Information-Based Learning Under the Background of the Combination of Large-Scale Online Learning and Multimedia Technology","authors":"Zhe Liu","doi":"10.4018/ijwltt.320519","DOIUrl":"https://doi.org/10.4018/ijwltt.320519","url":null,"abstract":"As a stakeholder group in the promotion of basic education informatization, parents' attitudes towards children's informatization learning is an important factor affecting the smooth development of school informatization teaching. Based on the classic convolutional neural network and CK+ dataset, this paper proposes a convolutional neural network model to evaluate the attitude of parents to children's information-based learning in the context of large-scale online learning and multimedia technology. It aims to provide an important reference for promoting the informatization teaching reform in the basic education stage in the post-pandemic era. The experiment shows that the convolution neural network model proposed in this paper can accurately identify the facial information of learners in the live classroom. Based on learners' emotional changes, teachers can adjust teaching strategies in time to improve the teaching process.","PeriodicalId":39282,"journal":{"name":"International Journal of Web-Based Learning and Teaching Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78846033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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