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Multi-Expert and Multi-Criteria Evaluation of Online Education Factors: A Fuzzy AHP Approach


Affiliations
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2 Chitkara Business School, Chitkara University, Punjab, India
 

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COVID-19 has highly impacted industry, agriculture, services sector as well as education sector all over the world. The countries have seen a complete lockdown, and it has badly affected students' lives in the education sector. Almost more than 32 crores of learners are unable to move to schools or colleges in India. The solution to overcome the offline education crisis is to move to online platforms. But, the effectiveness of online platforms for teaching is a big challenge. The most important thing in teaching is achieving the satisfaction level of students. The literature shows many factors impact satisfaction level, and these factors are ICT orientation, Big-Five Personality Dimensions, Instructor Quality, and Course Design. These factors are having subfactors four, five, seven, and six, respectively. The current study targets to prioritize the factors by using the fuzzy AHP approach. The factors are pritorized based on their normalized weight. To gain depth insights, the sub-factors are also prioritized, and they are ranked relatively as well as globally. Relatively means to figure out the important and least sub-factor from the corresponding factor, globally means to rank each sub-factor among all identified factors. The results show that BF is the most important and CD is the least important factor for achieving students satisfaction level. Looking at relative weights, NE and LQ are the most important factors among BF and CD, respectively. After considering global weights, PI and AD are the most and least important sub-factors, respectively.

