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.
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
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
通讯机构：
[Yi Ding] F;Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Existing methods to eliminate the laser speckle noise in quantitative phase imaging always suffer from the loss of detailed phase information and the resolution reduction in the reproduced image. To overcome these problems, this paper proposes a speckle noise suppression method based on empirical mode decomposition. Our proposed method requires only one image without additional equipment and avoids the complicated process of searching the optimal processing parameters. In this method, we use empirical mode decomposition to highlight the high frequency information of the interference image and use the Canny operator to perform edge detection, so the diffusion denoising process is guided by high-precision detection results to achieve better results. To validate the performance of our proposed method, the phase maps processed by our proposed method are compared with the phase maps processed by the improved anisotropic diffusion equation method with edge detection, the mean filter method and the median filter method. The experimental results show that the method proposed in this paper not only has a better denoising effect but also preserves more details and achieves higher phase reconstruction accuracy.
期刊：
Computer Animation and Virtual Worlds,2022年33(1):e2032- ISSN：1546-4261
通讯作者：
Zhang, Junsong(jszhang@outlook.com)
作者机构：
[Liu, Xiaoyu; Liu, Kunxiang; Zhang, Junsong; Zhu, Shaoqiang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Zhang, Junsong] Xiamen Univ, Dept Artificial Intelligence, Mind Art & Computat Grp, Xiamen 361005, Fujian, Peoples R China.
通讯机构：
[Junsong Zhang; Junsong Zhang Junsong Zhang Junsong Zhang] N;National Engineering Research Center for E-learning, Central China Normal University, Wuhan, China<&wdkj&>Mind, Art and Computation Group, Department of Artificial Intelligence, Xiamen University, Xiamen, Fujian 361005, P.R, China
期刊：
Journal of King Saud University - Computer and Information Sciences,2022年34(10):8972-8984 ISSN：1319-1578
通讯作者：
Yu, XG
作者机构：
[Sun, Huihui; Yu, Xinguo; Sun, Chao; Yu, XG] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 43007, Peoples R China.
通讯机构：
[Yu, XG ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 43007, Peoples R China.
关键词：
Relation-centric algorithm;Symbolic solver;Syntax semantics model with period;Diagram understanding
摘要：
A tutorable algorithm for solving text-diagram function (TDF) problems is an essential technology for building the humanoid tutorial service since "function" is a core portion of mathematics. Solving TDF problems encounters challenges in understanding diagrams, representing the functions of being compound objects, and solving the mixture group of functions and universal relations. To address these challenges, this paper proposes a relation-centric algorithm, leveraging on making breakthrough in handling function, understanding diagram, and generating tutorable solution. The proposed algorithm comprises two phases: problem understanding and symbolic solver. In the problem understanding, it proposes a (SP)-P-2 (Syntax Semantics with Period) model method of acquiring relations from text and a L-2 (Line Segment with Labels Pattern) model method of acquiring relations from diagrams. To get the problem fully understood, this phase acquires both universal relations and period relations. In the symbolic solver, the function is first built from the acquired relations. Then an equation-function interaction method is created to solve a mixture system of relations and functions. The developed algorithm is the first one for solving text-diagram function problems. Experimental results show that the proposed algorithm not only has high accuracies of 74.5% on Math23KtoF and 80.8% on TnD1K datasets, but also can produce the tutorable solutions of TDF problems.
期刊：
DATA TECHNOLOGIES AND APPLICATIONS,2022年56(2):303-326 ISSN：2514-9288
通讯作者：
Yang, Juan
作者机构：
[Yang, Juan] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Yang, Juan; Du, Xu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.;[Tu, Chih-Hsiung] No Arizona Univ, Dept Educ Specialties, Flagstaff, AZ 86011 USA.
