scholarly journals Spontaneous facial expression database of learners’ academic emotions in online learning with hand occlusion

2022 ◽  
Vol 97 ◽  
pp. 107667
Author(s):  
Li Lyu ◽  
Ya Zhang ◽  
Meng-Ya Chi ◽  
Fei Yang ◽  
Shu-Gang Zhang ◽  
...  
Keyword(s):  
Online Learning ◽  
Facial Expression ◽  
Academic Emotions ◽  
Facial Expression Database ◽  
Hand Occlusion

Spontaneous facial expression database for academic emotion inference in online learning

2019 ◽  
Vol 13 (3) ◽  
pp. 329-337 ◽  
Author(s):  
Cunling Bian ◽  
Ya Zhang ◽  
Fei Yang ◽  
Wei Bi ◽  
Weigang Lu
Keyword(s):  
Online Learning ◽  
Facial Expression ◽  
Academic Emotion ◽  
Facial Expression Database

scholarly journals Student's Learning Strategies and Academic Emotions: Their Influence on Learning Satisfaction During the COVID-19 Pandemic

2021 ◽  
Vol 12 ◽  
Author(s):  
Changcheng Wu ◽  
Bin Jing ◽  
Xue Gong ◽  
Ya Mou ◽  
Junyi Li
Keyword(s):  
Online Learning ◽  
Learning Strategies ◽  
Online Teaching ◽  
Value Theory ◽  
Equation Modeling ◽  
Behavioral Engagement ◽  
Learning Context ◽  
Academic Emotions ◽  
Learning Satisfaction ◽  
Learning Platform

Background: Based on the control-value theory (CVT), learning strategies and academic emotions are closely related to learning achievement, and have been considered as important factors influencing student's learning satisfaction and learning performance in the online learning context. However, only a few studies have focused on the influence of learning strategies on academic emotions and the interaction of learning strategies with behavioral engagement and social interaction on learning satisfaction.Methods: The participants were 363 pre-service teachers in China, and we used structural equation modeling (SEM) to analyze the mediating and moderating effects of the data.Results: The main findings of the current study showed that learning strategies influence students' online learning satisfaction through academic emotions. The interaction between learning strategies and behavioral engagement was also an important factor influencing online learning satisfaction.Conclusions: We explored the internal mechanism and boundary conditions of how learning strategies influenced learning satisfaction to provide intellectual guarantee and theoretical support for the online teaching design and online learning platform. This study provides theoretical contributions to the CVT and practical value for massive open online courses (MOOCs), flipped classrooms and blended learning in the future.

scholarly journals P2-35: The KU Facial Expression Database: A Validated Database of Emotional and Conversational Expressions

i-Perception ◽  
10.1068/if694 ◽  
2012 ◽  
Vol 3 (9) ◽  
pp. 694-694 ◽  
Author(s):  
Haenah Lee ◽  
Ahyoung Shin ◽  
BoRa Kim ◽  
Christian Wallraven
Keyword(s):  
Facial Expression ◽  
Facial Expression Database

Analyses of a Multimodal Spontaneous Facial Expression Database

2013 ◽  
Vol 4 (1) ◽  
pp. 34-46 ◽  
Author(s):  
Shangfei Wang ◽  
Zhilei Liu ◽  
Zhaoyu Wang ◽  
Guobing Wu ◽  
Peijia Shen ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Database

scholarly journals GFT Facial Expression Database

2018 ◽  
Author(s):  
Jeffrey M. Girard ◽  
Wen-Sheng Chu ◽  
László A Jeni ◽  
Jeffrey F Cohn ◽  
Fernando De la Torre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Interpersonal Communication ◽  
Group Formation ◽  
Interpersonal Behavior ◽  
Facial Expression Analysis ◽  
Video Frames ◽  
Landmark Tracking ◽  
Linear Svm ◽  
Facial Expression Database

Despite the important role that facial expressions play in interpersonal communication and our knowledge that interpersonal behavior is influenced by social context, no currently available facial expression database includes multiple interacting participants. The Sayette Group Formation Task (GFT) database addresses the need for well-annotated video of multiple participants during unscripted interactions. The database includes 172,800 video frames from 96 participants in 32 three-person groups. To aid in the development of automated facial expression analysis systems, GFT includes expert annotations of FACS occurrence and intensity, facial landmark tracking, and baseline results for linear SVM, deep learning, active patch learning, and personalized classification. Baseline performance is quantified and compared using identical partitioning and a variety of metrics (including means and confidence intervals). The highest performance scores were found for the deep learning and active patch learning methods. Learn more at http://osf.io/7wcyz.

scholarly journals DEVELOPMENT AND VALIDATION OF THE KOKORO RESEARCH CENTER (KRC) FACIAL EXPRESSION DATABASE

PSYCHOLOGIA ◽  
2019 ◽  
Vol 61 (4) ◽  
pp. 221-240 ◽  
Author(s):  
Yoshiyuki UEDA ◽  
Masato NUNOI ◽  
Sakiko YOSHIKAWA
Keyword(s):  
Facial Expression ◽  
Research Center ◽  
Development And Validation ◽  
Facial Expression Database

Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology

2019 ◽  
Vol 58 (1) ◽  
pp. 63-86 ◽  
Author(s):  
Zhaoli Zhang ◽  
Zhenhua Li ◽  
Hai Liu ◽  
Taihe Cao ◽  
Sannyuya Liu
Keyword(s):  
Online Learning ◽  
Facial Expression ◽  
Fundamental Problem ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Expression Recognition ◽  
Learning Engagement ◽  
Movement Data ◽  
Efficient Detection ◽  
Mouse Movement

Online learning engagement detection is a fundamental problem in educational information technology. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real time. To improve the accuracy of learning engagement detection, we have collected two aspects of students’ behavior data: face data (using adaptive weighted Local Gray Code Patterns for facial expression recognition) and mouse interaction. In this article, we propose a novel learning engagement detection algorithm based on the collected data (students’ behavior), which come from the cameras and the mouse in the online learning environment. The cameras were utilized to capture students’ face images, while the mouse movement data were captured simultaneously. In the process of image data labeling, we built two datasets for classifier training and testing. One took the mouse movement data as a reference, while the other did not. We performed experiments on two datasets using several methods and found that the classifier trained by the former dataset had a better performance, and its recognition rate is higher than that of the latter one (94.60% vs. 91.51%).

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