Abstract
Considering the rise of online education and an increasing number of students with disabilities in higher education, examining the validity of the Self-efficacy Questionnaire for Online Learning (SeQoL) for students with disabilities is warranted. The purpose of this study is to examine the reliability and validity of (SeQoL; Shen et al., 2013) for students with disabilities in higher education. We analyzed the internal structure, convergent validity, criterion validity, and reliability of SeQoL. A sample of 278 students with disabilities responded to an online survey in Spring 2021. Most of our sample were female, White, and undergraduate students. We used confirmatory factor analysis, correlation, multivariate analysis of variance, and Cronbach’s alpha to analyze the data. Our results indicated that data fit the five factors model with 25 items. Students who preferred online or hybrid courses had significantly higher online learning self-efficacy than face-to-face courses. Limitations and future research were discussed.
Similar content being viewed by others
References
Alamri, A., & Tyler-Wood, T. (2017). Factors affecting learners with disabilities–instructor interaction in online learning. Journal of Special Education Technology, 32(2), 59–69. https://doi: 1177/0162643416681497.
Alivernini, F., & Lucidi, F. (2008). The academic motivation scale (AMS): Factorial structure, invariance, and validity in the italian context. Testing Psychometrics Methodology in Applied Psychology, 15(4), 211–220. https://www.researchgate.net/publication/286683014.
Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research, 9(1), 45–52. https://doi.org/10.19030/cier.v9i1.9549.
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562.
American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association, American Psychological Association and National Council on Measurement in Education.
Bandura, A. (1986). Social foundations of thought and action. Prentice Hall.
Bradley, R. L., Browne, B. L., & Kelley, H. M. (2017). Examining the influence of self-efficacy and self-regulation in online learning. College Student Journal, 51(4), 518–530. https://link-gale-com.proxy.lib.ohio-state.edu/apps/doc/A519935687/AONE?u=colu44332 &sid=googleScholar&xid=0d8b5al0.
Chen, C. H., & Su, C. Y. (2019). Using the BookRoll e-book system to promote self-regulated learning, self-efficacy, and academic achievement for university students. Journal of Educational Technology & Society, 22(4), 33–46. https://doi.org/10.2307/26910183.
Cho, M. H., & Heron, M. L. (2015). Self-regulated learning: The role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course. Distance Education, 36(1), 80–99. https://doi.org/10.1080/01587919.2015.1019963.
Dahlstrom-Hakki, I., Alstad, Z., & Banerjee, M. (2020). Comparing synchronous and asynchronous online discussions for students with disabilities: The impact of social presence. Computers & Education, 150, 103842. https://doi.org/10.1016/j.compedu.2020.103842.
U.S. Department of Education, National Center for Education Statistics (2019). Digest of education statistics, 2018 (2020-009), Chap. 3.
Edwards, F. (2018). The relationship between college student attitudes towards online learning based on reading self-efficacy, ethnicity, and age [Unpublished doctoral dissertation] Liberty University.
Galanek, J. D., Gierdowski, D. C., & Brooks, C. (2018). ECAR study of undergraduate students and information technology, 2018. Research report. ECAR.
Hill, P. (2019, December 8). Fall 2018 IPEDS data: New profile of US higher ed online education. PhilonEdTech. https://philonedtech.com/fall-2018-ipeds-data-new-profile-of-us-higher-ed-online-education/.
Hu, L. T., & 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.
Jan, S. K. (2015). The relationships between academic self-efficacy, computer self-efficacy, prior experience, and satisfaction with online learning. American Journal of Distance Education, 29(1), 30–40. https://doi.org/10.1080/08923647.2015.994366.
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford publications.
Kotera, Y., Cockerill, V., Green, P., Hutchinson, L., Shaw, P., & Bowskill, N. (2019). Towards another kind of borderlessness: Online students with disabilities. Distance Education, 40(2), 170–186. https://doi.org/10.1080/01587919.2019.1600369.
Lee, O. E., Kim, S. Y., & Gezer, T. (2021). Factors associated with online learning self-efficacy among students with disabilities in higher education. American Journal of Distance Education, 35(4), 293–306. https://doi.org/10.1080/08923647.2021.1979344.
