2022 International Conference on Intelligent Education and Intelligent Research (IEIR)最新文献_第5页

Design a Cloud-enabled Humanoid Robot Application System to Assess the ABA Learning for Autistic Children 设计一个基于云的人形机器人应用系统来评估自闭症儿童的ABA学习

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050050

Ziyuan Wang, Yiwei Chen, Xiaojun Hei

{"title":"Design a Cloud-enabled Humanoid Robot Application System to Assess the ABA Learning for Autistic Children","authors":"Ziyuan Wang, Yiwei Chen, Xiaojun Hei","doi":"10.1109/IEIR56323.2022.10050050","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050050","url":null,"abstract":"In recent years, the autistic community has gradually attracted widespread social attention. There are currently many difficulties with autism therapies in China, such as high market demand, high prices and inadequate autism teachers. With the continuous development of robotics and digital therapeutics in recent years, there are increasingly potentials of applying robotics to the autism companionship therapy. To demonstrate this possibility, in this paper we apply the cloud-based robotics and the practical needs of the Applied- Behaviour-Analysis (ABA) learning for autistic children, and design an a cloud-enabled humanoid robot application system, in order to reduce the teacher’s workload. We look into a typical ABA therapy process for autistic children to recognize fruits. Following the Childhood Autism Rating Scale and the Autism Behavior Checklist, we design an autistic child digital model to interact with this humanoid robot system in a Robotic Development Kit (RDK) virtual environment. This digital autistic children model mimes autistic children behaviors. Our humanoid robot is en-powered with cloud-enabled intelligence to recognize the emotions and actions of this digital autistic child in order to assess the cognitive progress in his ABA learning. This robotic system also implements various functions to enable interaction with this digital autistic child, such as automatic navigation, intelligent voice interaction, visual recognition and robotic dance. Our system prototype demonstrates the feasibility of designing a cloud-enabled humanoid robot to accompany autistic children.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130339922","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}

引用次数: 0

Approaches and Quality of Algorithm Evaluation 算法评价的方法和质量

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050072

Xinguo Yu, Jing Xia, Weina Cheng

引用次数: 0

Explore the interrelationship of cognition, emotion and interaction when learners engage in online discussion 探讨学习者参与在线讨论时认知、情感和互动的相互关系

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050057

Zhu Su, Yue Li, Sannyuya Liu, Liang Zhao, Zhi Liu, Xian Peng

{"title":"Explore the interrelationship of cognition, emotion and interaction when learners engage in online discussion","authors":"Zhu Su, Yue Li, Sannyuya Liu, Liang Zhao, Zhi Liu, Xian Peng","doi":"10.1109/IEIR56323.2022.10050057","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050057","url":null,"abstract":"Online learning community provides abundant semantic text information for investigating learning behaviors. Despite the increasing of learning behavior in higher education, few previous researches have studied online learning from multidimensional behavior. This study mainly explores the interrelationship of cognition, emotion and interaction when learners engage in online discussion. The research object is online discussion textual from a certain course. First, lag sequence analysis is adopted to analyze the dynamic of cognition from the micro perspective, and the relationship between cognition and emotion combined with emotion analysis is investigated. Furthermore, this study proposes the standards of quantifying cognition and emotion from the macro perspective, and the cognition, emotion and interaction are analyzed in a unified framework by using social network analysis. Our findings suggest that: (1) Cognitive level is stable during the learning process, and can be improved by continuous thinking and analysis. (2) Positive emotion plays a significant role in developing higher-level cognition, and its value increases gradually with the improvement of cognition level. (3) Network structure has important influence on individual cognition, who with higher cognitive levels are usually in the center of the network and have larger interaction quality. (4) The learning community with more intensive interactions show higher positive emotion, indicating that emotion transmission is realized by means of network structure. This study might give theoretical and technical supports for helping learners improve the learning quality and efficiency in the online learning.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114684477","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}

引用次数: 0

A BERT-Based Pre-Training Model for Solving Math Application Problems 基于bert的数学应用问题预训练模型

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050073

Yuhao Jia, Pingheng Wang, Zhen Zhang, Chi Cheng, Zhifei Li, Xinguo Yu

{"title":"A BERT-Based Pre-Training Model for Solving Math Application Problems","authors":"Yuhao Jia, Pingheng Wang, Zhen Zhang, Chi Cheng, Zhifei Li, Xinguo Yu","doi":"10.1109/IEIR56323.2022.10050073","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050073","url":null,"abstract":"Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129727652","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}

引用次数: 0

The Spatio-temporal Hybrid Development Methodology for Smart IoT: A Review based Study 基于综述的智能物联网时空混合开发方法研究

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050075

Yazeed AlZahrani, Jun Shen, Jun Yan

{"title":"The Spatio-temporal Hybrid Development Methodology for Smart IoT: A Review based Study","authors":"Yazeed AlZahrani, Jun Shen, Jun Yan","doi":"10.1109/IEIR56323.2022.10050075","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050075","url":null,"abstract":"This paper deals with a review-based study on the efficient development methodologies for the deployment of IoT systems. Efficient hardware and software development reduces the risk of system bugs and faults. However, the optimal placement of the IoT devices is one of the major challenges for the monitoring applications. In this paper, a combined Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodology is proposed to build IoT software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional data-oriented methodologies. This methodology directs software systems to the specific goal in obtaining outputs inherent to the process. The hybrid methodology includes the support of tools integrated, and also fits the general purpose. This methodology repeats the structure of spatio-temporal reasoning. Segmentation and object detection are used for the division of the region into sub-regions. Furthermore, the coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"45 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127836786","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}

引用次数: 0

Prompt-Based Missing Entity Recovery for Solving Arithmetic Word Problems 基于提示的缺失实体恢复算法求解算术单词问题

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050063

Hao Meng, Liang Xue, Bin He, Xinguo Yu

引用次数: 0