Engagement Recognition in an E-learning Environment Using Convolutional Neural Network

Abstract: Background. Under the current situation, distance education has rapidly become popular among students and teachers. This educational situation has changed the traditional way of teaching in the classroom. Under this kind of circumstance, students will be required to learn independently. But at the same time, it also brings some drawbacks, and teachers cannot obtain the feedback of students’ engagement in real-time. This thesis explores the feasibility of applying a lightweight model to recognize student engagement and the practicality of the model in a distance education environment. Objectives. This thesis aims to develop and apply a lightweight model based on Convolutional Neural Network(CNN) with acceptable performance to recognize the engagement of students in the environment of distance learning. Evaluate and compare the optimized model with selected original and other models in different performance metrics. Methods. This thesis uses experiments and literature review as research methods. The literature review is conducted to select effective CNN-based models for engagement recognition and feasible strategies for optimizing chosen models. These selected and optimized models are trained, tested, evaluated and compared as independent variables in the experiments. The performance of different models is used as the dependent variable. Results. Based on the literature review results, ShuffleNet v2 is selected as the most suitable CNN architecture for solving the task of engagement recognition. Inception v3 and ResNet are used as the classic CNN architecture for comparison. The attention mechanism and replace activation function are used as optimization methods for ShuffleNet v2. The pre-experiment results show that ShuffleNet v2 using the Leaky ReLU function has the highest accuracy compared with other activation functions. The experimental results show that the optimized model performs better in engagement recognition tasks than the baseline ShuffleNet v2 model, ResNet v2 and Inception v3 models. Conclusions. Through the analysis of the experiment results, the optimized ShuffleNet v2 has the best performance and is the most suitable model for real-world applications and deployments on mobile platforms.