Shao-Hsun Chang
ABSTRACT
Advances in information technology facilitate new (or improved) educational and training practices, creating opportunities for new methods and tools; while also, these technologies are changing the educational paradigm. In the past, smart education frameworks were mostly qualitative studies, describing the conceptual framework (and its connotations) of smart educational systems. Although these conceptually structured smart education systems can promote student learning, peer and teacher interaction, and teaching practice, which assist in understanding students’ status and needs in multiple ways and providing students with real-time synchronous/asynchronous guidance and help, unfortunately, many qualitative studies in the past still lack quantitative data to verify the priority of E-Education planning. The experts surveyed by AHP in this article are selected from E-learning and information technology, with a total of 14 people. The results from the software, Expert Choice, found that in the planning of the smart education system, the importance order of the main criteria, from the most important to the least were smart classroom function, technology-based learning system (IoT, Metaverse), teaching monitoring system, conceptual elements of smart learning, respectively. The sub-criteria weight values showed the key factors of E-Learning Education Planning, which in order were educational resources optimization, teaching is differentiated from person to person, students’ cooperation learning, course completion rate, reliable wireless connection (Wi-Fi, IoT apps, Wearable technology). Finally, the least important were smart pedagogies based on learning theory, learning attention detection, and system data backup. In terms of contribution, to our knowledge, very few studies have used the AHP model and hierarchical design method presenting in the current research to quantitatively demonstrate smart education planning and lecture design, to identify and verify the feasibility model of smart education.
- Remington, D. B., & Dent, W. T. 2000. Electronic bill presentment and payment system: Google Patents.Google Scholar
- Kadir Alpaslan Demir. 2021. Smart education framework. Smart Learn. Environ 8, 29. https://doi.org/10.1186/s40561-021-00170-xGoogle ScholarCross Ref
- Zhi-Ting Zhu1 , Ming-Hua Yu & Peter Riezebos. 2016. research framework of smart education. Smart Learning Environments 3, 4. DOI 10.1186/s40561-016-0026-2.Google ScholarCross Ref
- Kiran Budhrani, Yaeeun, Ji & Jae Hoon, Lim. 2018. Unpacking conceptual elements of smart learning in the Korean scholarly discourse. Smart Learning Environments 5, 23. https://doi.org/10.1186/s40561-018-0069-7Google ScholarCross Ref
- I-Chin Wu, Wen-Shan Chen. 2013. Evaluating the E-Learning Platform from the Perspective of Knowledge Management: The AHP Approach. Journal of Library and Information Studies 11, 1 (June).1-24. doi: 10.6182/jlis.2013.11(1).001Google ScholarCross Ref
- Zhu, Z.T.; Yu, M.H.; Riezebos, P. 2016. A research framework of smart education. Smart Learn. Environ3, 4.Google Scholar
- Zhu, Z. T., & He, B. 2012. Smart education: New frontier of educational informatization. E-Education Research 12, 1–13.Google Scholar
- Sandars, J. & Langlois, M. 2005. E-learning and the educator in primary care: Responding to the challenge. Education for Primary Care 16, 129-133.Google Scholar
- Yucel, A. S. 2006. E-learning approach in teacher training [Electronic version]. Turkish Online Journal of Distance Education 7, 4. 123-131.Google Scholar
- Yusufu, G., & Nathan, N. 2020. A novel model of smart education for the development of smart university system. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science. New York: IEEE. https://doi.org/10.1109/icmcecs47690.2020.240912.Google ScholarCross Ref
- Baoping Li, Siu Cheung Kong and Guang Chen. 2015. Development and validation of the smart classroom inventory. Smart Learning Environments 2, 3. DOI 10.1186/s40561-015-0012-0Google ScholarCross Ref
- Li-Shing Huang , Jui-Yuan Su and Tsang-Long Pao. 2019. A Context Aware Smart Classroom Architecture for Smart Campuses. Appl. Sci. 2019, 9, 1837; doi:10.3390/app9091837Google ScholarCross Ref
