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The development of an online learning readiness scale for high school students

Year 2022, Volume: 9 Issue: Special Issue, 126 - 145, 29.11.2022
https://doi.org/10.21449/ijate.1125823

Abstract

Assessing students’ online learning readiness is important since numerous countries have started online learning at all education levels during the Covid-19 pandemic in the 21st century. By taking students’ online learning readiness level into account, it will be easier to establish on-target online learning environments. Although there are a number of online learning readiness scales available aiming at higher-education students in the Turkish setting, there is no scale available specifically for high-school students. This study, therefore, aims to develop a valid and reliable scale to identify the levels of online learning readiness for high school students in Türkiye. In order to develop an Online Learning Readiness Scale for high school students, a mixed-method exploratory sequential design was employed in this study. The first sample consisted of 916 students and the second sample consisted of 323 students who had previously experienced an online learning environment. The data were analyzed through exploratory factor analysis and confirmatory factor analysis. Validity and reliability evidences were also provided. The final version of the scale consisted of a total of 16 items in three dimensions; namely, computer self-efficacy, internet self-efficacy, and self-learning and explained 65.76% of the variance. The results of the study indicate that the Online Learning Readiness Scale (OLRS) developed in this particular study is a reliable and valid measurement tool in the assessment of online learning readiness levels of high school students in Türkiye and is expected to guide researchers and practitioners who focus on assessing high school students’ online learning readiness levels.

Thanks

The authors would like to thank the assistance of Siirt Provincial Directorate of National Education personnel and high school principals who helped researchers to get in touch with high school students.

