期刊：
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2022年26(6):2435-2446 ISSN：2168-2194
通讯作者：
Xiong, N.
作者机构：
[Xiong, N.] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China;Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China;[Sun, Le; Zhong, Zhaoyi; Qu, Zhiguo] Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China<&wdkj&>Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China
通讯机构：
[Xiong, N.] C;Central China Normal University, National Engineering Research Center for E-Learning, Wuhan, China
期刊：
Expert Systems with Applications,2022年207:117680 ISSN：0957-4174
通讯作者：
Liu, Sannyuya(lsy5918@mail.ccnu.edu.cn)
作者机构：
[Liu, Sannyuya; Zou, Rui; Li, Qing; Liang, Ruxia; Sun, Jianwen] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Gao, Lu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Zou, Rui; Gao, Lu; Li, Qing; Liang, Ruxia; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Zhang, Kai] Yangtze Univ, Sch Comp Sci, Jingzhou 434025, Peoples R China.;[Jiang, Lulu] Nanhai Expt Sch, Foshan 528299, Peoples R China.
通讯机构：
[Sannyuya Liu; Qing Li] N;National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China
关键词：
Deep neural network;Knowledge Tracing;Learning interactions;User modeling
期刊：
IEEE Transactions on Industrial Informatics,2022年18(1):16-25 ISSN：1551-3203
通讯作者：
Zhang, Kun
作者机构：
[Yang, Zongkai; Guo, Chen; Zhang, Kun; Xu, Ruyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Yang, Zongkai; Guo, Chen; Zhang, Kun; Xu, Ruyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Honghai] Harbin Inst Technol Shenzhen, State Key Lab Robot & Syst, Shenzhen 518055, Peoples R China.;[Liu, Honghai] Univ Portsmouth, Portsmouth PO1 2UP, Hants, England.
通讯机构：
[Zhang, Kun] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Empathy ability is one of the most important social communication skills in early childhood development. To analyze the children's empathy ability, facial expression analysis (FEA) is an effective way due to its ability to understand children's emotional states. Previous works mainly focus on recognizing the facial expression categories yet fail to estimate expression intensity, the latter of which is more important for fine-grained emotion analysis. To this end, this article first proposes to analyze children's empathy ability with both the categories and the intensities of facial expressions. A novel FEA method based on intensity label distribution learning is presented, which aims to recognize expression categories and estimate their intensity levels in an end-to-end framework. First, the intensity label distribution is generated for each frame in the expression sequence using a linear interpolation estimation and a Gaussian function to address the lack of reasonable annotations for expression intensity. Then, the extended intensity label distribution is presented to automatically encode the expression intensity in a multidimensional expression space, which aims to integrate the expression recognition and intensity estimation into a unified framework as well as boost the expression recognition performance by suppressing the variations in appearance caused by intensity and by emphasizing those variations among weak expressions. Finally, a Siamese-like convolutional neural network is presented to learn the expression model from a pair of frames that includes an expressive frame and its corresponding neutral frame using the extended intensity label distribution as the supervised information, thus effectively eliminating the expression-unrelated information's influence on FEA. Numerous experiments validate that the proposed method is promising in analysis of the differences in empathy ability between typically developing children and children with autism spectrum disorder.
期刊：
JOURNAL OF BALTIC SCIENCE EDUCATION,2022年21(1):156-170 ISSN：1648-3898
通讯作者：
Lu, C.
作者机构：
[Xing, Danxia] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Lu, Chun] Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
Educational Informatization Strategy Research Base Ministry of Education, Central China Normal University, Hubei, Wuhan, China
关键词：
computational thinking skills;Internet attitude;Internet self-efficacy;Internet use;smart classroom;secondary school students
作者机构：
[Yuan, Yishuang; Chen, Jingying; Zhang, Kun; Luo, Meijuan; Chen, Qian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Yuan, Yishuang; Chen, Jingying; Zhang, Kun; Luo, Meijuan; Chen, Qian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
通讯机构：
[Chen, JY ] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
摘要：
Facial expression processing mainly depends on whether the facial features related to expressions can be fully acquired, and whether the appropriate processing strategies can be adopted according to different conditions. Children with autism spectrum disorder (ASD) have difficulty accurately recognizing facial expressions and responding appropriately, which is regarded as an important cause of their social disorders. This study used eye tracking technology to explore the internal processing mechanism of facial expressions in children with ASD under the influence of spatial frequency and inversion effects for improving their social disorders. The facial expression recognition rate and eye tracking characteristics of children with ASD and typical developing (TD) children on the facial area of interest were recorded and analyzed. The multi-factor mixed experiment results showed that the facial expression recognition rate of children with ASD under various conditions was significantly lower than that of TD children. TD children had more visual attention to the eyes area. However, children with ASD preferred the features of the mouth area, and lacked visual attention and processing of the eyes area. When the face was inverted, TD children had the inversion effect under all three spatial frequency conditions, which was manifested as a significant decrease in expression recognition rate. However, children with ASD only had the inversion effect under the LSF condition, indicating that they mainly used a featural processing method and had the capacity of configural processing under the LSF condition. The eye tracking results showed that when the face was inverted or facial feature information was weakened, both children with ASD and TD children would adjust their facial expression processing strategies accordingly, to increase the visual attention and information processing of their preferred areas. The fixation counts and fixation duration of TD children on the eyes area increased significantly, while the fixation duration of children with ASD on the mouth area increased significantly. The results of this study provided theoretical and practical support for facial expression intervention in children with ASD.
