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ADAPTATION OF ONLINE STUDENT ENGAGEMENT SCALE TO TURKISH: VALIDITY AND RELIABILITY STUDY

Year 2022, Volume: 12 Issue: 1, 41 - 56, 14.01.2022
https://doi.org/10.17943/etku.936669

Abstract

One of the most vital factors determining the effectiveness of online learning environments is engagement. Engagement contributes to improving success, motivation, satisfaction, and even reducing isolation. The aim of this study is to adapt the Online Student Engagement Scale developed by Dixson (2010) to Turkish and to create a valid and reliable measurement tool for use in the Turkish environment. It began with the translation of the original scale into Turkish, and it was finalized with linguistic equivalence and pre-pilot application. The study participants consisted of 254 students studying at university. The study used confirmatory factor analysis to show how much a previously determined structure validates the collected data, that is, the level of construct validity. All 19 items and a 4-factor structure (skills, emotion, engagement, and performance) of the original scale were preserved in the Turkish scale, which was observed to be compatible with the original. The resulting fit indices show a good fit above acceptable. Cronbach alpha values vary between α=.77 and α=.87. This study offers a scale essential for determining the engagement of students in online learning, particularly in the current period when online learning applications are being used extensively. Based on such findings, instructional designers and teachers will be able to develop strategies to encourage and guide students in online learning and therefore increase engagement, a crucial factor in online learning.

References

  • Banna, J., Lin, M. F. G., Stewart, M., & Fialkowski, M. K. (2015). Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course. Journal of Online Learning and Teaching, 11(2), 249.
  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International journal of Research in Marketing, 13(2), 139-161.
  • Bowen, S. (2005). Engaged learning: Are we all on the same page. Peer Review, 7(2), 4–7.
  • Britt, R. (2006). