2022 International Conference on Intelligent Education and Intelligent Research (IEIR)最新文献

Prospects and Challenges of Equipping Mathematics Tutoring Systems with Personalized Learning Strategies 为数学辅导系统配备个性化学习策略的前景与挑战

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050082

Xinguo Yu, Jing Xia, Weina Cheng

引用次数: 0

Scene Parsing via Tree Structure Enhancement Lightweight Network 基于树结构增强轻量级网络的场景解析

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050053

Wenxin Huang, Wenxuan Liu, Xuemei Jia

{"title":"Scene Parsing via Tree Structure Enhancement Lightweight Network","authors":"Wenxin Huang, Wenxuan Liu, Xuemei Jia","doi":"10.1109/IEIR56323.2022.10050053","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050053","url":null,"abstract":"Scene parsing is a hot topic in the field of computer vision communities. It has extensive applications in visual perception e.g. education system, human-object robots, etc. However, there exists a huge size difference among objects in the scene image because of the diversity of objects and the influence of observation distance and other factors. How to better solve the varying scale problem has become a challenging problem in scene parsing. Thus, a tree-structure is proposed to handle the varying scale problem, where the feature maps of different levels are gradually nested and connected, which strengthens the connection between multiple feature maps, and captures more representative information. For real-time, we propose a framework named tree structure enhancement lightweight network (TSELight), which introduces the depth-wise separable dilated convolution (DSDC) into the tree structure and decomposes the middle nodes in the tree structure along the channel direction, thus improving the efficiency. Experimental results demonstrate that our TSELight architecture outperforms state-of-the-art methods on Cityscapes dataset, and provides consistent improvements on the real-time scene parsing performance.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"57 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":"126698342","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

LFCKT: A Learning and Forgetting Convolutional Knowledge Tracking Model LFCKT:一种学习与遗忘卷积知识跟踪模型

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050085

Mengjuan Li, L. Niu, Jinhua Zhao, Yuchen Wang

{"title":"LFCKT: A Learning and Forgetting Convolutional Knowledge Tracking Model","authors":"Mengjuan Li, L. Niu, Jinhua Zhao, Yuchen Wang","doi":"10.1109/IEIR56323.2022.10050085","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050085","url":null,"abstract":"Personalized exercise recommendation is a key research direction of personalized learning. In personalized exercise recommendation, we recommend suitable exercises for students according to their knowledge mastery status to improve their learning efficiency. Therefore, the accuracy of predicting students’ knowledge state in personalized exercise recommendation affects the goodness of the exercise recommendation. In the process of students’ learning, learning behavior and forgetting behavior are intertwined, and students’ forgetting behavior has a great influence on the knowledge state. In order to accurately model students’ learning and forgetting, we propose a Learning and Forgetting Convolutional Knowledge Tracking model (LFCKT) that takes into account both learning and forgetting behaviors. The model takes into account three factors that affect knowledge forgetting, including the interval time of target knowledge interaction, the count of past target knowledge interaction and student’s state of knowledge. LFCKT model uses students’ answer results as indirect feedback of knowledge mastery in the process of knowledge tracking, and integrates individual personalized learning behavior and individual forgetting behavior. Through experiments on the real online education public dataset, LFCKT can better track students’ knowledge mastery status and has better predictive performance than current knowledge tracking models.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"30 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":"128900012","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

An empirical study on the factors influencing college students’ intention to use the English Vocabulary APP 大学生英语词汇APP使用意向影响因素的实证研究