References

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  • Arbaugh, J.B. (2000). Virtual classroom characteristics and student satisfaction with internet based MBA courses. Journal of Management Education, 24(1), 32 54. https://doi.org/10.1177/105256290002400104
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman/Times Books/ Henry Holt & Co.
  • Bandura, A., Adams, N.E., & Beyer, J. (1977). Cognitive processes mediating behavioral change. Journal of Personality and Social Psychology, 35(3), 125 139. https://doi.org/10.1037//0022-3514.35.3.125
  • Barker, P. (2002). On being an online tutor. Innovations in Education and Teaching International, 39(1), 3 13. https://doi.org/10.1080/13558000110097082
  • Bentler, P.M., & Chou, C.P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78-117. https://doi.org/10.1177/0049124187016001004
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  • Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K.A. Bollen and J.S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
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  • Cattell, R.B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10
  • Chang, S.C., & Tung, F.C. (2008). An empirical investigation of students’ behavioral intentions to use the online learning course website. British Journal of Educational Technology, 39(1), 71-83. https://doi.org/10.1111/j.1467-8535.2007.00742.x
  • Chizmar, J.F., & Walbert, M.S. (1999). Web-based learning environments guided by principles of good teaching practice. The Journal of Economic Education, 30(3), 248 259. https://doi.org/10.1080/00220489909595985
  • Choucri, N., Maugis, V., Madnick, S., Siegel, M., Gillet, S., O’Donnel, S., Best, M., Zhu, H., & Haghseta F. (2003). Global e readiness for what. In N. Choucri, V. Maugis, S. Madnick, & M. Siegel (Eds.), Global e-readiness-for what (pp. 1–47). Center for eBusiness at MIT: Massachusetts Institute of Technology Cambridge, MA.
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  • DeTure, M. (2004). Cognitive Style and Self-Efficacy: Predicting student success in online distance education. American Journal of Distance Education, 18(1), 21 38. https://doi.org/10.1207/s15389286ajde1801 _3
  • DeVellis, R.F. (2017). Scale development: Theory and applications (4th ed.). Sage.
  • Durak, H. (2017). Turkish adaptation of the flipped learning readiness scale for middle school students. Bartın University Journal of Faculty of Education, 6(3), 1056 1068. https://doi.org/10.14686/buefad.328826
  • Forero, G.C., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling, 16(4), 625–641. https://doi.org/10.1080/10705510903203573
  • Gökçearslan, Ş., Solmaz, E., & Kukul, V. (2017). Mobile learning readiness scale: an adaptation study. Eğitim Teknolojisi Kuram ve Uygulama, 7(1), 143 157. https://doi.org/10.17943/etku.288492
  • Guttman, L. (1954). Some necessary conditions for common-factor analysis. Psychometrika, 19(2), 149 161. https://doi.org/10.1007/BF02289162
  • Hill, J.R. (2000). Web-based instruction: Prospects and challenges. Educational media and technology yearbook, 25, 141 55.
  • Horzum, M., Bektaş, M., Ayvaz Can, A., Üngören, Y., & Sellüm, F. (2019). Authentic learning readiness scale for teachers: The validity and reliability study. Uluslararası Alan Eğitimi Dergisi, 5(2), 94-106. https://doi.org/10.32570/ijofe.645859
  • Hu, L.T., & Bentler, P.M. (1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1 55. https://doi.org/10.1080/10705519909540118
  • Hung, M.L. (2016). Teacher readiness for online learning: scale development and teacher perceptions. Computers & Education, 94, 120 133. https://doi.org/10.1016/j.compedu.2015.11.012
  • Hung, M.L., Chou, C., Chen, C.H., & Own, Z.Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080-1090. https://doi.org/10.1016/j.compedu.2010.05.004
  • International Society for Technology in Education (ISTE). (2016). ISTE Standards for Students (ebook): A Practical Guide for Learning with Technology. Susan Brooks-Young.
  • İlhan, M., & Çetin, B. (2013). The validity and reliability study of the Turkish version of an online learning readiness scale. Eğitim Teknolojisi Kuram ve Uygulama, 3(2), 72-101.
  • Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141 151. https://doi.org/10.1177/001316446002000116
  • Katz, Y.J. (2000). The comparative suitability of three ICT distance learning methodologies for college level instruction. Educational Media International, 7(1), 25 30. https://doi.org/10.1080/095239800361482
  • Katz, Y.J. (2002). Attitudes affecting college students’ preferences for distance learning. Journal of Computer Assisted Learning, 18(1), 2 9. https://doi.org/10.1046/j.0266 4909.2001.00202.x
  • Khan, I.M. (2009). An analysis of the motivational factors in online learning (Doctoral dissertation, University of Phoenix). https://www.learntechlib.org/p/127822/
  • Kim, Y., & Glassman, M. (2013). Beyond search and communication: development and validation of the internet self-efficacy scale (ISS). Computers in Human Behavior, 29 (4), 1421-1429. https://doi.org/10.1016/j.chb.2013.01.018
  • Kline, B.R. (2011). Principles and practice of structural equation modeling. (3rd ed.). Guilford
  • Kuo, Y.C., Walker, A., Schroder, K.E.E., & Belland, B.R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35 50. https://doi.org/10.1016/j.iheduc.2013.10.001
  • Lajoie, S.P., & Naismith, L. (2012). Computer-based learning environments. In: Seel, N. M. (eds) Encyclopedia of the sciences of learning. Springer. https://doi.org/10.1007/978-1-4419-1428-6_512
  • Lenahan Bernard, J.M. (2014). Relationship of computer self efficacy and self directed learning readiness to civilian employees’ completion of online courses (Doctoral dissertation, Nova Southeastern University). https://www.proquest.com/docview/1727477573?pqorigsite=gscholar&fromopenview=true
  • Li, C.H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavioral Researcher, 48, 936 949. https://doi.org/10.3758/s13428-015-0619-7
  • Liang, J.C., & Wu, S.H. (2010). Nurses' motivations for web-based learning and the role of internet self-efficacy. Innovations in Education and Teaching International, 47(1), 25-37. https://doi.org/10.1080/14703290903525820
  • Lim, C. K. (2001). Computer self-efficacy, academic self-concept, and other predictors of satisfaction and future participation of adult distance learners. American Journal of Distance Education, 15(2), 41 51. https://doi.org/10.1080/08923640109527083
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The development of an online learning readiness scale for high school students

Year 2022, Volume: 9 Issue: Special Issue, 126 - 145, 29.11.2022
https://doi.org/10.21449/ijate.1125823

Abstract

Assessing students’ online learning readiness is important since numerous countries have started online learning at all education levels during the Covid-19 pandemic in the 21st century. By taking students’ online learning readiness level into account, it will be easier to establish on-target online learning environments. Although there are a number of online learning readiness scales available aiming at higher-education students in the Turkish setting, there is no scale available specifically for high-school students. This study, therefore, aims to develop a valid and reliable scale to identify the levels of online learning readiness for high school students in Türkiye. In order to develop an Online Learning Readiness Scale for high school students, a mixed-method exploratory sequential design was employed in this study. The first sample consisted of 916 students and the second sample consisted of 323 students who had previously experienced an online learning environment. The data were analyzed through exploratory factor analysis and confirmatory factor analysis. Validity and reliability evidences were also provided. The final version of the scale consisted of a total of 16 items in three dimensions; namely, computer self-efficacy, internet self-efficacy, and self-learning and explained 65.76% of the variance. The results of the study indicate that the Online Learning Readiness Scale (OLRS) developed in this particular study is a reliable and valid measurement tool in the assessment of online learning readiness levels of high school students in Türkiye and is expected to guide researchers and practitioners who focus on assessing high school students’ online learning readiness levels.

