Yükseköğretimde E-Öğrenme Değerlendirme Ölçeğinin geliştirilmesi ve psikometrik değerlendirmesi

Yıl 2024, Cilt: 24 Sayı: 1, 1 - 13, 30.06.2024

Sıdıka Pelit Aksu , Şengül Yaman Sözbir , Canan Uçakcı Asalıoğlu , Sevil Çiçek Özdemir , Ayten Şentürk Erenel

https://doi.org/10.53629/sakaefd.1312178

Öz

E-öğrenme, özellikle COVID-19 salgını sırasında artan, oldukça yeni ve hızlı bir şekilde genişleyen bir eğitim eğilimidir. bu nedenle, e-öğrenmenin standartlaştırılmış araçlarla değerlendirilmesi önem kazanmıştır. Bu çalışma, üniversite öğrencilerine yönelik bir e-öğrenme değerlendirme ölçeği geliştirmeyi amaçlamaktadır. Bu metodolojik çalışma Türkiye'de 434 öğrenci ile gerçekleştirilmiştir. Ölçeğin madde havuzu ilgili literatürden yararlanılarak oluşturulmuştur. Veriler, tanımlayıcı istatistikler, açıklayıcı ve doğrulayıcı faktör analizi (DFA), Pearson korelasyon analizi, bağımlı değişkenler için t-testi ve Cronbach alfa katsayısı kullanılarak tahmin edilmiştir. Uzman görüşlerine göre hesaplanan bu ölçeğin kapsam geçerlilik indeksi 0.84-1.00 arasında değişmektedir. Açımlayıcı faktör analizine göre iki faktörün özdeğeri > 1'dir. Bu iki faktör toplam varyansın 68,6'sını açıklamaktadır. CFA, Chi ön sahası/serbestlik derecesi (χ2/df), karşılaştırmalı uyum göstergesi (CFI) ve ortalama karesel yaklaşım hatası (RMSEA) için olumlu sonuçlar gösterdi. Ölçeğin Cronbach alfa değeri 0.96'dır. Alt boyutların Cronbach alfa ölçüsü birbirinden bağımsız olarak 0.94 ve 0.93'tür. E-Öğrenim Değerlendirme Ölçeği, güçlü test-tekrar test güvenilirliğine sahipti. Sonuç olarak, 18 maddelik E-Öğrenim Değerlendirme Ölçeği, e-öğrenmeyi değerlendirmek için geçerli ve güvenilir bir araçtır. Ölçek, öğrencilerin e-öğrenme sürecini değerlendirmelerini sağlar.

Anahtar Kelimeler

E-learning, E-Learning assessment scale, Higher education, Higher education students.

Development and psychometric evaluation of E-Learning Assessment Scale in Higher Education

Öz

E-learning is a fairly recent and fleetly expanding trend of education that increased particularly during the COVID-19 epidemic. thus, assessing thee-learning with standardized tools has gained significance. This study aims to develop an e-learning assessment scale for university students. This methodological study was performed with 434 students in Türkiye. The item pool of the scale was created grounded on the applicable literature. The data were estimated using descriptive statistics, explanatory and confirmational factor analysis (CFA), Pearson correlation analysis, t-test for dependent variables and Cronbach alpha coefficient. The content validity index of this scale calculated according to experts‟ opinions ranged from 0.84-1.00. According to the explanatory factor analysis, two factors had an eigenvalue> 1. These two factors reckoned for 68.6 of the total variance. CFA showed favourable results for Chi forecourt/ degrees of freedom (χ2/df), comparative fit indicator (CFI) and root mean square error of approximation (RMSEA). The Cronbach alpha of the scale was 0.96. The Cronbach alpha measure of the sub-dimensions independently,0.94 and 0.93. The E-Learning Assessment Scale had strong test-retest reliability. In conclusion, the 18-item E-Learning Assessment Scale was a valid and reliable tool for assessing e-learning. The scale enables students to assess the e-learning process.

Anahtar Kelimeler

E-learning, E-Learning assessment scale, Higher education, Higher education students

Kaynakça

• Acaroğlu, R. (2014). “Reliability and Validity of Turkish Version of the Nurses Professional Values Scale -Revised.” Florence Nightingale Journal of Nursing, 22 (1): 8-16. doi:10.17672/fnhd.88515
• Alpar, R. (2010). “Applied Statistics and Validity-Reliability in Sports, Health and Educational Sciences.” Ankara: Detay Publications.
• Amir, L. R., Tanti, I., Maharani, D. A., Wimardhani, Y. S., Julia, V., Sulijaya, B., & Ria. P. (2020). “Student Perspective of Classroom and Distance Learning During COVID-19 Pandemic in the Undergraduate Dental Study Program Universitas Indonesia.” BMC Medical Education 20 (1): 1-8. doi:10.1186/s12909-020-02312-0.
• Buyukozturk, S. (2020). Data Analysis Handbook For Social Sciences: Statistical Research Design SPSS Applications and Interpretation. Ankara: Pegem Akademi Publications.
• elen, F. K., Celik, A., & Seferoglu, S. S. (2011). “Online Learning in Higher Education: Problems in the System and Solutions.” Journal of European Education, 1 (1): 25-34.
• Chaney, B. H., Eddy, J. M., Dorman, S. M., Glessner, L., Green, B. L., & Lara-Alecio. R. (2007). “Development of an Instrument to Assess Student Opinions of the Quality of Distance Education Courses.” The American Journal of Distance Education, 21 (3): 145-164. doi:10.1080/08923640701341679.
• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.
• Costello, A. B., & Osborne J. W. (2005). “Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis.” Practical Assessment, Research and Evaluation, 10 (7): 1-9. doi:10.7275/jyj1-4868.
• Demir, Z. K., & Horzum, M. B. (2013). “The Relationship Between Online Learning Students' Readiness for Online Learning, Their Perceived Structure and Interaction.” Educational Sciences in Theory and Practice, 13 (3): 1783-1797. doi: 10.12738/estp.2013.3.1580.
• Deshwal, P., Trivedi, A., & Himanshi. H. L. N. (2017). “Online Learning Experience Scale Validation and its Impact on Learners’ Satisfaction.” Procedia Computer Science, 112: 2455-2462. doi:10.1016/j.procs.2017.08.178.
• Dray B. J., Lowenthal P. R., Miszkiewicz M. J., Ruiz‐Primo, M. A., & Marczynski. K. (2011). “Developing an Instrument to Assess Student Readiness for Online Learning: A Validation Study.” Distance Education, 32 (1):29-47 doi:0.1080/01587919.2011.565496.
• Esin, M. N. (2014). “Reliability and Validity of Data Collection Methods and Tools and Data Collection Tools.” In Research in Nursing: Process, Practice and Critical, edited by Semra Erdoğan, Nursen Nahcivan, N., Nihal Esin, 217-230. İstanbul: Nobel Tıp Publications.
• Grove, S. K., Burns, N., & Gray, J. (2013). The Practice of Nursing Research: Appraisal, Synthesis, and Generation of Evidence. Missouri: Elsevier Saunders.
• Guillasper, J. N., Soriano, G. P., & Oducado, R. M. F. (2020). Psychometric properties of ‘attitude towards e-learning scale’among nursing students. International Journal of Educational Sciences, 30(1-3), 1-5.
• Hayran M., and Hayran, M. (2011). Basic Statistics for Health Research. Ankara: Ari Ofset Publications.
• Horzum, M. B., Kaymak, Z. D., & Gungoren, O. C. (2015). “Structural Equation Modeling towards Online Learning Readiness, Academic Motivations, and Perceived Learning.” Educational Sciences: Theory and Practice, 15 (3): 759-70. doi:10.12738/estp.2015.3.2410.
• Klibanov, O. M., Dolder, C., Anderson, K., Kehr, H. A., & Woods, J. A. (2018). “Impact of Distance Education via İnteractive Videoconferencing on Students’ Course Performance and Satisfaction.” Advances in Physiology Education, 42 (1): 21-25. doi:10.1152/advan.00113.2016.
• Kisanga, D., & Ireson, G. (2016). Test of e-Learning Related Attitudes (TeLRA) scale: Development, reliability and validity study. International Journal of Education and Development using ICT, 12(1):20-36.
