期刊：
International Journal of Educational Research,2022年111:101910 ISSN：0883-0355
通讯作者：
Miaoyun Li
作者机构：
[Wu, Di] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China;[Yang, Xiao; Yang, Wei] Educational Informatization Strategy Research Base of Ministry of Education, Central China Normal University, Wuhan, Hubei, China;[Lu, Chun] Research Center for Science and Technology Promoting Educational Innovation and Development, Strategic Research Base of the Ministry of Education, Wuhan, Hubei, China;[Li, Miaoyun] Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China
通讯机构：
[Miaoyun Li] F;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China
关键词：
A multilevel moderation model;Digital educational resource;ICT training;Rural schools;School training
作者：
Mingyan Zhang;Xu Du;Kerry Rice;Jui-Long Hung;Hao Li
期刊：
Information Discovery and Delivery,2022年50(2):206-216 ISSN：2398-6247
通讯作者：
Hung, J.-L.
作者机构：
[Zhang M.; Li H.; Du X.] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Hung J.-L.; Rice K.] Department of Educational Technology, College of Education, Boise State University, Boise, ID, United States
通讯机构：
[Hung, J.-L.] D;Department of Educational Technology, College of Education, Boise State University, Boise, ID, United States
关键词：
Early warning in online education;Learning pattern analysis;Learning performance prediction;Long short-term memory encoder
作者：
Du, Xu;Zhang, Lizhao;Hung, Jui-Long;Li, Hao;Tang, Hengtao;...
期刊：
INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION,2022年19(1):1-21 ISSN：2365-9440
通讯作者：
Jui-Long Hung
作者机构：
[Li, Hao; Du, Xu; Xie, Yiqian] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Hung, Jui-Long; Zhang, Lizhao] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.;[Tang, Hengtao] Univ South Carolina, Dept Educ Studies, Charleston, SC USA.
通讯机构：
[Jui-Long Hung] N;National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China<&wdkj&>Department of Educational Technology, Boise State University, Boise, USA
摘要：
The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed.
作者机构：
[Zhang, Kun; He, Chaoying; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[He, Chaoying] Natl Changhua Univ Educ, Dept Special Educ, Changhua, Peoples R China.
通讯机构：
[Jingying Chen] N;National Engineering Research Center For E-Learning, Central China Normal University, Wuhan, People’s Republic of China
关键词：
Internal and external cues;visual contact;children with ASD;pupil reflection;joint attention
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
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：
[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.
摘要：
The recommendation systems in the online platforms often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage review information to construct an accurate user/item latent factor. To address this issue, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, and reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods. The proposed method also further promotes the application in learning resource adaptation. The source Python code will be available upon request. (c) 2021 Elsevier B.V. All rights reserved.
作者机构：
[Liu, Leyuan; Huo, Jiao; Jiang, Rubin; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Leyuan; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Jingying Chen] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China<&wdkj&>National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.
