E-Pod: E-learning System for Improving Student Engagement in Asynchronous Mode

Abstract

Over the last decade, e-learning has grown significantly as the internet and education have merged to give individuals the possibility to learn new skills. With the COVID-19 pandemic, the use of e-learning has increased in an exponential manner. The asynchronous e-learning mode is found to be appealing to students due to its adoption at any time and in any location. Yet, this mode of learning suffers from lack of interactivity. Under such circumstances, this research proposes E-Pod, an asynchronous e-learning system, which promotes student engagement. Through attention monitoring, when the students are found to be inattentive they are provided with opportunities to engage in a wide range of activities such as summarization activities, puzzles and answering questions to improve the interactivity. The accuracy achieved for the gaze estimation model is 89.5 % and the accuracy achieved for the facial emotion recognition model is 83%. In order to generate FBQ and MCQ questions for students, a SVM model was trained to an accuracy of 95.56%. E-Pod includes a MaLSTM model with 83.98% accuracy for short answer evaluation and a DistilBERT model with 86.8% accuracy for essay answer evaluation. The system is developed using a blend of cutting-edge technologies including image processing, Natural Language Processing, machine learning algorithms and language models. With these features, E-Pod is proposed as an all-inclusive system which stands out from existing e-learning systems and will be helpful for educational institutions to deliver flexible and self-paced learning to their students in asynchronous mode.