A Novel Hybrid Machine Learning Model for Analyzing E-Learning Users' Satisfaction

Analyzing lecturers' and students' satisfaction with using e-learning is important to improve the teaching-learning processes. The existing approaches have been widely employing machine learning algorithms, usage-based, and System Usability Scale (SUS) metrics based on users' opinions, activities, and usability testing, respectively. However, the usage-based and SUS metrics fail to cover users' opinions about e-learning systems and they involve manual features engineering. Whereas, the machine learning classifiers do not analyze satisfaction based on activities and usability. Toward this end, we propose a machine learning model that employs CNN and BiLSTM algorithms to concatenate the features extracted from users' activities, usability testing, and users' opinions. The proposed model is coined as E-learning Users' Satisfaction Detection (El-USD). Experimental results suggest that there is a significant correlation between satisfaction analysis by achieving an average r = 0.778. The evaluation results further suggest that our proposed approach can analyze users' satisfaction accurately.