• Kuriplachová, G., Kováčková, G., Magurová, D., Majerníková, Ľ., & Kendrová, L. (2019). “Advantages and Disadvantages of E-learning in Nursing Teaching Process.” Journal of Health Systems and Policies, 1 (2): 45-54.
• Kürtüncü, M., & Kurt, A. (2020). “Problems of Nursing Students in Distance Education in the Covid-19 Pandemia Period.” Eurasian Journal of Researches in Social and Economics, 7 (5): 66-77.
• Lee, I., & Wang, H. H. (2014). “Preliminary Development of Humanistic Care İndicators For Residents İn Nursing Homes: A Delphi Technique.” Asian Nursing Research, 8(1): 75-81. doi:10.1016/j.anr.2014.03.001.
• Nashwan, A. J., Mohamed, A. S., & Kelly. D. R. (2020). “Nursing Education in the Emergence of COVID-19.” Open Journal of Nursing, 10 (6): 595. doi: 10.4236/ojn.2020.106040.
• Ozdamar, K. (2016). Scale and Test Development Structural Equation Modeling. Ankara: Nisan Publishing.
• Ozturk, E. S., Arpaci, T., & Kalkan, N. (2022). “Development and Psychometric Evaluation of the Nursing Students’ Attitude Towards E-Learning Scale.” Nurse Education in Practice, 63, 103392. doi:10.1016/j.nepr.2022.103392.
• Özutku, R., & Başboğaoğlu, U. (2022). The Scale of Online Learning Perception: The Covid-19 Effect on Shifting Higher Education to Distance Learning in Turkey. E-Uluslararası Pedandragoji Dergisi, 2(1), 17-32.
• Pallant, J. (2001). SPSS Survival Manual. Maidenhead: Open University Press.
• Preacher, K. J., & Yaremych, H. E. (2023). Model selection in structural equation modeling. Handbook of structural equation modeling, 206-222.
• Polit, D.F., & Yang, F.M.. (2016). Measurement and the Measurement of Change. Wolters Kluwer; Philadelphia, PA.
• Sahu, P. (2020). “Closure of Universities due to Coronavirus Disease 2019 (COVID-19): Impact on Education and Mental Health of Students and Academic Ataff.” Cureus, 2019 (4): 4-9. doi:10.7759/cureus.7541.
• Sáiz-Manzanares, M. C., Escolar-Llamazares, M. C., & Arnaiz González, A. (2020). “Effectiveness of Blended Learning in Nursing Education.” International Journal of Environmental Research and Public Health, 17 (5): 1589. doi:10.3390/ijerph17051589.
• Saritepeci, M., & Cakir, H. (2015). “The effect of Blended Learning Environments on Student Motivation and Student Engagement: A Study on Social Studies Course.” Education and Science, 40 (177): 203-16. doi: 10.15390/EB.2015.2592.
• Simuth, J., & Sarmany-Schuller, I. (2010). “Online Learning Barriers”. In Technological Developments in Education and Automation, edited by Magued Iskander, Vikram Kapila, Mohammad A. Karim. Springer. doi:10.1007/978-90-481-3656-8_21.
• Smith, P. J., Murphy, K. L., & Mahoney, S.E., (2003). “Towards Identifying Factors Underlying Readiness for Online Learning: An Exploratory Study.” Distance Education, 24 (1): 57-67. doi:10.1080/01587910303043
• Tavşancil, E. (2018). Measuring Attitudes and Data Analysis with Spss Ankara: Nobel Akademik Publishing.
• Viner, R. M., Russell, S. J., Croker, H., Packer, J., Ward, J., Stansfield, C., … Booy, R. (2020). School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review. The Lancet Child & Adolescent Health, 0(0), 1-8. http://doi.org/10.1016/S2352-4642(20)30095-X.
• Yavuzalp, N., & Bahcivan, E. (2020). The online learning self-efficacy scale: its adaptation into Turkish and interpretation according to various variables. Turkish Online Journal of Distance Education, 21(1), 31-44.
• Yemez, I. (2016). “By Confirmatory Factor Analysıs of The Examınes Structure Valıdıty of Scale of Attitudes Towards Socıal Medıa Adverstises: A Research in Cumhuriyet University, Faculty of Economics and Administrative Sciences.” Cumhuriyet University Journal of Economics and Administrative Sciences, 17 (2): 97-118.
• Yurdugül, H., & Sırakaya, D. A. (2013). Çevrimiçi öğrenme hazır bulunuşluluk ölçeği: Geçerlik ve güvenirlik çalışması. Eğitim ve Bilim, 38(169), 391-406.