Keywords

Online Classes, Fuzzy AHP, Instructor Quality, ICT Orientation, COVID 19.
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  • Ajzen, I. (2015). Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions. Italian Review of Agricultural Economics, 70(2), 121–138. https://doi.org/10.13128/REA-18003
  • Al-araibi, A. A. M., Mahrin, M. N. Bin, & Yusoff, R. C. M. (2019). Technological aspect factors of E-learning readiness in higher education institutions: Delphi technique. Education and Information Technologies, 24(1), 567–590. https://doi.org/10.1007/s10639-018-9780-9
  • Almaiah, M. A., & Al Mulhem, A. (2018). A conceptual framework for determining the su c c e s s f a c to r s o f E- l e a rn in g sy s t em implementation using Delphi technique. Journal of Theoretical and Applied Information Technology, 96(17), 5962–5976.
  • Almaiah, M. A., Jalil, M. @. M. A., & Man, M. (2016a). Empirical investigation to explore factors that achieve high quality of mobile learning system based on students’ perspectives. Engineering Science and Technology, an International Journal, 19(3), 1314–1320. https://doi.org/10.1016/j.jestch.2016.03.004
  • Almaiah, M. A., Jalil, M. A., & Man, M. (2016b). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485. https://doi.org/10.1007/s40692-0160074-1
  • Amin Almaiah, M., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261–5280. https://doi.org/10.1007/s10639-02010219-y
  • Aung, T. N., & Khaing, S. S. (2016). Challenges of implementing e-learning in developing countries: A review. Advances in Intelligent Systems and Computing, 388, 405–411. https://doi.org/10.1007/978-3-319-23207-2_41
  • Bangert, A. W. (2006). The development of an instrument for assessing online teaching effectiveness. Journal of Educational Computing R e s e a r c h , 3 5 ( 3 ) , 2 2 7 – 2 4 4 . https://doi.org/10.2190/B3XP-5K61-7Q07-U443
  • Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging T e c h n o l o g i e s , 2 ( 2 ) , 1 1 3 – 1 1 5 . https://doi.org/10.1002/hbe2.191
  • Basilaia, G., & Kvavadze, D. (2020). Transition to Online Education in Schools during a SARSCoV2 Coronavirus (COVID-19) Pandemic in Georgia. Pedagogical Research, 5(4), 1–9. https://doi.org/10.29333/pr/7937
  • Bennett, S., Lockyer, L., & Agostinho, S. (2018). Towards sustainable technology-enhanced innovation in higher education: Advancing learning design by understanding and supporting teacher design practice. British Journal of Educational Technology, 49(6), 1014–1026. https://doi.org/10.1111/bjet.12683
  • Biasutti, M., & El-Deghaidy, H. (2012). Using Wiki in teacher education: Impact on knowledge management processes and student satisfaction. Computers and Education, 59(3), 861–872. https://doi.org/10.1016/j.compedu.2012.04.009
  • Bidjerano, T., & Dai, D. Y. (2007). The relationship between the big-five model of personality and self-regulated learning strategies. Learning and Individual Differences, 17(1), 6 9 – 8 1. https://doi.org/10.1016/j.lindif.2007.02.001
  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of e-learning portal from students’ perspective: A structural equation model (SEM) approach. Interactive Technology and S m a r t E d u c a t i o n , 1 6 ( 2 ) , 9 4 – 1 1 6 . https://doi.org/10.1108/ITSE-05-2018-0027
  • Cohen, A., & Baruth, O. (2017). Personality, learning, and satisfaction in fully online academic courses. Computers in Human Behavior, 72(2), 1–12. https://doi.org/10.1016/j.chb.2017.02.030
  • Connolly, T. M., MacArthur, E., Stansfield, M., & McLellan, E. (2007). A quasi-experimental study of three online learning courses in computing. Computers and Education, 49(2), 345–359. https://doi.org/10.1016/j.compedu.2005.09.001
  • Countries, H., Countries, M., & Index, H. C. (2020). Objective 1 - Continuity of Learning Objective 2- Adequate Financing ( short to Objective 3 - Build Resilience.
  • Craig, A., Coldwell-Neilson, J., Goold, A., & Beekhuyzen, J. (2012). A review of e-learning technologies: Opportunities for teaching and learning. In CSEDU 2012 - Proceedings of the 4th International Conference on Computer Supported E d u c a t i o n ( p p . 2 9 – 4 1 ). https://doi.org/10.5220/0003915400290041
  • Di Vaio, A., Boccia, F., Landriani, L., & Palladino, R. (2020). Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability (Switzerland), 12(12), 1–12. https://doi.org/10.3390/SU12124851
  • Dixson, M. D. (2010). Creating effective student engagement in online courses: What do students find engaging? Journal of the Scholarship of Teaching & Learning, 10(2), 1–13. Retrieved f r o m http://ezproxy.deakin.edu.au/login?url=http://sea rch.ebscohost.com/login.aspx?direct=true&db=e ue&AN=52225431&site=eds-live&scope=site
  • Endres, M. L., Chowdhury, S., Frye, C., & Hurtubis, C. A. (2009). The Multifaceted Nature of Online MBA Student Satisfaction and Impacts on Behavioral Intentions. Journal of Education f o r B u s i n e s s , 8 4 ( 5 ) , 3 0 4 – 3 1 2 . https://doi.org/10.3200/JOEB.84.5.304-312
  • Englund, C., Olofsson, A. D., & Price, L. (2017). Teaching with technology in higher education: un de rs t andin g con cep tua l chang e an d development in practice. Higher Education Research and Development, 36(1), 73–87. https://doi.org/10.1080/07294360.2016.1171300
  • Eysenck, H. J. (1992). Four ways five factors are not basic. Personality and Individual Differences, 13(6), 667–673. https://doi.org/10.1016/01918869(92)90237-J
  • Garba Shawai, Y., & Amin Almaiah, M. (2018). Malay Language Mobile Learning System (MLMLS) using NFC Technology. International Journal of Education and Management E n g i n e e r i n g , 8 ( 2 ) , 1 – 7 . https://doi.org/10.5815/ijeme.2018.02.01
  • Gaytan, J., & McEwen, B. C. (2007). Effective online instructional and assessment strategies. International Journal of Phytoremediation, 21(1), 1 1 7 – 1 3 2 . h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 08923640701341653
  • Gray, J. A., & DiLoreto, M. (2016). The Effects of Student Engagement, Student Satisfaction, and Perceived Learning in Online Learning Environments This. NCPEA International Journal of Educational Leadership Preparation, 11(1), 98–119.
  • He, F., Deng, Y., & Li, W. (2020). Coronavirus disease 2019: What we know? Journal of Medical V i r o l o g y , 9 2 ( 7 ) , 7 1 9 – 7 2 5 . https://doi.org/10.1002/jmv.25766
