In the context of college physical education curriculum reform, fostering students' interest and promoting lifelong physical exercise have become crucial. Aerobics, an integral component of physical education, plays a key role in achieving these objectives. However, existing data flow analysis technologies lack integration, limiting their ability to leverage information from various sources. To address this issue, this paper proposes an aerobics teaching model utilizing few-shot learning technology for data flow analysis. The model incorporates a label feature network based on metric learning, enhancing its ability to analyze multi-scale features and label features within classes. Comparative analysis demonstrates an 8.12% improvement in accuracy compared to traditional image feature combined classifier models.
{"title":"Aerobics Teaching With Few-Shot Learning Technology for Data Flow Analysis","authors":"Qiuping Peng, Ningfei Wei","doi":"10.4018/ijicte.349586","DOIUrl":"https://doi.org/10.4018/ijicte.349586","url":null,"abstract":"In the context of college physical education curriculum reform, fostering students' interest and promoting lifelong physical exercise have become crucial. Aerobics, an integral component of physical education, plays a key role in achieving these objectives. However, existing data flow analysis technologies lack integration, limiting their ability to leverage information from various sources. To address this issue, this paper proposes an aerobics teaching model utilizing few-shot learning technology for data flow analysis. The model incorporates a label feature network based on metric learning, enhancing its ability to analyze multi-scale features and label features within classes. Comparative analysis demonstrates an 8.12% improvement in accuracy compared to traditional image feature combined classifier models.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800364","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}
Zhengrong Deng, Hong Xiang, Weijun Tang, Hanlie Cheng, Qiang Qin
This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.
本文采用 BP 神经网络（BPNN）理论对高校创新创业教育进行评价。它利用学生的评价指标作为输入向量，并确定隐层神经元的数量。实验结果作为输出向量。事实证明，BPNN 方法在职业教育课程评价中是合理可行的，其准确率比传统遗传算法高出 14.96%。本文讨论了神经网络的模型、配置、特点、训练过程、算法改进和局限性，然后介绍了遗传算法。通过对原理、基本操作和常用运算符的分析，为后续讨论奠定了理论基础。
{"title":"BP Neural Network-Enhanced System for Employment and Mental Health Support for College Students","authors":"Zhengrong Deng, Hong Xiang, Weijun Tang, Hanlie Cheng, Qiang Qin","doi":"10.4018/ijicte.348334","DOIUrl":"https://doi.org/10.4018/ijicte.348334","url":null,"abstract":"This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810498","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}
Godwin Kaisara, Clayton Peel, C. J. Niemand, K. Bwalya
The COVID-19 period ushered in a paradigmatic shift towards exponential growth of ubiquitous e-learning. Despite the well-documented benefits of e-learning, which received unprecedented attention during the COVID-19 pandemic, little has been reported on factors influencing student dropout rates in courses delivered via e-learning. In this paper, the authors explore the factors contributing to student discontinuations in nonvolitional postpandemic conditions. Adopting a multimethod qualitative research design, the authors investigated the factors leading to increased student dropout rates from e-learning programs. The researchers used thematic analysis to interpret the data, resulting in the emergence of five themes. The findings reveal several factors contributing to failure to complete studies on programs delivered via e-learning. Although not exclusively conclusive, the study's findings indicate skills gap solutions and resource concerns which need to be addressed to convert market demand and enrolment into optimum completion rates, thereby increasing e-learning's success.
{"title":"Exploring Factors Influencing e-Learning Dropout Rates in the Post-COVID-19 Era","authors":"Godwin Kaisara, Clayton Peel, C. J. Niemand, K. Bwalya","doi":"10.4018/ijicte.348660","DOIUrl":"https://doi.org/10.4018/ijicte.348660","url":null,"abstract":"The COVID-19 period ushered in a paradigmatic shift towards exponential growth of ubiquitous e-learning. Despite the well-documented benefits of e-learning, which received unprecedented attention during the COVID-19 pandemic, little has been reported on factors influencing student dropout rates in courses delivered via e-learning. In this paper, the authors explore the factors contributing to student discontinuations in nonvolitional postpandemic conditions. Adopting a multimethod qualitative research design, the authors investigated the factors leading to increased student dropout rates from e-learning programs. The researchers used thematic analysis to interpret the data, resulting in the emergence of five themes. The findings reveal several factors contributing to failure to complete studies on programs delivered via e-learning. Although not exclusively conclusive, the study's findings indicate skills gap solutions and resource concerns which need to be addressed to convert market demand and enrolment into optimum completion rates, thereby increasing e-learning's success.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824255","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 objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.
{"title":"Study on the Application of Error Back-Propagation Algorithm Applied to the Student Status Management in Higher Education Institutions","authors":"XinXiu Yang","doi":"10.4018/ijicte.348960","DOIUrl":"https://doi.org/10.4018/ijicte.348960","url":null,"abstract":"The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825576","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 rapid development of Internet technology, distance online education and training is becoming an important part of the education and training market. Based on the theory of perceived value, taking the distance online education and training platform as the research object, this paper establishes a sample database, analyzes the reliability, validity, correlation and regression of the data, obtains the main factors of parents' perceived service quality, and analyzes the relationship between parents' perceived service quality and satisfaction and willingness to act, so as to provide ideas for improving the brand awareness of distance online education and training. The research results provide theoretical data support for parents to perceive the brand recognition of distance online education and training.
