Trust among faculty and students as an essential element of Smart Education System
Journal of Contemporary Issues in Business and Government,
2021, Volume 27, Issue 3, Pages 1568-1574
AbstractThe Covid19 pandemic negativity has come up with one silver lining in the educational system, that of more use of smart and intelligent technological interventions in education: Smart and education system. This has sensitised the educators about the kind of technological change that the education systems are about to experience. As with all the technologies smart education system has its pros and cons. However, one of the main impediments in adopting this technological transformation is trust.
During the next phases of the pandemic, colleges everywhere must be able to transition to a completely online format at a moment’s notice. Once the COVID-19 crisis ends, the need for agile teaching models will continue, as a potential health or climate crisis may occur at any time. However, getting the technology in place to offer course content online is not enough to ensure robust teaching.
This research paper is exploring the importance of ‘trust’ with effective and appropriate use of smart education tools and techniques and establish relationship between ‘Smart education’ and ‘trust’ as well as ‘Evaluation and attentiveness’ and ‘trust’ to ensure broad understanding between the faculty and students as their individual experiences. The paper also focuses on involving ethical practises particularly in evaluation procedure while using smart education system.
- Corcoran, P. B., & Wals, A. E. (2004). Higher education and the challenge of sustainability. Dordrecht: Kluwer Academic Publishers, 10, 0-306.
- Huang, R., Tlili, A., Chang, T. W., Zhang, X., Nascimbeni, F., & Burgos, D. (2020). Disrupted classes, undisrupted learning during COVID-19 outbreak in China: application of open educational practices and resources. Smart Learning Environments, 7(1), 1-15.
- Tsai, Y. S., Perrotta, C., & Gašević, D. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), 554-567.
- Veletsianos, G., & Houlden, S. (2019). An analysis of flexible learning and flexibility over the last 40 years of Distance Education. Distance Education, 40(4), 454-468.
- Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause review, 27, 1-12.
- Coccoli, M., Guercio, A., Maresca, P., & Stanganelli, L. (2014). Smarter universities: A vision for the fast-changing digital era. Journal of Visual Languages & Computing, 25(6), 1003-1011.
- Mulà, I., & Tilbury, D. (2009). A United Nations Decade of Education for Sustainable Development (2005–14) What Difference Will It Make? Journal of Education for Sustainable Development, 3(1), 87-97.
- Flavin, M. (2012). Disruptive technologies in higher education. Research in Learning Technology, 20.
- Basu, C. (2009). Disrupting class how disruptive innovation will change the way the world learns. Journal of Information Privacy and Security, 5(4), 70-71.
- Singh, H., & Miah, S. J. (2020). Smart education literature: A theoretical analysis. Education and Information Technologies, 25(4), 3299-3328.
- Meng, Q., Jia, J., & Zhang, Z. (2020). A framework of smart pedagogy based on the facilitating of high order thinking skills. Interactive Technology and Smart Education.
- Raeder, J., Larson, D., Li, W., Kepko, E. L., & Fuller-Rowell, T. (2008). OpenGGCM simulations for the THEMIS mission. Space Science Reviews, 141(1), 535-555.
- Santana-Mancilla P. C., Echeverría, M. A. M., Santos, J. C. R., Castellanos, J. A. N., & Díaz, A. P. S. (2013). Towards smart education: Ambient intelligence in the Mexican classrooms. Procedia-Social and Behavioral Sciences, 106, 3141-3148.
- Bajaj, R., & Sharma, V. (2018). Smart Education with artificial intelligence-based determination of learning styles. Procedia computer science, 132, 834-842.
- Tikhomirova, N., Gritsenko, A., & Pechenkin, A. (2008). University approach to knowledge management. Vine.
- Yu, Z., Zhao, H., Guo, C., Guo, J., Zhang, S., Hu, K., & Chen, Z. (2020, May). A LSTM Network-based Learners’ Monitoring Model for Academic Self-efficacy Evaluation Using EEG Signal Analysis. In 2020 5th IEEE International Conference on Big Data Analytics (ICBDA) (pp. 154-159). IEEE.
- Uskov, V. L., Bakken, J. P., Gayke, K., Fatima, J., Galloway, B., Ganapathi, K. S., & Jose, D. (2020). Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction. In Smart Education and e-Learning 2020 (pp. 3-18). Springer, Singapore.
- Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49.
- Asif, R., Merceron, A., & Pathan, M. K. (2014). Predicting student academic performance at degree level: a case study. International Journal of Intelligent Systems and Applications, 7(1), 49.1, pp. 49-61, 2014.
- Hasan, R., Palaniappan, S., Raziff, A. R. A., Mahmood, S., & Sarker, K. U. (2018, August). Student academic performance prediction by using decision tree algorithm. In 2018 4th international conference on computer and information sciences (ICCOINS) (pp. 1-5). IEEE.
- Article View: 132
- PDF Download: 138