作者机构：
[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Wuhan, Hubei, Peoples R China.;[Wang, Guangshuai; Zhang, Kun; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Tang, Suyun] Guangzhou Univ, Sch Educ, Guangzhou, Guangdong, Peoples R China.;[Wang, Guanghai] Shanghai Jiao Tong Univ, Shanghai Childrens Med Ctr, Dept Dev & Behav Pediat, Pediat Translat Med Inst,Sch Med, 1678 Dongfang Rd, Shanghai 200127, Peoples R China.
通讯机构：
[Jingying Chen] N;[Guanghai Wang] P;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>Pediatric Translational Medicine Institution, Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
作者：
Geng, Jing;Zhu, Junya;Yang, Huali;Hu, Shengze;Huang, Tao
作者机构：
[Geng, Jing; Hu, Shengze; Huang, Tao] National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan;430079, China;[Zhu, Junya] Central China Normal University, Wuhan;[Yang, Huali] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan;[Geng, Jing; Zhu, Junya; Yang, Huali; Hu, Shengze; Huang, Tao] 430079, China
会议名称：
7th International Conference on Distance Education and Learning, ICDEL 2022
作者机构：
[Du, Xu; Zhang, Mingyan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China;[Shelton, Brett E.; Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA
通讯机构：
[Shelton, Brett E.] B;Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.
摘要：
The study proposes two new measures, time and location entropy, to depict students' physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been touted as one of the most attractive features of online learning, the question remains as to the success of students when engaging in online courses through disparate locations and points-in-time. The procedures describe an analysis of 5293 students' spatio-temporal patterns using metadata relating to place and time of access. Grouping into segments that describe their patterns of engagement, results indicate that the high location-high time entropy (i.e. multiple times, multiple locations) was the segment with lowest success when compared with other students. Statistical and modeling results also found that female students tended to learn at fixed or few locations resulting in the highest performance scores on the final exam. The primary implication is that female students tend to be successful because they study in fewer locations, and all students who study at consistent times outperform those with more varied time patterns. Existing brain research supports the findings on gender differences in learning performance and spatio-temporal characteristics.
摘要：
Head pose estimation (HPE) has many wide industrial applications such as human-computer interaction, online education and automatic manufacturing. This study addresses two key problems in HPE based on the deep learning and attention mechanism. 1) How to bridge the gap between the better prediction performance of networks and incorrect labeled pose images in the HPE datasets 2) How to take full advantages of the adjacent poses information around the centered pose image To tackle the first problem, we reconstruct all the incorrected labels as a two-dimensional Lorentz distribution. Instead of directly adopting the angle values as hard labels, we assign part of the probability values (soft labels) to adjacent labels for learning the discriminative feature representations. To address the second problem, we reveal the asymmetric relation nature of HPE datasets, namely, the yaw direction and pitch direction are assigned different weights by introducing the ratio of half with at half-maximum of Lorentz distribution. Compared to traditional end-to-end frameworks, the proposed one can leverage the asymmetric relation cues for predicting the head poses angle in the incorrected label scenarios. Extensive experiments on two public datasets and our infrared dataset demonstrate that the proposed ARHPE network significantly outperforms other state-of-the-art approaches. IEEE
作者机构：
[Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.;[Gao, Yijing; Nagarajan, Srikantan S.; Cai, Chang; Hinkley, Leighton] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.;[Hashemi, Ali; Haufe, Stefan] Charite Univ Med Berlin, Berlin Ctr Adv Neuroimaging, Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Elect Engn & Comp Sci Fac, Machine Learning Grp, Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Inst Math, Berlin, Germany.
通讯机构：
[Cai, C.] N;[Nagarajan, S.S.] D;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China<&wdkj&>Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States
关键词：
Dynamics of neural activity;Brain source power changes;Five-dimensional neuroimaging;Electromagnetic brain imaging;Bayesian inference