{"title":"Influence of Parents' Perceptions of Brand Recognition of Distance Online Education and Training","authors":"DongMei Xu","doi":"10.4018/ijicte.348961","DOIUrl":"https://doi.org/10.4018/ijicte.348961","url":null,"abstract":"With the rapid development of Internet technology, distance online education and training is becoming an important part of the education and training market. Based on the theory of perceived value, taking the distance online education and training platform as the research object, this paper establishes a sample database, analyzes the reliability, validity, correlation and regression of the data, obtains the main factors of parents' perceived service quality, and analyzes the relationship between parents' perceived service quality and satisfaction and willingness to act, so as to provide ideas for improving the brand awareness of distance online education and training. The research results provide theoretical data support for parents to perceive the brand recognition of distance online education and training.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826662","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 the context of the multimedia era, college counselors and college students should make progress together. This article aims to explore the practical issues of applying multimedia and visual-image technology to the training of college counselors. We propose a three-dimensional multimedia visual image recognition technology based on convolutional neural networks (CNNs) and apply the algorithm to image-recognition tasks to provide support and assistance for college-counselor training in network and multimedia environments. By combining a CNN with image recognition, our research results show that this method can effectively adapt to different types of image-recognition tasks. This means that our algorithm can be fully applied to these tasks and provide strong support for the training of college counselors.
{"title":"Practical Research on the Application of Multimedia and Visual Image Technology in the Cultivation of College Counselors in the Network Environment","authors":"Rui Du","doi":"10.4018/ijicte.349570","DOIUrl":"https://doi.org/10.4018/ijicte.349570","url":null,"abstract":"In the context of the multimedia era, college counselors and college students should make progress together. This article aims to explore the practical issues of applying multimedia and visual-image technology to the training of college counselors. We propose a three-dimensional multimedia visual image recognition technology based on convolutional neural networks (CNNs) and apply the algorithm to image-recognition tasks to provide support and assistance for college-counselor training in network and multimedia environments. By combining a CNN with image recognition, our research results show that this method can effectively adapt to different types of image-recognition tasks. This means that our algorithm can be fully applied to these tasks and provide strong support for the training of college counselors.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828565","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 social economy, the globalization of the world has become an inevitable trend. However, the cultures of various countries and nationalities in the world are constantly communicating and merging with each other; the folk art in China has been greatly impacted, and the traditional culture with distinctive national characteristics has been rejected. Strengthening folk art education is needed not only to improve people's personality and cultivate people's aesthetic taste, but also to protect traditional culture and impart national spirit. With the rapid development of information technology relying on hand-held learning equipment, mobile internet technology, and network multimedia technology, life-long learning is possible. Using smart mobile devices to study the art teaching of folk art is conducive to in-depth exploration, applying the learning methods of folk art to our own study and life, and protecting and inheriting the infringed folk art.
{"title":"Research on the Path of Folk Art","authors":"Xiangqun Wang","doi":"10.4018/ijicte.347668","DOIUrl":"https://doi.org/10.4018/ijicte.347668","url":null,"abstract":"With the development of social economy, the globalization of the world has become an inevitable trend. However, the cultures of various countries and nationalities in the world are constantly communicating and merging with each other; the folk art in China has been greatly impacted, and the traditional culture with distinctive national characteristics has been rejected. Strengthening folk art education is needed not only to improve people's personality and cultivate people's aesthetic taste, but also to protect traditional culture and impart national spirit. With the rapid development of information technology relying on hand-held learning equipment, mobile internet technology, and network multimedia technology, life-long learning is possible. Using smart mobile devices to study the art teaching of folk art is conducive to in-depth exploration, applying the learning methods of folk art to our own study and life, and protecting and inheriting the infringed folk art.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829295","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 study examines the current research on educational data mining, educational learning support services, personalized learning services, and personalized learning paths in education. The authors aim to integrate personalized learning concepts into traditional support services by drawing on the latest theoretical and practical research. Using multimodal data fusion techniques, the study conduct exploratory analyses on various data types, including learner academic performance, psychological assessments, learning behavior, and physiological information. This leads to the construction of a personalized education learning support service model. The model focuses on objectives such as monitoring learning behavior, identifying preferences, recognizing abilities, optimizing paths, and recommending resources. The goal is to provide learners with sustained support services throughout the personalized learning process, addressing individual needs, fostering enthusiasm, and maintaining long-term motivation.