  • Hofstee, W. K. B., de Raad, B., & Goldberg, L. R. (1992). Integration of the Big Five and Circumplex Approaches to Trait Structure. Journal of Personality and Social Psychology, 63(1), 146–163. https://doi.org/10.1037/00223514.63.1.146
  • Huang, R. (2020). The Chinese Experience in Maintaining Undisrupted Learning in COVID-19 Outbreak. In Handbook on Facilitating Flexible Learning During Educational Disruption (pp. 1–46). Retrieved from https://www.researchgate.net/publication/339939064
  • Kalafatis, S. P., Pollard, M., East, R., & Tsogas, M. H. (1999). Green marketing and Ajzen’s theory of planned behaviour: A cross-market examination. Journal of Consumer Marketing, 16(5), 441–460. https://doi.org/10.1108/07363769910289550
  • Kanwal, F., & Rehman, M. (2017). Factors Affecting E-Learning Adoption in Developing Countries-Empirical Evidence from Pakistan’s Higher Education Sector. IEEE Access, 5, 1 0 9 6 8 – 1 0 9 7 8 . https://doi.org/10.1109/ACCESS.2017.2714379
  • Kearns, L. (2012). Student Assessment in Online Learning: Challenges and Effective Practices.Jolt.Merlot.Org, 8(3), 198–208. Retrieved from http://jolt.merlot.org/vol8no3/kearns_0912.htm
  • Keller, H., & Karau, S. J. (2013). The importance of personality in students’ perceptions of the online learning experience. Computers in Human B e h a v i o r , 2 9 ( 6 ) , 2 4 9 4 – 2 5 0 0 . https://doi.org/10.1016/j.chb.2013.06.007
  • Kukreja, V., Sakshi, Kaur, A., & Aggarwal, A. (2021). What factors impact online education? A factor analysis approach. Journal of Engineering Education Transformations, 34(Special Issue), 365–374. https://doi.org/10.16920/jeet/2021/ v34i0/157180
  • Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers and Education, 5 1 ( 2 ) , 8 6 4 – 8 7 3 . https://doi.org/10.1016/j.compedu.2007.09.005
  • Lin, Y. M., Lin, G. Y., & Laffey, J. M. (2008). Building a social and motivational framework for understanding satisfaction in online learning. Journal of Educational Computing Research, 38(1), 1–27. https://doi.org/10.2190/EC.38.1.a
  • Maina, E. K. (2010). The Communications Commission of Kenya.
  • Makokha, G. L., & Mutisya, D. N. (2015). International Review of Research in Open and Distributed Learning Status of E-Learning in Public Universities in Kenya Status of E-Learning in Public Universities in Kenya. INternational Review of Research in Open and Distributed Learning, 17(3), 120–141.
  • Manochehri, N. N., & Young, J. I. (2006). the Impact of Student Learning Styles With WebBased Learning or Instructor-Based Learning on Student Knowledge and Satisfaction. Quarterly Review of Distance Education, 7(3), 313–316.
  • Mccrae, R. R., & Costa, P. T. (1999). “The fivefactor theory of personality” 2008 - Google Acadèmic.
  • Mitić, S., Nikolić, M., Jankov, J., Vukonjanski, J., & Terek, E. (2017). The impact of information technologies on communication satisfaction and organizational learning in companies in Serbia. Computers in Human Behavior, 76(7), 87–101. https://doi.org/10.1016/j.chb.2017.07.012
  • Mulhanga, M. M., & Lima, S. R. (2017). Podcast as e-Learning Enabler for Developing Countries. In 9th International Conference on Education Technology and Computers (pp. 126–130). https://doi.org/10.1145/3175536.3175581
  • Munteanu, C., Ceobanu, C., Bobâlcǎ, C., & Anton, O. (2010). An analysis of customer satisfaction in a higher education context. International Journal of Publ ic Sector M a n a g e m e n t , 2 3 ( 2 ) , 1 2 4 – 1 4 0 . https://doi.org/10.1108/09513551011022483 [45]paul Black. (2004). Assessment for Learning in the Classroom (pp. 1–14).
  • Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: Results from a worldwide educational assessment. Computers a n d E d u c a t i o n , 3 7 ( 2 ) , 1 6 3 – 1 7 8 . https://doi.org/10.1016/S0360-1315(01)00045-8
  • Ramsden, P. (1991). A Performance Indicator of Teaching Quality in Higher Education: The Course Experience Questionnaire. Studies in Hi g h e r Ed u c a t i o n , 1 6 ( 2 ) , 1 2 9 – 1 5 0 . https://doi.org/10.1080/03075079112331382944
  • Roff, K. A. (2018). Student Satisfaction and/or Di s s a t i s f a c t i o n i n Bl en d ed Le a r n i n g Envi ronmen t s . Front i er s in Educ a tio n T e c h n o l o g y , 1 ( 2 ) , 1 4 9 . https://doi.org/10.22158/fet.v1n2p149
  • Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality and Quantity, 7(0123456789), 1–22. https://doi.org/10.1007/s11135-02001028-z
  • Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced R e s e a r c h , 2 4 ( 4 ) , 9 1 – 9 8 . https://doi.org/10.1016/j.jare.2020.03.005
  • Shinn, E. H., Poston, W. S. C., Kimball, K. T., St. Jeor, S. T., & Foreyt, J. P. (2001). Blood pressure and symptoms of depression and anxiety: A prospective study. American Journal of H y p e r t e n s i o n , 1 4 ( 7 I ) , 6 6 0 – 6 6 4 . https://doi.org/10.1016/S0895-7061(01)01304-8
  • Soto, C. J., & John, O. P. (2017). Short and extrashort forms of the Big Five Inventory–2: The BFI2-S and BFI-2-XS. Journal of Research in P e r s o n a l i t y , 6 8 , 6 9 – 8 1 . https://doi.org/10.1016/j.jrp.2017.02.004
  • Tartavulea, C. V., Albu, C. N., Albu, N., Dieaconescu, R. I., & Petre, S. (2020). Online teaching practices and the effectiveness of the educational process in the wake of the Covid-19 pandemic. Amfiteatru Economic, 22(55), 9 2 0 – 9 3 6 . https://doi.org/10.24818/EA/2020/55/920
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and Test. Computers & Education, 57(4) 2 4 3 2 – 2 4 4 0 . R e t r i e v e d f r o m https://d1wqtxts1xzle7.cloudfront.net/35739921 /CAE-Factors_influencing_teachers_intention_ to_use_technology.pdf?1417035517=&response -content-dispos ition=inline;+fil ename=Factors_influencing_teachers_intention_t.pdf& Expires=1608768133&Signature=In4HLur
  • UNESCO. (2020). Global Education Monitoring (GEM) Report 2020. Retrieved March 12, 2021, from https://en.unesco.org/news/globaleducationmonitoring-gem-report-2020
  • Warren, J., Rixner, S., Greiner, J., & Wong, S. (2014). Facilitating human interaction in an online programming course. In 45th ACM Technical Symposium on Computer Science E d u c a t i o n ( p p . 6 6 5 – 6 7 0 ) . https://doi.org/10.1145/2538862.2538908
  • Wooldridge, M., & Jennings, N. R. (1995). Wooldridge Jennings.pdf. Knowledge Eng. Rev., 10(2), 115–152.
  • Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending Classes Without Stopping Learning: China’s Education Emergency Management Policy in the COVID-19 Outbreak. Journal of Risk and Financial Management, 13(3), 55. https://doi.org/10.3390/jrfm13030055

