Uzaktan Eğitimde Kalite İyileştirme Boyutlarının Değerlendirilmesi: SMART-AHP Tabanlı SERVQUAL Yaklaşımı

Year 2019, Volume: 19 Issue: 2, 173 - 187, 30.04.2019

Mehmet Aksaraylı Osman Pala

https://doi.org/10.21121/eab.513557

Cited By: 4

Abstract

Son yıllarda,
internetin ortaya çıkışı ve gelişmesi ile üniversitelerde uzaktan eğitim
programları ve kurumları yaygınlaşmıştır. Rekabetin yoğun olduğu uzaktan eğitim
alanında, kurumların verdiği hizmetin kalitesinin ölçülmesi, kurumların sürdürülebilir
olması için gereklidir. Çalışmada, Dokuz Eylül Üniversitesi Uzaktan Eğitim
Uygulama ve Araştırma Merkezi bünyesinde yer alan programlarda kayıtlı olan 261
öğrenci ile anket yapılmıştır. Öğrencilerin aldıkları hizmetin kalitesine dair
algı ve beklentileri, uzaktan eğitime uyarlanmış SERVQUAL ölçeği ile
ölçülmüştür. SERVQUAL ölçeğinin ana kalite boyutları; Empati, Güven,
Heveslilik, Güvenilirlik ve Web Sitesi İçeriği olarak ele alınmıştır. Kurum
uzmanlarına, kalite boyutlarından hangisini iyileştirmenin kurum açısından en
iyi sonucu vereceği SMART-AHP kullanılarak sorulmuş ve değerlendirmeler elde
edilmiştir. Son olarak ise literatürde ilk defa SERVQUAL ve SMART-AHP birlikte
kullanılarak, kurumun kalite boyutlarından hangisinde yapacağı
iyileştirmenin, öğrencilerin algıladıkları hizmet kalitesini en çok
arttıracağını, hizmeti veren ve alan bağlamında bütünleşik olarak
değerlendirmek mümkün olmuştur. Çalışmanın, hizmet sunan karar vericilere kalite iyileştirme stratejilerini
oluşturmada rehber olabileceği düşünülmekte ve hizmet veren ile alanı birlikte
ele alması nedeniyle gelecek çalışmalara yön vermesi beklenmektedir. 

Keywords

SERVQUAL, Uzaktan Eğitim, SMART-AHP

Evaluation Of Quality Improvement Dimensions In Distance Education: SMART-AHP Based SERVQUAL Approach

Abstract

In recent
years, distance
education programs has become widespread in universities with the presence and
development of internet. In distance learning sector where competition is
intense, measuring the quality of education is indispensable for sustainability
and viability of education programs. In the study, a survey is conducted with
261 students who are enrolled in a program at Dokuz Eylül Üniversitesi Uzaktan
Eğitim Uygulama ve Araştırma Merkezi. Perceptions and expectations of students about
the quality of service which they receive, is measured by SERVQUAL scale which
is adapted to distance learning. The main quality dimensions of SERVQUAL scale are; Empathy, Assurance, Responsiveness, Reliability and Website Content. The
institution's experts evaluated by SMART-AHP which dimension of quality must be
improved to achieve best result for the institution. Finally, SERVQUAL and
SMART-AHP were used together for the first time in the literature to assess
which of the quality dimensions of the institution will improve the quality of
services that students perceive most. The
study is thought to be able to guide quality improvement strategies to service
decision makers and is expected to direct future work due to its framework
which is dealing with both service provider and receiver at the same time.