Massengale, L. R., & Vasquez, I. I. I., E (2016). Assessing accessibility: How accessible are online courses for students with disabilities? Journal of the Scholarship of Teaching and Learning, 16(1), 69–79. https://doi.org/10.14434/josotl.v16i1.19101.
Newman, L. A., & Madaus, J. W. (2015). Reported accommodations and supports provided to secondary and postsecondary students with disabilities: National perspective. Career Development and Transition for Exceptional Individuals, 38, 173–181. https://doi.org/10.1177/2165143413518235.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.).). McGraw-Hill.
Peechapol, C., Na-Songkhla, J., Sujiva, S., & Luangsodsai, A. (2018). An exploration of factors influencing self-efficacy in online learning: A systematic review. International Journal of Emerging Technologies in Learning, 13(9), https://doi.org/10.3991/ijet.v13i09.8351.
Poulin, R., & Straut, T. (2016). WCET distance education enrollment report 2016. Western Interstate Commission for Higher Education. http://wcet.wiche.edu/initiatives/research/WCET-Distance-Education-Enrollment-Report-2016.
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29(1), 91–97. https://doi.org/10.1016/j.iheduc.2016.01.001.
Rashidi, N., & Moghadam, M. (2014). The effect of teachers’ beliefs and sense of self-efficacy Iranian EFL learners’ satisfaction and academic achievement. The Electronic Journal for English as a Second Language, 18(2). https://www.teslej.org/wordpress/issues/volume18/ej70/ej70a3/.
Rosseel, Y. (2012). lavaan: An R Package for Structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://www.jstatsoft.org/v48/i02/.
Seaman, J. E., Allen, E., & Seaman, J. (2018). Grade increase: Tracking distance education in the United States. Babson Survey Research Group. https://onlinelearningsurvey.com/reports/gradeincrease.pdf.
Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001.
Taipjutorus, W., Hansen, S., & Brown, M. (2012). Investigating a relationship between learner control and self-efficacy in an online learning environment. Journal of Open Flexible and Distance Learning, 16(1), 56–69. https://www.learntechlib.org/p/147977/article_147977.pdf.
Tsai, C. L., Cho, M. H., Marra, R., & Shen, D. (2020). The Self-Efficacy Questionnaire for Online Learning (SeQoL). Distance Education, 41(4), 472–489. https://doi.org/10.1080/01587919.2020.1821604.
Turan, Z., Kucuk, S., & Cilligol Karabey, S. (2022). The university students’ self-regulated effort, flexibility and satisfaction in distance education. International Journal of Educational Technology in Higher Education, 19, https://doi.org/10.1186/s41239-022-00342-w.
Verdinelli, S., & Kutner, D. (2016). Persistence factors among online graduate students with disabilities. Journal of Diversity in Higher Education, 9(4), 353. https://doi.org/10.1037/a0039791.
Yalcin, Y. (2017). Online learners’ satisfaction: Investigating the structural relationships among self- regulation, self-efficacy, task value, learning design, and perceived learning [Unpublished doctoral dissertation]. The Florida State University.
Yu, T. (2018). Examining construct validity of the student online learning readiness (SOLR) instrument using confirmatory factor analysis. Online Learning, 22(4), 277–288. https://doi.org/10.24059/olj.v22i4.1297.
Yu, T., & Richardson, J. C. (2015). An exploratory factor analysis and reliability analysis of the Student Online Learning readiness (SOLR) instrument. Online Learning, 19(5), 120–141. https://doi.org/10.24059/olj.v19i5.593.
Funding
This study is funded by Scholarship on Teaching and Learning grant of the University of North.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gezer, T., Kim, S.Y. & Lee, O.E. Validation of self-efficacy questionnaire of online learning for students with disabilities in higher education. J Comput High Educ (2023). https://doi.org/10.1007/s12528-023-09386-x
Accepted:
Published:
DOI: https://doi.org/10.1007/s12528-023-09386-x