User Engagement Recognition Using Transfer Learning and Multi-task Classification

[1]  Wu Zhang,et al.  Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores , 2018, Comput. Intell. Neurosci..

[2]  Abhay Gupta,et al.  DAiSEE: Towards User Engagement Recognition in the Wild. , 2016, 1609.01885.

[3]  Cemil Oz,et al.  Performance Comparison of Transfer Learning and Training from Scratch Approaches for Deep Facial Expression Recognition , 2019, 2019 4th International Conference on Computer Science and Engineering (UBMK).

[4]  Marcia D. Dixson.  Measuring Student Engagement in the Online Course: The Online Student Engagement Scale (OSE). , 2015 .

[5]  Abhinav Dhall,et al.  Prediction and Localization of Student Engagement in the Wild , 2018, 2018 Digital Image Computing: Techniques and Applications (DICTA).

[6]  Xiang Xiao,et al.  Undertanding and Detecting Divided Attention in Mobile MOOC Learning , 2017, CHI.

[7]  Derek Hoiem,et al.  Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Javier R. Movellan,et al.  The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions , 2014, IEEE Transactions on Affective Computing.

[10]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  Len Hamey,et al.  Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression , 2018, ECML/PKDD.