期刊：
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT,2022年70(3):849-880 ISSN：1042-1629
通讯作者：
Hung, C.-Y.;Zhang, Y.
作者机构：
[Wang, Jue] Huzhou Univ, Sch Teacher Educ, Huzhou, Zhejiang, Peoples R China.;[Zhang, Yi; Hung, Cheng-Yu] Cent China Normal Univ, Fac Artificial Inteligence Educ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.;[Wang, Qiyun] Nanyang Technol Univ, Natl Inst Educ, Singapore, Singapore.;[Zheng, Ying] Yichun Univ, Sch Foreign Languages, Yichun, Jiangxi, Peoples R China.
通讯机构：
[Yi Zhang; Cheng-Yu Hung] S;School of Educational Information Technology, Faculty of Artificial Inteligence in Education, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>School of Educational Information Technology, Faculty of Artificial Inteligence in Education, Central China Normal University, Wuhan, People’s Republic of China
关键词：
Non-programming;Plugged learning;Mathematics;Computational thinking (CT);Design-based implementation research (DBIR);Problem-solving
作者：
Li, Xiuhan*;Yang, Yuqin*;Chu, Samuel Kai Wah;Zainuddin, Zamzami;Zhang, Yin
期刊：
Asia Pacific Journal of Education,2022年42(2):211-227 ISSN：0218-8791
通讯作者：
Li, Xiuhan;Yang, Yuqin
作者机构：
[Zainuddin, Zamzami; Li, Xiuhan; Chu, Samuel Kai Wah] Univ Hong Kong, Fac Educ, Pokfulam Rd, Hong Kong 999077, Peoples R China.;[Yang, Yuqin] Cent China Normal Univ, Sch Educ Informat Technol, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Zhang, Yin] Ocean Univ China, Dept Educ, Qingdao, Peoples R China.
通讯机构：
[Li, Xiuhan] U;[Yang, Yuqin] C;Univ Hong Kong, Fac Educ, Pokfulam Rd, Hong Kong 999077, Peoples R China.;Cent China Normal Univ, Sch Educ Informat Technol, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
关键词：
Blended synchronous learning;flexible learning;higher education;online learning;active research
期刊：
Journal of Educational Computing Research,2021年59(7):1319-1342 ISSN：0735-6331
通讯作者：
Wu, Linjing;Liu, Qingtang
作者机构：
[Wu, Linjing; Liu, Qingtang; Li, Jing; Yang, Weiqing; He, Liming; Zhang, Yaosheng] Cent China Normal Univ, Sch Educ Informat Technol, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Liu, Qingtang] Cent China Normal Univ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.;[Cheng, Yun] Huang Gang Normal Univ, Sch Educ, Huanggang, Hubei, Peoples R China.
通讯机构：
[Wu, LJ; Liu, QT] C;Cent China Normal Univ, Sch Educ Informat Technol, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
collaborative knowledge building;knowledge contribution;information theory;amount of information;information gain
摘要：
The measurement of knowledge contribution in collaborative knowledge building is an important research topic in computer-supported collaborative learning. The information measures of knowledge contribution based on information theory are proposed in this study, which includes two measures: amount of information and information gain. Discourse data collected from a collaborative knowledge building activity were analyzed to validate these measures. The results showed that our information measures can complement the traditional behavioral. With the help of the two measures, community-level variation tendency and individual-level knowledge contribution characteristics could be analyzed in collaborative knowledge building activities. A log function was used to fit the community knowledge variation tendency to measure the convergence of knowledge building. Students were clustered into five types according to their behaviors and contributions in collaborative knowledge building. Both teachers and researchers can benefit from these two information measures by using them in practice.
期刊：
International Journal of Distributed Sensor Networks,2020年16(2):155014772090783 ISSN：1550-1477
通讯作者：
Wei, Yantao
作者机构：
[Wei, Yantao] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;[Wei, Yantao] Cent China Normal Univ, Hubei Res Ctr Educ Informationizat, Wuhan 430079, Peoples R China.
通讯机构：
[Wei, Yantao] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei Res Ctr Educ Informationizat, Wuhan 430079, Peoples R China.
关键词：
Human activity recognition;broad learning system;multi-level fused features;principal component analysis
摘要：
Human activity recognition using depth videos remains a challenging problem while in some applications the available training samples is limited. In this article, we propose a new method for human activity recognition by crafting an integrated descriptor called multi-level fused features for depth sequences and devising a fast broad learning system based on matrix decomposition for classification. First, the surface normals are computed from original depth maps; the histogram of the surface normal orientations is obtained as a low-level feature by accumulating the contributions from normals, then a high-level feature is acquired by sparse coding and pooling on the aggregation of polynormals. After that, the principal component analysis is applied to the conjunction of the two-level features in order to obtain a low-dimensional and discriminative fused feature. At last, fast broad learning system based on matrix decomposition is proposed to accelerate the training process and enhance the classification results. The recognition results on three benchmark data sets show that our method outperforms the state-of-the-art methods in term of accuracy, especially when the number of training samples is small.
期刊：
Mathematical Problems in Engineering,2020年2020:1-16 ISSN：1024-123X
通讯作者：
Wei, Yantao
作者机构：
[Yao, Huang; Wei, Yantao; Tian, Yuan; Zhang, Yu] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;[Wei, Yantao] Cent China Normal Univ, Educ Informatizat Res Ctr Hubei, Wuhan 430079, Peoples R China.
通讯机构：
[Wei, Yantao] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Educ Informatizat Res Ctr Hubei, Wuhan 430079, Peoples R China.
关键词：
Discriminant analysis;Image analysis;Image classification;Multilayer neural networks;Network layers;Spectroscopy;Classification accuracy;Computing demands;High dimensionality;Locality sensitive discriminant analysis;Manifold learning;Output layer;Stacking layers;State of the art;Learning systems
