Abstract
Various new revolutions are observed with the changing face of learning paradigms in the education industry due to the impact of the global pandemic. This study reveals the intentions of students and academicians towards improving e-learning systems. The Technology Acceptance Model (TAM) is used to identify the acceptability and expected improvements by them. The model identifies certain factors and categorizes them under various hypothesis parameters. A survey was conducted to collect opinions from multiple respondents, consisting of two main parts. The initial part includes questions for academic staff (16 questions), while the second part includes 18 questions for students. The questions focus on the current e-learning system, its adaptability, and the expected improvements at the next level. The data collected can be used to assess e-learning systems and their issues. The integration of blended learning and IoT devices can improve current features. Data was collected for two months, from March to April 2023. The survey was distributed in Middle Eastern universities, and over twenty universities responded. The staff submitted 1080 responses, and approximately 740 university students participated in the second part.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Djamolovhich, S.N.: Improvement of education system management based on modern management approaches (2019)
Jabbarova, A.: The Importance of the Teaching Method-Theory and its Application (2020)
Lin, X.F., et al.: Chinese undergraduate students’ perceptions of mobile learning: conceptions, learning profiles, and approaches (2019)
A. Khan, R., Qudrat-Ullah, H.: Adoption of LMS in Higher Educational Institutions of the Middle East. ASTI, Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50112-9
Moser, L., Seufert, S., Guggemos, J.: Institutional levers to implement digital learning methods in the classroom. Use Case: i-MOOC to foster information literacy in upper secondary education in Switzerland. In: European Association for Research on Learning and Instruction (EARLI) SIG 6-7 Conference (2020)
Alhramelah, A., Alshahrani, H.: Saudi graduate student acceptance of blended learning courses based upon the unified theory of acceptance and use of technology. Aust. Educ. Comput. 35, 1–22 (2020)
Mehmood, R., et al.: UTiLearn: a personalized ubiquitous teaching and learning system for smart societies (2017)
Chen, X., et al.: Learning to stop while learning to predict. In: International Conference on Machine Learning. PMLR (2020)
Rashidov, A.S.: Interactive methods in teaching mathematics: CASE STUDY method. Sci. Res. 3, 21–24 (2020)
Martinez, P.: Machine learning applications in educational settings: current developments and future prospects. Comput. Educ. 128, 143–156 (2022)
Jin, X., et al.: Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms. Gut 69(6), 1002–1009 (2020)
Fernández-Caramés, T.M., Fraga-Lamas, P.J.S.: Teaching and learning IoT cybersecurity and vulnerability assessment with Shodan through practical use cases. Sens. J. 20(11), 3048 (2020)
Smith, J.A., Johnson, K.L.: Exploring IoT security and vulnerability assessment using real-world scenarios. Cybersecur. J. 25(5), 1123–1140 (2021)
Pillai, R., et al.: Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Inf. Technol. People (2023). https://doi.org/10.1108/ITP-02-2021-0152. (ahead-of-print)
Holmes, et al.: Artificial intelligence in education. Globethics Publications (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mahafdah, R.F., Bouallegue, S., Bouallegue, R. (2024). Examining University Students and Teachers’ Behavioral Intention to Upgrade Blended Learning Using an Extended TAM Model. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-57931-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-57931-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57930-1
Online ISBN: 978-3-031-57931-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)