2022 International Conference on Intelligent Education and Intelligent Research (IEIR)最新文献_第4页

Classification of Students’ Attentional States Using Attention Mechanism and BiLSTM Fusion 基于注意机制和BiLSTM融合的学生注意状态分类

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050046

Chen Li, Qing Yang, Ming Li, Dou Wen, Yaqun Wang

引用次数: 0

Student Action Recognition Based on Fuzzy Broad Learning System 基于模糊广义学习系统的学生行为识别

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050086

Yantao Wei, Fen Lei, Jie Gao, Xiuhan Li

{"title":"Student Action Recognition Based on Fuzzy Broad Learning System","authors":"Yantao Wei, Fen Lei, Jie Gao, Xiuhan Li","doi":"10.1109/IEIR56323.2022.10050086","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050086","url":null,"abstract":"Automatic recognition of student action is an important means to evaluate students' learning status in the class. It also provides a technique for measuring the effectiveness of teaching. However, the complexity of student action poses a challenge to automatic recognition. In this paper, a student action recognition method based on the fuzzy broad learning system (fuzzy BLS) is proposed. Fuzzy BLS is designed by merging the Takagi-Sugeno (TS) fuzzy system into BLS. As a neuro-fuzzy model, fuzzy BLS overcomes some problems, such as suffering from a time-consuming training stage and a large number of fuzzy rules. To get more abundant local features from student action images, we use the Scale-Invariant Feature Transform (SIFT) descriptor combined with the Local LogEuclidean Multivariate Gaussian $(mathrm{L}^{2}mathrm{E}mathrm{M}mathrm{G})$ descriptor to extract image features. Then, the extracted features are fed into fuzzy BLS after dimension reduction. The experimental results on the self-built dataset have shown that the proposed student action recognition method achieves better performance than other benchmarking methods.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"98 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":"128592486","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: 1

The Effects of Contextualized Learning Content and Collaborative Behaviours in a Ubiquitous Learning Environment 泛在学习环境中情境化学习内容与协作行为的影响

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050066

Min Chen, Chi Zhou

{"title":"The Effects of Contextualized Learning Content and Collaborative Behaviours in a Ubiquitous Learning Environment","authors":"Min Chen, Chi Zhou","doi":"10.1109/IEIR56323.2022.10050066","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050066","url":null,"abstract":"Research suggests the importance of providing learners with contextualized learning content that meets the demands of learning context in the ubiquitous learning (ulearning) environment. Similarly, the positive role of collaborative learning is recognized. However, it is not clear how collaboration may benefit learners if they are provided with contextualized learning content that meets their individual needs in u-learning activities. To bridge the gap, this study explored the cross effect of contextualized learning content and collaborative behaviours on students’ learning effect in a u-learning environment. Thirty-four first-year students at a vocational college in China participated in a sixweek comparison experiment and were interviewed in focus groups. The study found that, regardless of whether contextualized learning content was provided, learners tended to collaborate by cutting the task apart in the ubiquitous environment; contextualized learning content had a positive impact on learners’ learning effect; collaboration by cutting the task apart did not benefit the learning effect; the cross effect of this collaboration and contextualized learning content on the learning effect was not significant. Implications for promoting effective u-learning in terms of learning content and collaboration are proposed.","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":"124787635","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

Intelligent Multimodal Analysis Framework for Teacher-Student Interaction 师生互动智能多模态分析框架

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050044

Mengke Wang, Liang Luo, Zengzhao Chen, Qiuyu Zheng, Jiawen Li, Wei Gao

{"title":"Intelligent Multimodal Analysis Framework for Teacher-Student Interaction","authors":"Mengke Wang, Liang Luo, Zengzhao Chen, Qiuyu Zheng, Jiawen Li, Wei Gao","doi":"10.1109/IEIR56323.2022.10050044","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050044","url":null,"abstract":"This paper constructed a multi-modal analysis framework of teacher-student interaction based on intelligent technology. Voiceprint recognition was used to divide the teaching video into slices according to sentences and then used speech recognition, speech emotion analysis, gaze point estimation, and other technologies to recognize and encoded the multimodal behavior of each slice. We analyzed 10 lessons using the event sampling method proposed in the analysis framework in comparison with the classical temporal sampling analysis method and demonstrated the results of multimodal interaction analysis of an instructional video as an example. The results indicated that the event sampling method proposed not only reduces the number of analysis units but also has more complete information about the utterance of each unit, overcoming the incomplete information or information redundancy of analysis units caused by the mechanical segmentation of temporal sampling. The multimodal analysis showed that taking into account both teacher-student verbal and nonverbal interactions can reveal richer and deeper information about classroom teaching and learning. This framework provides an important reference for intelligent multimodal analysis of teacher-student interaction.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"60 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":"126585542","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

Comparison of Three Learner Profiles under the Influence of the Double Reduction Policy — Evidence from the K-means Clustering Approach 双约策略下三种学习者特征的比较——来自k均值聚类方法的证据

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050061

Qinglin Huang, Zhili Zhang, Siyi Jiang, X. Liao, Heng Luo

{"title":"Comparison of Three Learner Profiles under the Influence of the Double Reduction Policy — Evidence from the K-means Clustering Approach","authors":"Qinglin Huang, Zhili Zhang, Siyi Jiang, X. Liao, Heng Luo","doi":"10.1109/IEIR56323.2022.10050061","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050061","url":null,"abstract":"The “double reduction” policy is a national educational policy issued by the Chinese government in 2021, aiming to reduce the amount of homework and study time of K-12 students. In this study, we collected various data on students’ demographic characteristics, learning patterns, and learning perceptions under the “double reduction” policy using a self-developed questionnaire, and obtained their standardized semester-end test results as measurement of learning outcomes. A total of 8100 5th graders from 45 primary schools in a school district in Wuhan participated in this study. Based on the K-means clustering results, we classified the students into three profile categories: Challenged Learners, Policy Followers, and Competitive Learners and further compared the three learner profiles to identify differences in learning load, learning motivation, and learning outcomes. The study results inform individualized education to accommodate profile differences and inform the sustainable implementation and refinement of the “double reduction” policy.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"32 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":"115906314","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

Solving Word Function Problems in Line with Educational Cognition Way 根据教育认知方式解决词功能问题

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050055

Bin Wang, Xinguo Yu, Huihui Sun

引用次数: 0

Automatic Recognition of Speech Acts in Classroom Interaction Based on Multi-Text Classification 基于多文本分类的课堂互动语音行为自动识别

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050047

Miao Xia, Wei Deng, Sixv Zhang, Meijuan Liu, JiaLi Xu, Peiyun Zhai

引用次数: 0

I-portrait: A Multidimensional Student Portrait System for Learning Situation Analysis I-portrait:用于学习情境分析的多维学生肖像系统

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050052

Xinyan Zhang, Yuqi Chen, Junjie Hu, Shengze Hu, Tao Huang

引用次数: 0

Linear Function Relation Identification Based on BERT and Bi-LSTM 基于BERT和Bi-LSTM的线性函数关系识别

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050065

Chensi Li, Xinguo Yu, Rao Peng

引用次数: 0

A Graph Convolutional Network Feature Learning Framework for Interpretable Geometry Problem Solving 用于可解释几何问题求解的图卷积网络特征学习框架

2022 International Conference on Intelligent Education and Intelligent Research (IEIR) Pub Date : 2022-12-18 DOI: 10.1109/IEIR56323.2022.10050084

Fucheng Guo, Pengpeng Jian

引用次数: 0