Volume 10, Issue 1 (6-2023)
Human Information Interaction 2023, 10(1): 1-23 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Norouzi Y, JafariFar N, bighlari Z. Determining user interface accessibility evaluation indicators in virtual education systems using the BWM method. Human Information Interaction 2023; 10 (1)
URL: http://hii.khu.ac.ir/article-1-3087-en.html
URL: http://hii.khu.ac.ir/article-1-3087-en.html
Department of Knowledge and Information Science. Department of Knowledge and Information Science, University of Qom, Iran
Abstract: (2950 Views)
purpose: The article aims to identify and prioritize indicators for evaluating the accessibility capabilities of the user interface in virtual education systems.
Methodology: In step 1, to identify the evaluation indicators from the indicators listed in ISO 9241, a localized checklist was prepared by the virtual education systems inside the country. Then, using the Fuzzy Delphi method and the opinions of experts from the higher education system of Iran in the field of accessibility of education and e-learning systems, the identified indicators were modified and finalized. In the next step, these indicators were prioritized using the BWM method from the point of view of experts, and their importance was determined.
Findings: The final framework of indicators for evaluating the accessibility capabilities of the interaction environment (user interface) of virtual education systems was compiled and prioritized in four general indicators (general guidelines and requirements, inputs, outputs, support services, help, and online documentation) and 24 sub-indices. For this purpose, LINGO software was used. Based on the findings of the research, the component "Compatibility with accessible technologies" won the first rank among all sub-indices. The component "online documentation" was ranked second and "setting accessibility levels" was ranked third. The last rank (rank 24) was awarded to "Camera".
Conclusion: There is no consensus on the standard framework for evaluating the accessibility of virtual education systems. The review of the conducted research showed that there is a research gap in the field of not comprehensively identifying and presenting a comprehensive and coherent picture to evaluate the accessibility of the interaction environment in virtual education systems and it was concluded that to improve the use of virtual education systems, identifying and prioritizing the factors It is necessary to evaluate the accessibility of virtual education systems. The innovation of this article is to provide a comprehensive framework for identifying and prioritizing the accessibility evaluation indicators of the interactive environment in virtual education systems localized for the country. The internet speed in Iran is not high and turning on the camera during virtual classes due to the high volume of the internet it consumes causes disconnection and communication between them. The statistical community of the research was aware of this fact, therefore, according to the existing conditions, they assigned the least weight to this index.
Methodology: In step 1, to identify the evaluation indicators from the indicators listed in ISO 9241, a localized checklist was prepared by the virtual education systems inside the country. Then, using the Fuzzy Delphi method and the opinions of experts from the higher education system of Iran in the field of accessibility of education and e-learning systems, the identified indicators were modified and finalized. In the next step, these indicators were prioritized using the BWM method from the point of view of experts, and their importance was determined.
Findings: The final framework of indicators for evaluating the accessibility capabilities of the interaction environment (user interface) of virtual education systems was compiled and prioritized in four general indicators (general guidelines and requirements, inputs, outputs, support services, help, and online documentation) and 24 sub-indices. For this purpose, LINGO software was used. Based on the findings of the research, the component "Compatibility with accessible technologies" won the first rank among all sub-indices. The component "online documentation" was ranked second and "setting accessibility levels" was ranked third. The last rank (rank 24) was awarded to "Camera".
Conclusion: There is no consensus on the standard framework for evaluating the accessibility of virtual education systems. The review of the conducted research showed that there is a research gap in the field of not comprehensively identifying and presenting a comprehensive and coherent picture to evaluate the accessibility of the interaction environment in virtual education systems and it was concluded that to improve the use of virtual education systems, identifying and prioritizing the factors It is necessary to evaluate the accessibility of virtual education systems. The innovation of this article is to provide a comprehensive framework for identifying and prioritizing the accessibility evaluation indicators of the interactive environment in virtual education systems localized for the country. The internet speed in Iran is not high and turning on the camera during virtual classes due to the high volume of the internet it consumes causes disconnection and communication between them. The statistical community of the research was aware of this fact, therefore, according to the existing conditions, they assigned the least weight to this index.
Keywords: Accessibility, fuzzy Delphi, user interface, best-worst method (BWM), virtual education system, interactive environment
References
1. Abran, A., Khelifi, A., Suryn, W., & Seffah, A. (2003). Usability meanings and interpretations in ISO standards. Software quality journal, 11(4), 325-338.
3. ahmady, R., ahmady, G., & zamyad, G. (2013). Investigating and explaining the effective factors in the acceptance and use of e-learning systems among e-learning students of Iran University of Science and Technology. Journal of Research in Educational Science, 6(19), 101-126. [In Persian].
4. Amado-Salvatierra, H. R., Hernández, R., & Hilera, J. R. (2012). Implementation of accessibility standards in the process of course design in virtual learning environments. Procedia Computer Science, 14, 363-370.
6. Askarinejad, M. (2021). Providing a Structural Model for the Use of E-Learning System with Empha-sis on the Mediation of Behavioral
7. Tendency, Perceived Ease and Usefulness. Research in School and Virtual Learning, 9(2), 39-48. [In Persian].
8. Azeta, A. A., Ayo, C. K., Atayero, A. A., & Ikhu-Omoregbe, N. A. (2010). Application of voiceXML in e-learning systems. In Cases on Successful E-Learning Practices in the Developed and Developing World: Methods for the Global Information Economy (pp. 92-108). IGI Global.
