Abstract
This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pre-trained model. Our model was tested and its performance was evaluated and compared to the existing models. It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%. The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’ engagement levels.
Highlights
According to recent researches [1,2,3,4], a large number of universities and educational institutions are increasingly adopting Electronic learning (E-learning) systems; especially after the spread of COVID-19, when schools and universities worldwide announced the closure of dozens of their campuses and governments started exploring alternatives to traditional school programs for a continuous educational process
The performances of the proposed models are as follows: the Histogram of Oriented Gradients (HOG) + Support Vector Machine (SVM) model achieved an accuracy of 67.69%, the CNN model achieved an accuracy of 72.03%, the VGGnet model achieved an accuracy of 68.11%
We suggest to use a deep transfer learning approach by taking knowledge learned in the Action Recognition domain, and leveraging it on our problem of analyzing Micro Actions for engagement detection
Summary
According to recent researches [1,2,3,4], a large number of universities and educational institutions are increasingly adopting Electronic learning (E-learning) systems; especially after the spread of COVID-19, when schools and universities worldwide announced the closure of dozens of their campuses and governments started exploring alternatives to traditional school programs for a continuous educational process. E-learning systems provide different educational activities (e.g., reading, online meetings and exams) in an efficient, affordable and flexible manner. E-learners express different engagement states during these activities (e.g., frustration, excitement, etc.). Negative engagement states may decrease e-learners’ performances leading to potential dropouts. To provide a better pedagogical experience, we suggest the notion of personalizing contents and activities based on users’ engagement levels. Most current e-learning systems do not have engagement level indicators/detectors [5,6]. It is desired to implement e-leaning systems that have the ability to automatically detect and recognize students’ engagement states [7]
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