Özel Gereksinimli Öğrencilerin E-Öğrenme Sistemlerini Kullanma Niyetlerini Etkileyen Değişkenlerin İncelenmesi

Öz

Özel gereksinimli üniversite öğrencilerinin e-öğrenme sitemlerini kullanma durumlarının incelendiği bu araştırmada; katılımcıların bu sistemleri kullanmaya yönelik niyetlerinin sahip olunan yetersizlik sayısı, cinsiyet ve kullanıma yönelik duygular bağlamında belirlenmesi amaçlanmaktadır. Bu amaç doğrultusunda 1711 özel gereksinimli öğrenciye ulaşılmış ve bu katılımcıların e-öğrenme sistemlerini kullanmasını etkileyen niyetleri ile pozitif ve negatif duygularına yönelik ölçümler gerçekleştirilmiştir. Elde edilen veriler betimsel istatistikler, bağımsız örneklemler için iki yönlü ANOVA ve çoklu doğrusal regresyon analizi ile çözümlenmiştir. Elde edilen sonuçlara göre katılımcıların kullanıma yönelik niyetleri ve pozitif duyguları ortalamanın üzerinde bir düzeye sahipken, e-öğrenme sistemlerinin kullanılmasına yönelik negatif duyguları ortalamanın altındadır. Kullanım niyeti cinsiyet ve sahip olunan yetersizlik sayısına göre anlamlı farklılık göstermektedir ve pozitif duygular kullanım niyetinin anlamlı bir yordayıcısıdır. Elde edilen sonuçlar alanyazın ışığında tartışılmış, araştırma ve uygulamaya dönük önerilerde bulunulmuştur.

Anahtar Kelimeler

• E-öğrenme
• Negatif duygular
• Niyet
• Özel gereksinimli öğrenci
• Pozitif duygular
• Teknoloji kabul ve kullanımı

Investigation of the Variables that Affect the Intention of Students with Special Needs to Use E-Learning Systems

Abstract

In this study, which examines the use of e-learning systems by university students with special needs; It is aimed to determine the participants' intention to use these systems in the context of the number of disabilities, gender, and emotions towards use. For this purpose, 1711 students with special needs were reached online and measurements were made regarding the intentions, positive and negative emotions of these participants that affect their use of e-learning systems. The obtained data were analyzed by descriptive statistics, two-way ANOVA for independent samples and multiple linear regression analysis. According to results, while participants' intentions and positive emotions towards use are above average, their negative feelings about using e-learning systems are below average. Intention to use varies significantly according to gender and the number of disabilities, and positive emotions are a significant predictor of intention to use e-learning systems. The results obtained were discussed in the light of the literature, and recommendations for research and practice were made.

Keywords

• E-learning
• Negative emotions
• Intention
• Student with special needs
• Positive emotions
• Technology acceptance and use

Kaynakça

1. Abdullah, F. & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036.
2. Baydaş, Ö. (2015). Öğretmen Adaylarının Gelecekteki Derslerinde Bilişim Teknolojilerini Kullanma Niyetlerini Belirlemeye Yönelik Bir Model Önerisi. Yayınlanmamış doktora tezi, Atatürk Üniversitesi, Erzurum.
3. Beaudry, A. & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS quarterly, 689-710. https://doi.org/10.2307/25750701.
4. Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2. baskı). New York: The Guilford Press.
5. Bühler, C. & Fisseler, B. (2007). Accessible e-learning and educational technology-extending learning opportunities for people with disabilities. İçinde Conference ICL2007 (ss. 11).
6. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş. & Demirel, F. (2013). Bilimsel araştırma yöntemleri (15. baskı). Ankara: Pegem Akademi.
7. Carmien, S. P. & Fischer, G. (2008, April). Design, adoption, and assessment of a socio-technical environment supporting independence for persons with cognitive disabilities. In Proceedings of the sigchi conference on human factors in computing systems (pp. 597-606).
8. Chan, J. M., Lambdin, L., Graham, K., Fragale, C. & Davis, T. (2014). A picture-based activity schedule intervention to teach adults with mild intellectual disability to use an iPad during a leisure activity. Journal of Behavioral Education, 23(2), 247-257. https://doi.org/10.1007/s10864-014-9194-8.