{"title":"Construction and Innovative Exploration of Personalized Learning Systems in the Context of Educational Data Mining","authors":"Xingle Ji, Lu Sun, Xueyong Xu, Xiaobing Lei","doi":"10.4018/ijicte.346992","DOIUrl":"https://doi.org/10.4018/ijicte.346992","url":null,"abstract":"This study examines the current research on educational data mining, educational learning support services, personalized learning services, and personalized learning paths in education. The authors aim to integrate personalized learning concepts into traditional support services by drawing on the latest theoretical and practical research. Using multimodal data fusion techniques, the study conduct exploratory analyses on various data types, including learner academic performance, psychological assessments, learning behavior, and physiological information. This leads to the construction of a personalized education learning support service model. The model focuses on objectives such as monitoring learning behavior, identifying preferences, recognizing abilities, optimizing paths, and recommending resources. The goal is to provide learners with sustained support services throughout the personalized learning process, addressing individual needs, fostering enthusiasm, and maintaining long-term motivation.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830734","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 study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an “interest” parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's efficacy by correlating learning content with academic performance, specifically CET-4 and CET-6 scores. Comprehensive preprocessing ensures data quality and privacy, employing techniques like de-identification, data perturbation, and aggregation. Advanced data analysis, including cross-validation and multivariate techniques, bolsters findings' reliability. Results highlight the ARM model's capacity to generate personalized learning paths, transcending conventional methods, and its potential as a cornerstone for data-driven education reforms. Future research will explore machine learning refinements and cultural adaptability to broaden its impact, fostering equitable, high-quality digital English education worldwide.
本研究通过应用改进的关联规则挖掘（ARM）算法，对英语网络教学进行了创新。它将 "兴趣 "参数整合到关联规则挖掘算法中，根据学习者的个人情况动态调整教学内容，提高学习者的参与度和学习效果。通过将学习内容与学习成绩（特别是 CET-4 和 CET-6 分数）相关联，跨不同在线平台的对照实验验证了 ARM 模型的有效性。全面的预处理确保了数据质量和隐私，采用了去标识化、数据扰动和聚合等技术。包括交叉验证和多元技术在内的高级数据分析增强了研究结果的可靠性。研究结果凸显了 ARM 模型生成个性化学习路径、超越传统方法的能力，以及作为数据驱动型教育改革基石的潜力。未来的研究将探索机器学习的改进和文化适应性，以扩大其影响，促进全球公平、高质量的数字英语教育。
{"title":"English Network Teaching Model and Design of Evaluation System Based on Association Rule Algorithm","authors":"Xu Sun, Ting Wang","doi":"10.4018/ijicte.349007","DOIUrl":"https://doi.org/10.4018/ijicte.349007","url":null,"abstract":"This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an “interest” parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's efficacy by correlating learning content with academic performance, specifically CET-4 and CET-6 scores. Comprehensive preprocessing ensures data quality and privacy, employing techniques like de-identification, data perturbation, and aggregation. Advanced data analysis, including cross-validation and multivariate techniques, bolsters findings' reliability. Results highlight the ARM model's capacity to generate personalized learning paths, transcending conventional methods, and its potential as a cornerstone for data-driven education reforms. Future research will explore machine learning refinements and cultural adaptability to broaden its impact, fostering equitable, high-quality digital English education worldwide.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829894","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 effort to implement personalized teaching in blended learning environments faces various challenges, such as the diversity of needs among learners; educators must find ways to identify, understand, and address these differences. This study explores how to construct a learner profile model in blended learning environments, develop personalized teaching strategies based on learner profiles, and evaluate the effectiveness of these strategies. The results demonstrate that personalized teaching strategies significantly enhance learner engagement, autonomy, and academic performance. These strategies have been validated through satisfaction surveys conducted with both teachers and students. The study provides a theoretical framework and practical guidance for personalized teaching while also highlighting challenges in implementation. It is recommended that future research expand sample sizes, integrate quantitative and qualitative research methods, and pay closer attention to learners' personalized needs to further enhance the practicality and effectiveness of personalized teaching strategies.
{"title":"Research on Personalized Teaching Strategies Based on Learner Profiles in a Blended Learning Environment","authors":"Bing Liu, Dongbin Yuan","doi":"10.4018/ijicte.346823","DOIUrl":"https://doi.org/10.4018/ijicte.346823","url":null,"abstract":"The effort to implement personalized teaching in blended learning environments faces various challenges, such as the diversity of needs among learners; educators must find ways to identify, understand, and address these differences. This study explores how to construct a learner profile model in blended learning environments, develop personalized teaching strategies based on learner profiles, and evaluate the effectiveness of these strategies. The results demonstrate that personalized teaching strategies significantly enhance learner engagement, autonomy, and academic performance. These strategies have been validated through satisfaction surveys conducted with both teachers and students. The study provides a theoretical framework and practical guidance for personalized teaching while also highlighting challenges in implementation. It is recommended that future research expand sample sizes, integrate quantitative and qualitative research methods, and pay closer attention to learners' personalized needs to further enhance the practicality and effectiveness of personalized teaching strategies.","PeriodicalId":55970,"journal":{"name":"International Journal of Information and Communication Technology Education","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641568","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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