In education and intervention programs, user engagement has been identified as a major factor in successful program completion. Automatic measurement of user engagement provides helpful information for instructors to meet program objectives and individualize program delivery. In this paper, we present a novel approach for video-based engagement measurement in virtual learning programs. We propose to use affect states, continuous values of valence and arousal extracted from consecutive video frames, along with a new latent affective feature vector and behavioral features for engagement measurement. Deep-learning sequential models are trained and validated on the extracted frame-level features. In addition, due to the fact that engagement is an ordinal variable, we develop the ordinal versions of the above models in order to address the problem of engagement measurement as an ordinal classification problem. We evaluated the performance of the proposed method on the only two publicly available video engagement measurement datasets, DAiSEE and EmotiW-EW, containing videos of students in online learning programs. Our experiments show a state-of-the-art engagement level classification accuracy of 67.4% on the DAiSEE dataset, and a regression mean squared error of 0.0508 on the EmotiW-EW dataset. Our ablation study shows the effectiveness of incorporating affect states and ordinality of engagement in engagement measurement.
Supervised Contrastive Learning for Ordinal Engagement Measurement
KITE Research Institute, University Health Network, Toronto, Canada（KITE研究机构、大学健康网络、多伦多，加拿大）；Institute of Biomedical Engineering, University of Toronto, Toronto, Canada（生物医学工程研究所、多伦多大学、多伦多，加拿大）；College of Engineering and Technology, American University of the Middle East, Kuwait（工程与技术学院、中东美国大学、科威特）
Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Students' behavioral and emotional states need to be analyzed at fine-grained time scales in order to measure their level of engagement. Many existing approaches have developed sequential and spatiotemporal models, such as recurrent neural networks, temporal convolutional networks, and three-dimensional convolutional neural networks, for measuring student engagement from videos. These models are trained to incorporate the order of beha…
Indian Institute of Technology Ropar（印度理工学院罗巴尔分校）；University of Canberra（堪培拉大学）；Australian National University（澳大利亚国立大学）
This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colorado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on `in the wild' data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.
Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recognition of the affect interplay between the members (e.g., joint engagement) that presents as complex, but subtle, nonverbal exchanges between them. Here we present a novel hybrid framework for identifying a parent-child dyad's joint engagement by combining a deep learning framework…
Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）
Automatic detection of students' engagement in online learning settings is a key element to improve the quality of learning and to deliver personalized learning materials to them. Varying levels of engagement exhibited by students in an online classroom is an affective behavior that takes place over space and time. Therefore, we formulate detecting levels of students' engagement from videos as a spatio-temporal classification problem. In this paper, we present a novel end-to-end Residual Network (ResNet) and Temporal Convolutional Network (TCN) hybrid neural network architecture for students' …
Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Moreover, their inference challenge is made harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized …
Transformer-Driven Modeling of Variable Frequency Features for Classifying Student Engagement in Online Learning
Malaviya National Institute of Technology Jaipur（斋浦尔马维亚国家理工学院）；Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）；SRM University AP（SRM 大学阿普拉分校）
Imperial College London（帝国理工学院）；University of Twente（特文特大学）
We address the task of simultaneous feature fusion and modeling of discrete ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling approach. In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the original features. Inference is performed in a novel variational framework, where the recovered latent representations are further constrained by the ordinal output labels. In this way, we seamlessly integrate the ordinal structure in the learned manifold, while attaining robust…
We introduce DAiSEE, the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration in the wild. The dataset has four levels of labels namely - very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. We have also established benchmark results on this dataset using state-of-the-art video classification methods that are available…
Student engagement is an important factor in meeting the goals of virtual learning programs. Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Many existing approaches solve video-based engagement measurement using the traditional frameworks of binary classification (classifying video snippets into engaged or disengaged classes), multi-class classification (classifying video snippets into multiple classes corresponding to different levels of engagement), or regression (estimating a co…
Multimodal Engagement Analysis from Facial Videos in the Classroom
Hector Research Institute of Education Sciences and Psychology, University of Tübingen（图宾根大学赫克托教育科学与心理学研究院）；University of Tübingen（图宾根大学）；Department of Computer Science, University of Colorado Boulder（科罗拉多大学博尔德分校计算机科学系）；Leibniz-Institut für Wissensmedien（莱布尼茨知识媒体研究所）