- Xiang Huang, Xingyu Huang, and Xiaoping Wang. 2021. Construction of the Teaching Quality Monitoring System of Physical Education Courses in Colleges and Universities Based on the Construction of Smart Campus with Artificial Intelligence. Mathematical Problems in Engineering, Volume 2021, Article ID 9907531, https://doi.org/10.1155/2021/9907531Google ScholarCross Ref
- Kiran Budhrani , Yaeeun Ji and Jae Hoon Lim. 2018. Unpacking conceptual elements of smart learning in the Korean scholarly discourse. Smart Learning Environments 5, 23.Google ScholarCross Ref
- Delone, W. H., & McLean, E. R. 2003. The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30.Google ScholarCross Ref
- Schmoldt, D., Kangas, J., Mendoza, G. A., & Pesonen, M. 2013. The analytic hierarchy process in natural resource and environmental decision making 3, Springer Science & Business Media.Google Scholar
- Macharis, C., Springael, J., De Brucker, K., & Verbeke, A. 2004. PROMETHEE and AHP: The design of operational synergies in multicriteria analysis.: Strengthening PROMETHEE with ideas of AHP. European journal of operational research 153, 2, 307-317.Google Scholar
- Hummel, J. M., Bridges, J. F., & IJzerman, M. J. 2014. Group decision making with the analytic hierarchy process in benefit-risk assessment: a tutorial. The Patient-Patient-Centered Outcomes Research 7, 2, 129-140.Google ScholarCross Ref
- Sura I. Mohammed Ali, Marwah Nihad. 2021. Internet of Things for Education Field. Journal of Physics, doi:10.1088/1742-6596/1897/1/012076Google ScholarCross Ref
- Laxmikant, S. 2017. Smart education service model based on IoT technology. International Interdesciplinar Conference on Science Technology Engineering Management Pharmacy and Humanities Held. https://bit.ly/3iyqFRH.Google Scholar
- A Y Ananta, E Rohadi, E Ekojono, V N Wijayaningrum, R Ariyanto, N Noprianto and A R Syulistyo. 2020.IOP Conf. Series: Materials Science and Engineering 732, 012042. doi:10.1088/1757-899X/732/1/012042Google ScholarCross Ref
- Ayman E. Khedr1,, Amira M. Idrees1, and Rashed Salem. 2021. Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm. PeerJ Computer Science, San Diego (Sep 9, 2021). DOI:10.7717/peerj-cs.669Google ScholarCross Ref
- Pise, Anil Audumbar; Vadapalli, Hima; Sanders, Ian. 2022. Estimation of Learning Affects Experienced by Learners: An Approach Using Relational Reasoning and Adaptive Mapping. Wireless Communications and Mobile Computing, Volume 2022, https://doi.org/10.1155/2022/8808283Google ScholarDigital Library
- Shin-Yuan, Hung; Chen, Charlie C; Wan-Ju, Lee. 2009. Moving hospitals toward e-learning adoption: an empirical investigation. Journal of Organizational Change Management 22 ,3, 239-256. DOI 10.1108/09534810910951041Google ScholarCross Ref
- Kadir Alpaslan Demir. 2021. Smart education framework. Smart Learn. Environ 8, 29. https://doi.org/10.1186/s40561-021-00170-xGoogle ScholarCross Ref
- Chanut Poondej and Thanita Lerdpornkulrat. 2019. Gamification in e-learning.Interactive Technology and Smart Education 17, 1, 56-66. DOI 10.1108/ITSE-06-2019-0030.Google Scholar
- Asokan, N., Janson, P. A., Steiner, M., & Waidner, M. 1997. The state of the art in electronic payment systems. Computer 30, 9, 28-35.Google ScholarDigital Library
- Kadambi, K. S., Li, J., & Karp, A. H. 2009. Near-field communication-based secure mobile payment service. Paper presented at the Proceedings of the 11th international Conference on Electronic Commerce.Google Scholar
- Fisher, A. S., & Kaplan, S. J. 2000. Method for supplying automatic status updates using electronic mail: Google Patents.Google Scholar
- Bhushan, N., & Rai, K. 2007. Strategic decision making: applying the analytic hierarchy process: Springer Science & Business Media.Google Scholar
- Lee, Y., & Kozar, K. A. 2006. Investigating the effect of website quality on e-business success: An analytic hierarchy process (AHP) approach. Decision Support Systems 42, 3, 1383-1401.Google ScholarDigital Library
Index Terms
- Measuring the Importance of Smart E-learning education system
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