Keywords

SERVQUAL, Distance Learning, SMART-AHP

References

• Bayrak, M., Aydemır, M., ve Karaman, S. (2017). An Investigation of the Learning Styles and the Satisfaction Levels of the Distance Education Students. Çukurova University. Faculty of Education Journal, 46(1), 231-263.
• Bolat, Y. İ., Aydemir, M., ve Karaman, S. (2017). Uzaktan Eğitim Öğrencilerinin Öğretimsel Etkinliklerde Mobil İnternet Kullanımlarının Teknoloji Kabul Modeline Göre İncelenmesi. Gazi University Journal of Gazi Educational Faculty (GUJGEF), 37(1). 63-91.
• Bolliger, D. U. ve Martindale, T. (2004). Key factors in determining student satisfaction in online courses. International Journal on E-Learning. 3(1):61–67
• Bower, B. L. ve Kamata, A. (2000). Factors influencing student satisfaction with online courses. Academic Exchange Quarterly. 4(3): 52–56.
• Bozkurt, A., Akgun-Ozbek, E., Yilmazel, S., Erdogdu, E., Ucar, H., Guler, E., Sezgin, S., Karadeniz, A., Sen-Ersoy, N., Goksel-Canbek, N., Dinçer, G. D., Ari, S., ve Aydin, C. H. (2015). Trends in distance education research: A content analysis of journals 2009-2013. The International Review of Research in Open and Distributed Learning, 16(1). 330-363.
• Button, D., Harrington, A., ve Belan, I. (2014). E-learning & information communication technology (ICT) in nursing education: A review of the literature. Nurse Education Today, 34(10), 1311-1323.
• Cantelon, J. E. (1995). The evolution and advantages of distance education. New Directions for Adult and Continuing Education. (67): 3-10.
• Chandrana, B., Golden, B. ve Wasil, E. (2005). Linear programming models for estimating weights in the analytic hierarchy process. Computers & Operations Research, 32: 2235-2236.
• Chao, R. J. ve Chen, Y. H. (2009). Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations. Expert Systems with Applications, 36(7), 10657-10662.
• Chen, C. F. (2006). Applying the analytical hierarchy process (AHP) approach to convention site selection. Journal of Travel Research, 45(2): 167-174.
• Chen, C. M., Lee, H. M. ve Chen, Y. H. (2005). Personalized e-learning system using item response theory. Computers and Education, 44(3): 237–255.
• Chiu, C. M. ve Wang, E. T. G. (2008). Understanding web-based learning continuance: The role of subjective task value. Information and Management. 45: 194–201.
• Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C. ve Sun, P. C. (2005). Usability, quality, value and e-learning continuance decisions. Computers and Education, 45(4): 399–416.
• Deuzem“Hakkımızda”http://deuzem.deu.edu.tr/hakkimizda/(14.06.2016).
• Devebakan, N. ve Aksaraylı, M. (2003). Sağlık işletmelerinde algılanan hizmet kalitesinin ölçümünde SERVQUAL skorlarının kullanımı ve Özel Altınordu Hastanesi uygulaması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 5(1): 38-54.
• Diaz, D. P. ve Cartnal, R. B. (1999). Students learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching. 47(4): 130–135.
• Doherty, W. (2006). An analysis of multiple factors affecting retention in web-based community college courses. The Internet and Higher Education, 9(4): 245–255.
• Enea, M. ve Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3(1): 39-62
• Gress, C. L., Fior, M., Hadwin, A. F. ve Winne, P. H. (2010). Measurement and assessment in computer-supported collaborative learning. Computers in Human Behavior, 26(5): 806-814.
• Hawkins, A., Graham, C. R., Sudweeks, R. R., ve Barbour, M. K. (2013). Academic performance, course completion rates, and student perception of the quality and frequency of interaction in a virtual high school. Distance Education, 34(1), 64-83.
• Ho, W. (2008). Integrated analytic hierarchy process and its applications: A literature review. European Journal of Operational Research. 186: 211-228.
• Koernig, S. K. (2003). E‐scapes: The electronic physical environment and service tangibility. Psychology & Marketing, 20(2):151-167.
• LaBay, D. G. ve Comm, C. L. (2003). A case study using gap analysis to assess distance learning versus traditional course delivery. International Journal of Educational Management, 17(7), 312-317.
• Lee, B. C., Yoon, J. O. ve Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329.
• 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. 51(2): 864–873.
• Liaw, S. S., Huang, H. M. ve Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers and Education. 49(4): 1066–1080.