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  • Multi-Expert and Multi-Criteria Evaluation of Online Education Factors: A Fuzzy AHP Approach

Abstract Views: 257  |  PDF Views: 108

Authors

Vinay Kukreja
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
Arun Aggarwal
Chitkara Business School, Chitkara University, Punjab, India

Abstract


COVID-19 has highly impacted industry, agriculture, services sector as well as education sector all over the world. The countries have seen a complete lockdown, and it has badly affected students' lives in the education sector. Almost more than 32 crores of learners are unable to move to schools or colleges in India. The solution to overcome the offline education crisis is to move to online platforms. But, the effectiveness of online platforms for teaching is a big challenge. The most important thing in teaching is achieving the satisfaction level of students. The literature shows many factors impact satisfaction level, and these factors are ICT orientation, Big-Five Personality Dimensions, Instructor Quality, and Course Design. These factors are having subfactors four, five, seven, and six, respectively. The current study targets to prioritize the factors by using the fuzzy AHP approach. The factors are pritorized based on their normalized weight. To gain depth insights, the sub-factors are also prioritized, and they are ranked relatively as well as globally. Relatively means to figure out the important and least sub-factor from the corresponding factor, globally means to rank each sub-factor among all identified factors. The results show that BF is the most important and CD is the least important factor for achieving students satisfaction level. Looking at relative weights, NE and LQ are the most important factors among BF and CD, respectively. After considering global weights, PI and AD are the most and least important sub-factors, respectively.

Keywords


Online Classes, Fuzzy AHP, Instructor Quality, ICT Orientation, COVID 19.

References





DOI: https://doi.org/10.16920/jeet%2F2021%2Fv35i2%2F158397