References

  • Achukwu, C.B., Nwosu, K.C., Uzoekwe, H.E., & Juliana, A. (2015). Computer self-efficacy, computer-related technology dependence and on-line learning readiness of undergraduate students. International Journal of Higher Education Management, 1(2), 60-71.
  • Arbaugh, J.B. (2000). Virtual classroom characteristics and student satisfaction with internet based MBA courses. Journal of Management Education, 24(1), 32 54. https://doi.org/10.1177/105256290002400104
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman/Times Books/ Henry Holt & Co.
  • Bandura, A., Adams, N.E., & Beyer, J. (1977). Cognitive processes mediating behavioral change. Journal of Personality and Social Psychology, 35(3), 125 139. https://doi.org/10.1037//0022-3514.35.3.125
  • Barker, P. (2002). On being an online tutor. Innovations in Education and Teaching International, 39(1), 3 13. https://doi.org/10.1080/13558000110097082
  • Bentler, P.M., & Chou, C.P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78-117. https://doi.org/10.1177/0049124187016001004
  • Bernard, R.M., Brauer, A., Abrami, P.C., & Surkes, M. (2004). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31 47. https://doi.org/10.1080/0158791042000212440 Borotis, S., & Poulymenakou, A. (2004). E learning readiness components: Key issues to consider before adopting e learning interventions. In E learn: World conference on e learning in corporate, government, healthcare, and higher education (pp. 1622 1629).
  • Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K.A. Bollen and J.S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
  • Byrne, B.M. (1998). Structural equation modeling with Lisrel, Prelis, and Simplis: Basic concepts, applications, and programming (1st ed.). Psychology Press. https://doi.org/10.4324/9780203774762
  • Cattell, R.B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10
  • Chang, S.C., & Tung, F.C. (2008). An empirical investigation of students’ behavioral intentions to use the online learning course website. British Journal of Educational Technology, 39(1), 71-83. https://doi.org/10.1111/j.1467-8535.2007.00742.x
  • Chizmar, J.F., & Walbert, M.S. (1999). Web-based learning environments guided by principles of good teaching practice. The Journal of Economic Education, 30(3), 248 259. https://doi.org/10.1080/00220489909595985
  • Choucri, N., Maugis, V., Madnick, S., Siegel, M., Gillet, S., O’Donnel, S., Best, M., Zhu, H., & Haghseta F. (2003). Global e readiness for what. In N. Choucri, V. Maugis, S. Madnick, & M. Siegel (Eds.), Global e-readiness-for what (pp. 1–47). Center for eBusiness at MIT: Massachusetts Institute of Technology Cambridge, MA.
  • Compeau, D.R., & Higgins, C.A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688 Comrey, A.L., & Lee, H.B. (1992). A first course in factor analysis. Lawrence Eribaum Associates.
  • Creswell, J. (2012). Educational research: planning, conducting, and evaluating quantitative and qualitative research. (4th Ed.). Pearson.
  • Creswell, J.W., & Plano Clark, V.L. (2006). Designing and conducting mixed methods research. SAGE.
  • Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
  • Daniels, H.L., & Moore, D.M. (2000). Interaction of cognitive style and learner control in a hypermedia environment. International Journal of Instructional Media, 27(4), 369-382.
  • Demir Kaymak, Z., & Horzum, M.B. (2013). Relationship between online learning readiness and structure and interaction of online learning students. Educational Sciences: Theory and Practice, 13(3), 1792-1797. https://doi.org/10.12738/estp.2013.3.1580
  • DeTure, M. (2004). Cognitive Style and Self-Efficacy: Predicting student success in online distance education. American Journal of Distance Education, 18(1), 21 38. https://doi.org/10.1207/s15389286ajde1801 _3
  • DeVellis, R.F. (2017). Scale development: Theory and applications (4th ed.). Sage.
  • Durak, H. (2017). Turkish adaptation of the flipped learning readiness scale for middle school students. Bartın University Journal of Faculty of Education, 6(3), 1056 1068. https://doi.org/10.14686/buefad.328826