• Liaw, S. S., ve Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.
• Limayem, M. ve Cheung, C. M. K. (2008). Understanding information systems continuance: The case of internet-based learning technologies. Information & Management. 45(4): 227–232.
• Liu, S. H., Liao, H. L. ve Pratt, J. A.(2009). Impact of media richness and flow on e-learning technology acceptance. Computers and Education.52(3): 599–607.
• Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G. ve Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education. 53(3): 950-965.
• Martinez, R. A., Bosch, M. M., Herrero, M. H. ve Nunoz, A. S. (2007). Psychopedagogical components and processes in e-learning. Lessons from an unsuccessful on-line course. Computers in Human Behavior. 23: 146–161.
• Mateo, J.R.S.C. (2012). Multi-Criteria Analysis in the Renewable Energy Industry. Verlag London Limited: SpringerParasuraman, A., Zeithaml, V. A. ve Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(Spring): 12–40.
• Parasuraman, A., Zeithaml, V. A., ve Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. the Journal of Marketing, 41-50.
• Park, S., ve Yun, H. (2017). Relationships between motivational strategies and cognitive learning in distance education courses. Distance Education, 38(3), 302-320.
• Richards, C. N. ve Ridley, D. R. (1997). Factors affecting college students’ persistence in on-line computer-managed instruction. College Student Journal, 31:490–495.
• Richardson, J. T. (2017). Academic attainment in students with autism spectrum disorders in distance education. Open Learning: The Journal of Open, Distance and e-Learning, 32(1), 81-91.
• Roca, J. C. ve Gagne, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24: 1585–1604.
• Shahin, A. ve Mahbod, M. A. (2007). Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting. International Journal of Productivity and Performance Management, 56(3): 226-240.
• Shaik, N., Lowe, S., ve Pinegar, K. (2007). DL-sQUAL: A multiple-item scale for measuring service quality of online distance learning programs. Online Journal of Distance Learning Administration, IX (II).
• Shee, D. Y. ve Wang, Y. S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50(3), 894-905.
• Sherry, L. (1995). Issues in distance learning. International journal of educational telecommunications, 1(4), 337-365.
• Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y. ve Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers and Education, 50(4): 1183–1202.
• Suthers, D. D., Hundhausen, C. D. ve Girardeau, L. E. (2003). Comparing the roles of representations in face-to-face and online computer supported collaborative learning. Computers and Education, 41(4): 335–351.
• Triantaphyllou, E. ve Mann, S. H. (1995). Using the analytic hierarchy process for decision making in engineering applications: some challenges. International Journal of Industrial Engineering: Applications and Practice, 2(1), 35-44.
• Tzeng, G. H., Chiang, C. H. ve Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert systems with Applications, 32(4), 1028-1044.
• Udo, G. J. ve Marquis, G. P. (2002). Factors affecting e-commerce web site effectiveness. The Journal of Computer Information Systems, 42(2): 10-16.
• Udo, G. J., K. K. Bagchi, ve Kirs, P. J. (2011). Using SERVQUAL to assess the quality of e-learning experience. Computers in Human Behavior 27.3: 1272-1283.
• Viberg, O., ve Grönlund, Å. (2017). Understanding students’ learning practices: challenges for design and integration of mobile technology into distance education. Learning, Media and Technology, 42(3), 357-377.
• Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information and Management, 41: 75–86.
• Wang, Y. S., Wang, H. Y. ve Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23: 1792–1808.
• Welsh, E. T., Wanberg, C. R., Brown, K. G. ve Simmering, M. J. (2003). E‐learning: emerging uses, empirical results and future directions. International Journal of Training and Development, 7(4): 245-258.
• Yang, Z., ve Liu, Q. (2007). Research and development of web-based virtual online classroom. Computers and Education, 48(2): 171–184.
• Zawacki-Richter, O., ve Naidu, S. (2016). Mapping research trends from 35 years of publications in Distance Education. Distance Education, 37(3), 245-269.
• Zeithaml, V. A., Berry, L. L. ve Parasuraman, A. (1996). The behavioral consequences of service quality. the Journal of Marketing. 31-46.