Blended Learning in Algeria: Assessing Students' Satisfaction and Future Preferences Using SEM and Sentiment Analysis.

Given the still existing restrictions of COVID-19, blended learning is undoubtedly becoming a better-fitting strategy for higher education institutions in underprivileged countries. Acknowledging the current changes in higher education, this study aims to investigate the elements that influence students' satisfaction and their future preferences regarding blended learning in Algeria. A total of 782 questionnaires were collected from different Algerian universities. A structural Equation Modeling (SEM) analysis was conducted to investigate the relationship among the latent variables of the proposed theoretical model. Moreover, an unsupervised sentiment analysis approach was applied to analyze the qualitative data received in the form of feedback from the participants. The results show that students' perceived ease of use and perceived usefulness of blended learning had a significant positive impact on their satisfaction. Similarly, satisfaction had a positive influence on students' future preferences regarding blended learning. In turn, students' perceived ease of use and usefulness had an indirect effect on their future preferences, mediated by satisfaction. Additionally, qualitative data echoed students' eagerness to adopt more advanced learning technologies and what obstacles currently stand in their way. The contribution of this study is to reflect the current situation of blended learning adoption in developing countries and to support future curriculum planning and development. It can also help teachers, students, and policymakers to make better decisions and recommendations for an improved and more sustainable learning and teaching environment in the future.