期刊：
Information Sciences,2022年596:567-587 ISSN：0020-0255
通讯作者：
Shen, Xiaoxuan;Sun, JW
作者机构：
[Shen, Xiaoxuan; Liu, Sannyuya; Li, Qing; Shen, XX; Sun, Jianwen; Liang, Ruxia; Zhang, Yunhan] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Yu, Jianwei] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Shen, Xiaoxuan; Liu, Sannyuya; Li, Qing; Shen, XX; Sun, Jianwen; Liang, Ruxia; Zhang, Yunhan; Yu, Jianwei] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
通讯机构：
[Shen, XX; Sun, JW ] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
摘要：
Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services, which estimates and traces the degree of learner's mastery of concepts based on students' responses to learning resources. The existing mainstream KT models, only attribute learners' feedback to the degree of knowledge mastery and leave the influence of mental ability factors out of consideration. Although ability is an essential component of the problem-solving process, these knowledge-centered models cause a contradiction between data fitting and rationalization of the model decision-making process, making it difficult to achieve high precision and readability simultaneously.In this paper, an innovative KT model, ability boosted knowledge tracing (ABKT)(1) is pro-posed, which introduces the ability factor into learning feedback attribution to enable the model to analyze the learning process from two perspectives, knowledge and ability, simul-taneously. Based on constructive learning theory, continuous matrix factorization (CMF) model is proposed to simulate the knowledge internalization process, following the initiative growth and stationarity principles. In addition, the linear graph latent ability (LGLA) model is proposed to construct learner and item latent ability features, from graph-structured learner interaction data. Then, the knowledge and ability dual-tracing framework is constructed to integrate the knowledge and ability modules. Experimental results on four public databases indicate that the proposed methods perform better than state-of-the-art knowledge tracing algorithms in terms of prediction accuracy in quantitative assessments, displaying some advantages in model interpretability and intelligibility.(c) 2022 Elsevier Inc. All rights reserved.
摘要：
Urban greenspace (UGS) plays an essential role in providing benefits to human well-being in cities. Under-standing how to promote positive emotions is vital for planning and designing a UGS supply that satisfies human demand. However, little is known about the impact on human emotions of the interaction of cross-cultural demands and different greenspace supplies. This study explored different human emotions about UGS from a cross-linguistics perspective using social media data (SMD). Sentiment analysis was conducted with geolocated Twitter data from 26 UGSs in Berlin to acquire sentiment value and tweet number of tweets. The sentiments of English and German tweets in four types of UGSs were compared, and the correlations with 11 physical and activity landscape characteristics were identified. The results demonstrate that (1) sentiment value and tweet number of tweets were distinctive in the 26 greenspaces, showing different emotions responding to the different types of UGS; (2) the cross-linguistic demands were different in the comparison of English and German tweets, with the highest sentiment values in gardens and parks, respectively; and (3) the sentiment of the all, English and German tweets was respectively correlated with open space, interesting plants and swimming infrastructure. The activity landscape made the highest contributions to positive emotions even with cross-cultural differences. The results of the study suggest that human emotions can indicate whether the UGS supply meets the human demand and that specific landscape characteristics can enhance positive emotions to maintain human demand in cross-cultural background, especially considering the increasing attention to immigrants and natives. Thus, human emotions identify the interaction between UGS supply and human demand based on SMD to improve UGS outcomes for urban sustainability and public well-being.
摘要：
Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by <inline-formula> <tex-math notation="LaTeX">$1\%$</tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$6\%$</tex-math> </inline-formula> and the improvement of signal-to-noise ratio (SNR) up to <inline-formula> <tex-math notation="LaTeX">$1.2$</tex-math> </inline-formula> dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR. IEEE
作者机构：
[Xiong, Neal N.; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Zheng, Chao; Shen, Xiaoxuan] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Li, Duantengchuan] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Lin, Ke] Harbin Inst Technol Shenzhen, Dept Control Sci & Engn, Shenzhen, Peoples R China.;[Wang, Jiazhang] Northwestern Univ, Evanston, IL USA.
通讯机构：
[Zheng, Chao] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.