9. Chang, C. T., Hajiyev, J. & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010.
10. Cho, J. & Lee, H. E. (2020). Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities. Disability and health journal, 13(2), 100878. https://doi.org/10.1016/j.dhjo.2019.100878.
11. Cinquin, P. A., Guitton, P. & Sauzéon, H. (2019). Online e-learning and cognitive disabilities: A systematic review. Computers & Education, 130, 152-167. https://doi.org/10.1016/j.compedu.2018.12.004.
12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2. baskı). Hillsdale, NJ: Erlbaum.
13. Copley, J. & Ziviani, J. (2004). Barriers to the use of assistive technology for children with multiple disabilities. Occupational Therapy International, 11(4), 229-243. https://doi.org/10.1002/oti.213.
14. Darcy, S., Maxwell, H. & Green, J. (2016). Disability citizenship and independence through mobile technology? A study exploring adoption and use of a mobile technology platform. Disability & Society, 31(4), 497-519. https://doi.org/10.1080/09687599.2016.1179172.
15. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982.
16. de Sausmarez, A. (2018). An investigation into the views of young people with Autism Spectrum Condition (aged 14-19) on their use of social media. Yayınlanmamış Doktora Tezi, University of Exeter.
17. Emre, İ. E., Akadal, E. & Gülseçen, S. (2018). Örgün ve Uzaktan Eğitim Öğrencileri İçin Kullanılabilirlik Araştırması: Marmara Üniversitesi Web Sitesi. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 2(1), 12-22.
18. Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104.
19. Fraenkel, J.R., Wallen, N. E. & Hyun, H. H. (2012). How to design and evaluate research in education (8. Baskı). New York: McGraw-Hill.
20. Goodman, G., Tiene, D. & Luft, P. (2002). Adoption of assistive technology for computer access among college students with disabilities. Disability and Rehabilitation, 24(1-3), 80-92. https://doi.org/10.1080/09638280110066307.
21. Heward, W. L., Alber, S. R. & Konrad, M. (2006). Exceptional children: An introduction to special education. Pearson Education/Merrill/Prentice Hall.
22. Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.
23. Hutinger, P., Johanson, J. & Stoneburner, R. (1996). Assistive technology applications in educational programs of children with multiple disabilities: A case study report on the state of the practice. Journal of Special Education Technology, 13(1), 16-35. https://doi.org/10.1177/016264349601300103.
24. Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S. & Rizman, Z. I. (2017). E-learning acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871-889. doi: 10.4314/jfas.v9i4S.50.
25. Jamwal, R., Jarman, H. K., Roseingrave, E., Douglas, J. & Winkler, D. (2020). Smart home and communication technology for people with disability: a scoping review. Disability and Rehabilitation: Assistive Technology, 1-21. https://doi.org/10.1080/17483107.2020.1818138.
26. Kane, S. K., Jayant, C., Wobbrock, J. O. & Ladner, R. E. (2009, October). Freedom to roam: a study of mobile device adoption and accessibility for people with visual and motor disabilities. İçinde Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility (ss. 115-122).
27. Kline, R. B. (2011). Convergence of structural equation modeling and multilevel modeling. İçinde Williams, M (Ed.), Handbook of methodological innovation. Thousand Oaks, CA: Sage.
28. Kurt, A. & Kurtoğlu Erden, M. (2020). Özel eğitim alanında teknoloji kullanımı ile ilgili yapılan çalışmaların incelenmesi. Ağrı İbrahim Çeçen Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(1), 47-70. https://doi.org/10.31463/aicusbed.676961.
29. Kyriazos, T. A. (2018). Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 9(08), 2207. doi: 10.4236/psych.2018.98126.
30. Laabidi, M., Jemni, M., Ayed, L. J. B., Brahim, H. B. & Jemaa, A. B. (2014). Learning technologies for people with disabilities. Journal of King Saud University-Computer and Information Sciences, 26(1), 29-45. https://doi.org/10.1016/j.jksuci.2013.10.005.
31. Lowenthal, P., Borup, J., West, R. & Archambault, L. (2020). Thinking beyond Zoom: Using asynchronous video to maintain connection and engagement during the COVID-19 pandemic. Journal of Technology and Teacher Education, 28(2), 383-391.
32. Newbutt, N., Sung, C., Kuo, H. J. & Leahy, M. J. (2017). The acceptance, challenges, and future applications of wearable technology and virtual reality to support people with autism spectrum disorders. İçinde Recent Advances in Technologies for Inclusive Well-Being (ss. 221-241). Springer, Cham.