  • Forero, G.C., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling, 16(4), 625–641. https://doi.org/10.1080/10705510903203573
  • Gökçearslan, Ş., Solmaz, E., & Kukul, V. (2017). Mobile learning readiness scale: an adaptation study. Eğitim Teknolojisi Kuram ve Uygulama, 7(1), 143 157. https://doi.org/10.17943/etku.288492
  • Guttman, L. (1954). Some necessary conditions for common-factor analysis. Psychometrika, 19(2), 149 161. https://doi.org/10.1007/BF02289162
  • Hill, J.R. (2000). Web-based instruction: Prospects and challenges. Educational media and technology yearbook, 25, 141 55.
  • Horzum, M., Bektaş, M., Ayvaz Can, A., Üngören, Y., & Sellüm, F. (2019). Authentic learning readiness scale for teachers: The validity and reliability study. Uluslararası Alan Eğitimi Dergisi, 5(2), 94-106. https://doi.org/10.32570/ijofe.645859
  • Hu, L.T., & Bentler, P.M. (1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1 55. https://doi.org/10.1080/10705519909540118
  • Hung, M.L. (2016). Teacher readiness for online learning: scale development and teacher perceptions. Computers & Education, 94, 120 133. https://doi.org/10.1016/j.compedu.2015.11.012
  • Hung, M.L., Chou, C., Chen, C.H., & Own, Z.Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080-1090. https://doi.org/10.1016/j.compedu.2010.05.004
  • International Society for Technology in Education (ISTE). (2016). ISTE Standards for Students (ebook): A Practical Guide for Learning with Technology. Susan Brooks-Young.
  • İlhan, M., & Çetin, B. (2013). The validity and reliability study of the Turkish version of an online learning readiness scale. Eğitim Teknolojisi Kuram ve Uygulama, 3(2), 72-101.
  • Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141 151. https://doi.org/10.1177/001316446002000116
  • Katz, Y.J. (2000). The comparative suitability of three ICT distance learning methodologies for college level instruction. Educational Media International, 7(1), 25 30. https://doi.org/10.1080/095239800361482
  • Katz, Y.J. (2002). Attitudes affecting college students’ preferences for distance learning. Journal of Computer Assisted Learning, 18(1), 2 9. https://doi.org/10.1046/j.0266 4909.2001.00202.x
  • Khan, I.M. (2009). An analysis of the motivational factors in online learning (Doctoral dissertation, University of Phoenix). https://www.learntechlib.org/p/127822/
  • Kim, Y., & Glassman, M. (2013). Beyond search and communication: development and validation of the internet self-efficacy scale (ISS). Computers in Human Behavior, 29 (4), 1421-1429. https://doi.org/10.1016/j.chb.2013.01.018
  • Kline, B.R. (2011). Principles and practice of structural equation modeling. (3rd ed.). Guilford
  • Kuo, Y.C., Walker, A., Schroder, K.E.E., & Belland, B.R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35 50. https://doi.org/10.1016/j.iheduc.2013.10.001
  • Lajoie, S.P., & Naismith, L. (2012). Computer-based learning environments. In: Seel, N. M. (eds) Encyclopedia of the sciences of learning. Springer. https://doi.org/10.1007/978-1-4419-1428-6_512
  • Lenahan Bernard, J.M. (2014). Relationship of computer self efficacy and self directed learning readiness to civilian employees’ completion of online courses (Doctoral dissertation, Nova Southeastern University). https://www.proquest.com/docview/1727477573?pqorigsite=gscholar&fromopenview=true
  • Li, C.H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavioral Researcher, 48, 936 949. https://doi.org/10.3758/s13428-015-0619-7
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There are 72 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Special Issue
Authors

Mehmet Ramazanoğlu 0000-0001-6860-0895

Sungur Gürel 0000-0003-3425-858X

Ali Çetin 0000-0002-1174-6997

Early Pub Date November 17, 2022
Publication Date November 29, 2022
Submission Date June 6, 2022
Published in Issue Year 2022 Volume: 9 Issue: Special Issue

Cite

APA Ramazanoğlu, M., Gürel, S., & Çetin, A. (2022). The development of an online learning readiness scale for high school students. International Journal of Assessment Tools in Education, 9(Special Issue), 126-145. https://doi.org/10.21449/ijate.1125823

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