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050079

Mengxi Yang, Yanyan Jin, Zhengyang Zhang, S. Lian, Xian Peng

{"title":"An empirical study on the factors influencing college students’ intention to use the English Vocabulary APP","authors":"Mengxi Yang, Yanyan Jin, Zhengyang Zhang, S. Lian, Xian Peng","doi":"10.1109/IEIR56323.2022.10050079","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050079","url":null,"abstract":"Vocabulary apps with their high accessibility and contextualization have made it a trend for college students for English learning. However, with the emergence of a new type of learning, it is a matter of concern how to improve user’s usage intention. This paper takes college students who use Maimemo App for learning as an example. Based on the Technology Acceptance Model, this paper explores the factors influencing user’s usage intention with words APP. The following conclusions are drawn: 1) According to the analyses of the moderating and mediating effects, memory pattern and resource optimization have positive effect on perceived usefulness and perceived ease of use; perceived usefulness and perceived ease of use indirectly affects users’ usage intention through self-efficacy; 2) This paper conducted multifactor analysis of variance on the data. The results show that the different duration and the learning contexts of people’s use with the English Vocabulary APP, the different extent of people’s usage intention. Users who have used the APP for “two to three months” are the ones who need more attention, and the demands of this group of users should be considered more deeply, so as to provide reference for the improvement of the English vocabulary apps.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"2002 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":"127399584","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

Analysis of Group Online Collaborative Learning Based on Log Data and ICAP 基于日志数据和ICAP的小组在线协作学习分析

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050064

Xiuling He, Chenyang Wang, Yangyang Li, Zhipin Peng, Jing Fang

引用次数: 0

YOLOv5 Enhanced Learning Behavior Recognition and Analysis in Smart Classroom with Multiple Students YOLOv5增强多学生智能课堂学习行为识别与分析

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050042

Zhifeng Wang, Jialong Yao, Chunyan Zeng, Wanxuan Wu, Hongmin Xu, Yang Yang

{"title":"YOLOv5 Enhanced Learning Behavior Recognition and Analysis in Smart Classroom with Multiple Students","authors":"Zhifeng Wang, Jialong Yao, Chunyan Zeng, Wanxuan Wu, Hongmin Xu, Yang Yang","doi":"10.1109/IEIR56323.2022.10050042","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050042","url":null,"abstract":"Deep learning-based computer vision technology has grown stronger in recent years, and cross-fertilization using computer vision technology has been a popular direction in recent years. The use of computer vision technology to identify students’ learning behavior in the classroom can reduce the workload of traditional teachers in supervising students in the classroom, and ensure greater accuracy and comprehensiveness. However, existing student learning behavior detection systems are unable to track and detect multiple targets precisely, and the accuracy of learning behavior recognition is not high enough to meet the existing needs for the accurate recognition of student behavior in the classroom. To solve this problem, we propose a YOLOv5s network structure based on you only look once (YOLO) algorithm to recognize and analyze students’ classroom behavior in this paper. Firstly, the input images taken in the smart classroom are pre-processed. Then, the pre-processed image is fed into the designed YOLOv5 networks to extract deep features through convolutional layers, and the Squeeze-and-Excitation (SE) attention detection mechanism is applied to reduce the weight of background information in the recognition process. Finally, the extracted features are classified by the Feature Pyramid Networks (FPN) and Path Aggregation Network (PAN) structures. Multiple groups of experiments were performed to compare with traditional learning behavior recognition methods to validate the effectiveness of the proposed method. When compared with YOLOv4, the proposed method is able to improve the mAP performance by 11%.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"96 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":"122374497","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: 8

A Dynamic Keyboard with Hierarchical Mathematical Symbols for Multi-Subject e-Learning Systems 面向多学科电子学习系统的分层数学符号动态键盘

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050045

Xuebi Xu, Shishun Wu, Shiwen Gu, Bin He, Xinguo Yu

引用次数: 0

Development of a Virtual Simulation Experiment Platform for Intelligent Substation to Promote the Integration between Industry and Education 开发智能变电站虚拟仿真实验平台，促进产教融合

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050068

Tianran Li, Sheng Huang, Yuxin Ding, Mingxuan Cai

引用次数: 0

Combining Coverage with TMPS for Reviewer Assignment 将覆盖率与TMPS结合起来，用于审阅者分配

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050060

Lu Xu, Daojian Zeng, Jianhua Dai, Lin Gui

引用次数: 0

An Approach to Optimize Lab-Seat Allocation Problem Based on Multi-Agent Negotiation 基于多智能体协商的实验室座位分配优化方法

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050056

Kai Li, Lei Niu, Yang Yang, Yuchen Wang

引用次数: 0