基于集成元学习分类器的残疾学生服务选择

Abdallah Namoun, M. Humayun, Oussama Benrhouma, Burhan Rashid Hussein, Ali Tufail, Abdullah M. Alshanqiti, Waqas Nawaz
{"title":"基于集成元学习分类器的残疾学生服务选择","authors":"Abdallah Namoun, M. Humayun, Oussama Benrhouma, Burhan Rashid Hussein, Ali Tufail, Abdullah M. Alshanqiti, Waqas Nawaz","doi":"10.3390/mti7050042","DOIUrl":null,"url":null,"abstract":"Students with special needs should be empowered to use assistive technologies and services that suit their individual circumstances and environments to maximize their learning attainment. Fortunately, modern distributed computing paradigms, such as the Internet of Things (IoT), cloud computing, and mobile computing, provide ample opportunities to create and offer a multitude of digital assistive services and devices for people with disabilities. However, choosing the appropriate services from a pool of competing services while satisfying the unique requirements of disabled learners remains a challenging research endeavor. In this article, we propose an ensemble meta-learning model that ranks and selects the best IoT services while considering the diverse needs of disabled students within the educational context. We train and test our deep ensemble meta-learning model using two synthetically generated assistive services datasets. The first dataset incorporates 50,000 records representing the possible use of 12 learning activities, fulfilled by 60 distinct assistive services. The second dataset includes a range of 120,000 service ratings of seven quality features, including response, availability, successibility, latency, cost, quality of service, and accessibility. Our deep learning model uses an ensemble of multiple input learners fused using a meta-classification network shared by all the outputs representing individual assistive services. The model achieves significantly better results than traditional machine learning models (i.e., support vector machine and random forest) and a simple feed-forward neural network model without the ensemble technique. Furthermore, we extended our model to utilize the accessibility rating of services to suggest appropriate educational services for disabled learners. The empirical results show the acceptability of our assistive service recommender for learners with disabilities.","PeriodicalId":408374,"journal":{"name":"Multimodal Technol. Interact.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Service Selection Using an Ensemble Meta-Learning Classifier for Students with Disabilities\",\"authors\":\"Abdallah Namoun, M. Humayun, Oussama Benrhouma, Burhan Rashid Hussein, Ali Tufail, Abdullah M. Alshanqiti, Waqas Nawaz\",\"doi\":\"10.3390/mti7050042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Students with special needs should be empowered to use assistive technologies and services that suit their individual circumstances and environments to maximize their learning attainment. Fortunately, modern distributed computing paradigms, such as the Internet of Things (IoT), cloud computing, and mobile computing, provide ample opportunities to create and offer a multitude of digital assistive services and devices for people with disabilities. However, choosing the appropriate services from a pool of competing services while satisfying the unique requirements of disabled learners remains a challenging research endeavor. In this article, we propose an ensemble meta-learning model that ranks and selects the best IoT services while considering the diverse needs of disabled students within the educational context. We train and test our deep ensemble meta-learning model using two synthetically generated assistive services datasets. The first dataset incorporates 50,000 records representing the possible use of 12 learning activities, fulfilled by 60 distinct assistive services. The second dataset includes a range of 120,000 service ratings of seven quality features, including response, availability, successibility, latency, cost, quality of service, and accessibility. Our deep learning model uses an ensemble of multiple input learners fused using a meta-classification network shared by all the outputs representing individual assistive services. The model achieves significantly better results than traditional machine learning models (i.e., support vector machine and random forest) and a simple feed-forward neural network model without the ensemble technique. Furthermore, we extended our model to utilize the accessibility rating of services to suggest appropriate educational services for disabled learners. The empirical results show the acceptability of our assistive service recommender for learners with disabilities.\",\"PeriodicalId\":408374,\"journal\":{\"name\":\"Multimodal Technol. Interact.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multimodal Technol. Interact.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/mti7050042\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimodal Technol. Interact.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/mti7050042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

应赋予有特殊需要的学生使用适合其个人情况和环境的辅助技术和服务的权力,以最大限度地提高他们的学习成绩。幸运的是,现代分布式计算范式,如物联网(IoT)、云计算和移动计算,为残疾人创建和提供大量数字辅助服务和设备提供了充足的机会。然而,在满足残疾学习者的独特需求的同时,从竞争服务池中选择合适的服务仍然是一项具有挑战性的研究工作。在本文中,我们提出了一个集成元学习模型,该模型在考虑残疾学生在教育背景下的不同需求的同时,对最佳物联网服务进行排名和选择。我们使用两个综合生成的辅助服务数据集训练和测试我们的深度集成元学习模型。第一个数据集包含50,000条记录,代表了12种学习活动的可能使用,由60种不同的辅助服务实现。第二个数据集包括7个质量特征的120,000个服务评级,包括响应、可用性、可持续性、延迟、成本、服务质量和可访问性。我们的深度学习模型使用多输入学习器的集成,融合了由代表个人辅助服务的所有输出共享的元分类网络。该模型明显优于传统的机器学习模型(即支持向量机和随机森林)和没有集成技术的简单前馈神经网络模型。此外,我们扩展了我们的模型,利用服务的可及性评级来为残疾学习者提供合适的教育服务。实证结果表明,我们的辅助服务推荐对有障碍的学习者是可接受的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Service Selection Using an Ensemble Meta-Learning Classifier for Students with Disabilities
Students with special needs should be empowered to use assistive technologies and services that suit their individual circumstances and environments to maximize their learning attainment. Fortunately, modern distributed computing paradigms, such as the Internet of Things (IoT), cloud computing, and mobile computing, provide ample opportunities to create and offer a multitude of digital assistive services and devices for people with disabilities. However, choosing the appropriate services from a pool of competing services while satisfying the unique requirements of disabled learners remains a challenging research endeavor. In this article, we propose an ensemble meta-learning model that ranks and selects the best IoT services while considering the diverse needs of disabled students within the educational context. We train and test our deep ensemble meta-learning model using two synthetically generated assistive services datasets. The first dataset incorporates 50,000 records representing the possible use of 12 learning activities, fulfilled by 60 distinct assistive services. The second dataset includes a range of 120,000 service ratings of seven quality features, including response, availability, successibility, latency, cost, quality of service, and accessibility. Our deep learning model uses an ensemble of multiple input learners fused using a meta-classification network shared by all the outputs representing individual assistive services. The model achieves significantly better results than traditional machine learning models (i.e., support vector machine and random forest) and a simple feed-forward neural network model without the ensemble technique. Furthermore, we extended our model to utilize the accessibility rating of services to suggest appropriate educational services for disabled learners. The empirical results show the acceptability of our assistive service recommender for learners with disabilities.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信