作者：
Guo Su;Taotao Long;Zhiyan Wang;Qingcui Zeng;Xueping Wang
期刊：
International Journal of Innovation and Learning,2022年31(3):348-368https://doi.org/10.1504/IJIL.2022.122065 ISSN：1471-8197
通讯作者：
Long, T.
作者机构：
1. School of Educational Information Technology, Central China Normal University, 152 Luoyu Rd., Wuhan, Hubei, China;2. School of Educational Information Technology, Central China Normal University, 152 Luoyu Rd., Wuhan, Hubei, China;3. School of Educational Information Technology, Central China Normal University, 152 Luoyu Rd., Wuhan, Hubei, China;4. School of Educational Information Technology, Central China Normal University, 152 Luoyu Rd., Wuhan, Hubei, China;5. School of Educational Information Technology, Central China Normal University, 152 Luoyu Rd., Wuhan, Hubei, China
通讯机构：
School of Educational Information Technology, Central China Normal University, 152 Luoyu Rd., Hubei, Wuhan, China
摘要：
Cognition and emotion in online collaboration play an important role in achieving high-quality collaboration performance. Especially in the online synchronous collaborative environment, the limited time and place require higher language instantaneity and flexibility of thinking, which brings great challenges to students' high cognitive behavior and emotional experience. However, few empirical studies have focused on the cognition and emotion of students in online synchronous collaboration. Therefore, a quasi-experimental study was conducted based on a creative synchronous collaboration task to explore the effects, social knowledge construction and emotional experience of Multi-Input and One-Output(MIOO) collaboration and Multi-Input and Multi-Output(MIMO) collaboration. The findings indicated that the MIMO collaboration groups performed better than the MIOO collaboration groups in creative achievements, but were more affected by technology. Besides, the MIOO collaboration groups had a higher degree of social knowledge construction and more positive emotional experiences.
摘要：
Despite the continuous emphasis on emotion in multimedia learning, it was still unclear how pedagogical agent emotional cues might affect learning. In the present study, a between-subjects experiment was performed to examine the effects of a pedagogical agent with dual-channel emotional cues on learners' emotions, cognitive load, and knowledge transfer performance. Participants from a central Chinese university (age mean = 21.26, N = 66) were randomly divided into three groups. These groups received instructions from an affective pedagogical agent, a neutral pedagogical agent, or a neutral voice narration without pedagogical agent embodiment. Results showed that learners assigned the affective pedagogical agent reported a significantly higher emotional level than learners assigned the neutral pedagogical agent. Learners' perceived task difficulty was not significantly different among groups while instructional efficiency was significantly higher for learners with the affective pedagogical agent. Moreover, learners assigned to the affective pedagogical agent performed significantly better on the knowledge transfer test than those assigned the neutral pedagogical agent or the neutral voice.
作者机构：
[Hu Z.] School of Educational Information Technology, Central China Normal University, Wuhan, China;[Su J.] School of Computer Science, Hubei University of Technology, Wuhan, China;[Koroliuk Y.] Chernivtsi Institute of Trade and Economics, Kyiv National University of Trade and Economics, Chernivtsi, Ukraine
会议名称：
3rd International Conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020
会议时间：
21 January 2020 through 22 January 2020
关键词：
Collaborative learning;Interactive learning environments;Predicting of academic performance;Simulations;Teaching strategies
摘要：
In the electronic age, the changes in the mirror space constructed by movies are also brought about by changes in the media. Cinematic space is a four-dimensional space illusion including time created by the use of light and shadow, color, perspective, sound, and the movement of characters and cameras. It is not a real four-dimensional space, but a reflection and reproduction of the real space. With the goal of improving the accuracy of spatially coded structured light 3D reconstruction, this paper conducts in-depth research on several key technologies that affect the reconstruction accuracy and makes corresponding innovations and improvements. Moreover, this paper respectively proposes an adaptive structured light spatial coding algorithm based on geometric features and a color structured light decoding algorithm based on color shift technology. In addition, this paper implements a spatially coded structured light three-dimensional reconstruction system and calibration system. The simulation research shows that the method in this paper has certain reliability.
作者机构：
[Xu B.; Zong J.F.] School of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China;[Xu Z.] School of Educational Information Technology, Central China Normal University, Wuhan, 430072, China;[Shu C.; Xiao J.] School of Electronic Information, Wuhan University, Wuhan, 430072, China;[Ding L.] School of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China, School of Electronic Information, Wuhan University, Wuhan, 430072, China
会议名称：
1st International Symposium on Geometry and Vision, ISGV 2021
会议时间：
28 January 2021 through 29 January 2021
关键词：
Convolutional neural network;Detection;Dotted line;The lane line;The solid line
作者机构：
[Liu, Qingtang; Wu, Linjing] School of Educational Information Technology, Central China Normal University, China;Information Systems and Technology, The University of Dodoma, Tanzania, United Republic of;[Swai, Carina Titus] School of Educational Information Technology, Central China Normal University, China<&wdkj&>Information Systems and Technology, The University of Dodoma, Tanzania, United Republic of
会议名称：
13th International Conference on Education Technology and Computers, ICETC 2021
摘要：
Object co-segmentation is a challenging task, which aims to segment common objects in multiple images at the same time. Generally, common information of the same object needs to be found to solve this problem. For various scenarios, common objects in different images only have the same semantic information. In this paper, we propose a deep object co-segmentation method based on channel and spatial attention, which combines the attention mechanism with a deep neural network to enhance the common semantic information. Siamese encoder and decoder structure are used for this task. Firstly, the encoder network is employed to extract low-level and high-level features of image pairs. Secondly, we introduce an improved attention mechanism in the channel and spatial domain to enhance the multi-level semantic features of common objects. Then, the decoder module accepts the enhanced feature maps and generates the masks of both images. Finally, we evaluate our approach on the commonly used datasets for the co-segmentation task. And the experimental results show that our approach achieves competitive performance. (C) 2020 Elsevier B.V. All rights reserved.