10. Bazargan, K. (2021) Relationship between Students' Readiness for e-Learning, Learner Satisfaction and Student Performance: The case of a post-graduate education program. IRPHE; 27 (3), 113-141. [In Persian].
11. Bazargan, K. (2021). Relationship between Students' Readiness for e-Learning, Learner Satisfaction and Student Performance: The case of a post-graduate education program. IRPHE, 27 (3), 113-141. [In Persian].
12. Bouzon, M., Govindan, K., Rodriguez, C. M. T., & Campos, L. M. (2016). Identification and analysis of reverse logistics barriers using method and AHP. Resources, conservation and recycling, 108, 182-197.
14. Chang, V., Walters, R. J., & Wills, G. (2013). The development that leads to the Cloud Computing Business Framework. International Journal of Information Management, 33(3), 524-538.
16. Chua, B. B., & Dyson, L. E. (2004, December). Applying the ISO 9126 model to the evaluation of an e-learning system. In Proc. of ASCILITE (Vol. 5, No. 8, pp. 184-190).
17. Dawood, K. A., Zaidan, A. A., Sharif, K. Y., Ghani, A. A., Zulzalil, H., & Zaidan, B. B. (2021). Novel multi-perspective usability evaluation framework for selection of open source software based on BWM and group VIKOR techniques. International Journal of Information Technology & Decision Making, 1-91.
19. Fazli, S. S., Saffaryan, S., & Hashemnejad, F. (2012). The Study of the Effect of IT Training Courses on Improving the Performance of the Staff in Mazandran Medical Science University. Information and Communication Technology in Educational Sciences, 2(4), 129-144. [In Persian].
20. Forman, E. H., & Selly, M. A. (2001). Decision by objectives: how to convince others that you are right. World Scientific.
22. Granić, A. (2008). Experience with usability evaluation of e-learning systems. Universal Access in the Information Society, 7(4), 209-221.
24. Hsu, Y. L., Lee, C. H., & Kreng, V. B. (2010). The application of Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419-425.
26. Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., & Mieno, H. (1993). The max-min Delphi method and method via fuzzy integration. Fuzzy sets and systems, 55(3), 241-253.
28. Kannan, D., de Sousa Jabbour, A. B. L., & Jabbour, C. J. C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of operational research, 233(2), 432-447.
30. Kiget, N. K., Wanyembi, G., & Peters, A. I. (2014). Evaluating usability of e-learning systems in universities. International Journal of Advanced Computer Science and Applications, 5(8).
32. Lotfi Zadeh, A. (1995). Fuzzy sets, Information and Control, 8, 338-353.
34. Medina-Flores, R., & Morales-Gamboa, R. (2015). Usability evaluation by experts of a learning management system. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 10(4), 197-203.
36. mohaghar, A., Bazazzadeh, S. H., & eghbal, R. (2017). Identification and Prioritization of Effective Factors on Online Advertising in Iran's Market by Use of Fuzzy MADM Technics (Case Study: Clothing Industry). Modern Research in Decision Making, 2(1), 149-178. [In Persian].
38. Muhammad, A. H., Siddique, A., Youssef, A. E., Saleem, K., Shahzad, B., Akram, A., & Al-Thnian, A. B. S. (2020). A hierarchical model to evaluate the quality of web-based e-learning systems. Sustainability, 12(10), 40-71.
40. Norouzi, Y.; Abdul Majeed, A. H. (2010). Human interactions of non-human interfaces: a reflection on the user interface environment in the process of distance education, Book Mah Keliat, 14(9), 86-94. [In Persian].
41. Nurhudatiana, A., Hiu, A. N., & Ce, W. (2018, September). Should I use laptop or smartphone? a usability study on an online learning application. In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 565-570). IEEE.
43. Pourrajabi Talemi, S. (2018), Rcognizing and Prioritizing the Effective Factors in the Success of Advertisements Using BWM, Master's Thesis, Rahbord Shomal Institue. [In Persian].
44. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
46. Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
48. Sadeghi, R. (2019). Teacher; Virtual space and classroom management methods. Development of Persian language and literature education, number 131, 8-13. [In Persian].
49. Sadeghi, S., Rasouli, N., & Jandaghi, G. (2016). Identifying and prioritizing contributing factors in supply chain competitiveness by using PLS-BWM techniques (case study: Payam shoes company). World Scientific News, 2(49), 117-143.
50. Shahhoseini, M. A., Narenji thani, F., Ebadi, R., & Roodbari, H. (2015). Service quality Evaluation of teaching-learning system in higher education. Academic Librarianship and Information Research, 49(2), 277-303. [In Persian].
51. van Roekel, W. S. (2017). Improving international logistics performance measurement. Master of Science in Systems Engineering, Policy Analysis
52. and Management, Netherlands: Delft University of Technology.
53. Yazdaanee, F., Ebraaheemzaadehy, E., Zandee, B., Aleepoor, A., & Zaare, H. (2010). Effectiveness of the Electronic Learning System at the virtual college of Oloome Hadees. The Journal of New Thoughts on Education, 6(3), 137-183. [In Persian].
54. Zareisaroukolaei, M., Shams, G., Rezaeizadeh, M., & ghahremani, M. (2020). Determinants of e-learning effectiveness: A qualitative study on the instructor. Research in Teaching, 8(2), 79-55. [In Persian].
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