关键词：
Diverse social relation;Graph convolutional network;Recommender system;Representation learning
摘要：
Social recommender systems (SRS) aim to study how social relations influence users' choices and how to use them for better learning users embeddings. However, the diversity of social relationships, which is instructive to the propagation of social influence, has been rarely explored. In this paper, we propose a graph convolutional network based representation learning method, namely multi-perspective social recommendation (MPSR), to construct hierarchical user preferences and assign friends' influences with different levels of trust from varying perspectives. We further utilize the attributes of items to partition and excavate users' explicit preferences and employ complementary perspective modeling to learn implicit preferences of users. To measure the trust degree of friends from different perspectives, the statistical information of users' historical behavior is utilized to construct multi-perspective social networks. Experimental results on two public datasets of Yelp and Ciao demonstrate that the MPSR significantly outperforms the state-of-the-art methods. Further detailed analysis verifies the importance of mining explicit characteristics of users and the necessity for diverse social relationships, which show the rationality and effectiveness of the proposed model. The source Python code will be available upon request. (c) 2021 Elsevier B.V. All rights reserved.
作者机构：
[Xu, Bin; Ding, Yi; Hu, Yi; Liu, Xinyue; Du, Zihao; Zhan, Xiaojiang] Wuyi Univ, Fac Intelligent Mfg, Jiangmen 529020, Peoples R China.;[Liao, Shengbin] Huazhong Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Xi, Jiangtao] Univ Wollongong, Sch Elect Comp & Telecommun Engn, Wollongong 2522, Australia.
通讯机构：
[Yi Ding] F;Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Imaging the interaction between the laser pulse and photoresist mixture on the ultrafast time scale can track the path of the light pulse and reveal the procedure of the microstructure machining. However, most existing imaging technologies suffer from problems such as requiring multiple repeated shots or a limited time resolution. To overcome these problems, we propose to capture the motion of laser pulses in a photoresist mixture by using compressed ultrafast photography (CUP). In this method, we can recover the motion process of non-repeatable events with a single shot at the time-resolution of about 1.54x10(11 )fps, where the depth of the imaging sequence reaches hundreds of frames. To verify the effectiveness of the proposed method, we estimate the speed of the laser pulse in a photoresist mixture and evaluate the similarity between the image captured by a streak camera and our reconstructed ultrafast sequence; the results validate the reliability of our proposed method.
摘要：
Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach adopts a strategy of breaking up the whole into parts to decrease the complexity of merging multiple taxonomies and employs a block-based method to reduce the scale of measuring semantic relations between concept pairs. In addition, for the problem of multiple inheritance, a method of topical coverage is proposed. Experiments conducted on synthetic and real-world scenarios indicate that the proposed merging approach is feasible and effective to merge multiple taxonomies. In particular, the proposed approach works well in the aspects of limiting the semantic redundancy and establishing high-quality hierarchical relations between concepts.
作者机构：
[Zhao, Liang; Zhu, Xiaoliang] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[He, Zili; Dai, Zhicheng; Yang, Qiaolai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Dai, ZC ; Zhao, L ] 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：
The performance of a facial expression recognition network degrades obviously under situations of uneven illumination or partial occluded face as it is quite difficult to pinpoint the attention hotspots on the dynamically changing regions (e.g., eyes, nose, and mouth) as precisely as possible. To address the above issue, by a hybrid of the attention mechanism and pyramid feature, this paper proposes a cascade attention-based facial expression recognition network on the basis of a combination of (i) local spatial feature, (ii) multi-scale-stereoscopic spatial context feature (extracted from the 3-scale pyramid feature), and (iii) temporal feature. Experiments on the CK+, Oulu-CASIA, and RAF-DB datasets obtained recognition accuracy rates of 99.23%, 89.29%, and 86.80%, respectively. It demonstrates that the proposed method outperforms the state-of-the-art methods in both the experimental and natural environment.
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi; Liu, Shiqi; Peng, Xian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi; Peng, Xian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Zongkai Yang] N;National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People's Republic of China<&wdkj&>National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People's Republic of China
关键词：
21st century abilities;Cooperative/collaborative learning;Data science applications in education;Distance education and online learning;Evaluation methodologies