33. Nora, A. & Snyder, B. P. (2009). Technology and Higher Education: The Impact of ELearning Approaches on Student Academic Achievement, Perceptions and Persistence. Journal of College Student Retention: Research, Theory & Practice, 10(1), 3-19. https://doi.org/10.2190/CS.10.1.b.
34. Nunnally, J. C. (1978). Psychometric theory. (2. baskı). New York.: McGraw-Hill.
35. Onofrio, R., Gandolla, M., Lettieri, E. & Pedrocchi, A. G. (2020). Acceptance Model of an Innovative Assistive Technology by Neurological Patients with a Motor Disability of Their Upper Limb. İçinde International Conference on Applied Human Factors and Ergonomics (ss. 907-913). Springer, Cham.
36. Pal, J., Viswanathan, A., Chandra, P., Nazareth, A., Kameswaran, V., Subramonyam, H., ... & O'Modhrain, S. (2017). Agency in assistive technology adoption: visual impairment and smartphone use in Bangalore. İçinde Proceedings of the 2017 CHI conference on human factors in computing systems (ss. 5929-5940).
37. Park, S. Y., Nam, M. W. & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British journal of educational technology, 43(4), 592-605. https://doi.org/10.1111/j.1467-8535.2011.01229.x.
38. Robinson, J., Dixon, J., Macsween, A., Van Schaik, P. & Martin, D. (2015). The effects of exergaming on balance, gait, technology acceptance and flow experience in people with multiple sclerosis: a randomized controlled trial. BMC sports science, medicine and rehabilitation, 7(1), 1-12. https://doi.org/10.1186/s13102-015-0001-1.
39. Salloum, S. A., Al-Emran, M., Shaalan, K. & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509-530. https://doi.org/10.1007/s10639-018-9786-3.
40. Scherer, M. J. (2017). Technology adoption, acceptance, satisfaction and benefit: integrating various assistive technology outcomes, 12 (1), 1-2. https://doi.org/10.1080/17483107.2016.1253939.
41. Schumacker, R. E., & Lomax, R. G. (1996). A beginner’s guide to structural equation modeling. Mahwah, NJ: Erlbaum.
42. Seale, J. K. (2013). E-learning and disability in higher education: accessibility research and practice. Routledge.
43. Smith, T. E. (2015). Serving students with special needs: A practical guide for administrators. Routledge.
44. Sümer, N. (2000). Yapısal eşitlik modelleri: Temel Kavramlar ve Örnek Uygulamalar. Türk Psikoloji Yazıları, 3(6), 49-74.
45. Şahin, F. (2016). Öğretmen adaylarının bilişim teknolojileri kabul düzeyleri ile bireysel yenilikçilik düzeyleri arasındaki ilişkinin incelenmesi. Yayınlanmamış yüksek lisans tezi, Anadolu Üniversitesi, Eskişehir.
46. Şahin, F., Doğan, E., İlic, U. & Şahin, Y. L. (2021). Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic. Education and Information Technologies, 1-26. https://doi.org/10.1007/s10639-021-10497-0.
47. Tabachnick, B. G. & Fidell, L. S. (2012). Using multivariate statistics. (6. baskı). Pearson.
48. Tarhini, A., Elyas, T., Akour, M. A. & Al-Salti, Z. (2016). Technology, demographic characteristics and e-learning acceptance: A conceptual model based on extended technology acceptance model. Higher Education Studies, 6(3), 72-89. http://dx.doi.org/10.5539/hes.v6n3p72.
49. Tarhini, A., Hone, K. & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163. https://doi.org/10.1016/j.chb.2014.09.020.
50. Theodorou, P. & Meliones, A. (2019). Developing apps for people with sensory disabilities, and implications for technology acceptance models. Global Journal of Information Technology: Emerging Technologies, 9(2), 33-40. https://doi.org/10.18844/gjit.v9i2.4431.
51. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington DC: American Psychological Association.
52. Toquero, C. M. (2020). Challenges and Opportunities for Higher Education Amid the COVID-19 Pandemic: The Philippine Context. Pedagogical Research, 5(4). https://doi.org/10.29333/pr/7947.
53. Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.
54. Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540.
55. Vereenooghe, L., Trussat, F. & Klose, K. (2020). Applying the technology acceptance model to digital mental health interventions: a qualitative exploration with adults with intellectual disabilities. doi: 10.31234/osf.io/vtbr7.
56. Weiss, P. L., Bialik, P. & Kizony, R. (2003). Virtual reality provides leisure time opportunities for young adults with physical and intellectual disabilities. CyberPsychology & Behavior, 6(3), 335-342. https://doi.org/10.1089/109493103322011650.
57. Yusril, A. N. (2020). E-accessibility Analysis in User Experience for People with Disabilities. IJDS: Indonesıan Journal of Dısabılıty Studıes, 7(1), 107-109. http://dx.doi.org/10.21776/ub.ijds.2019.007.01.12.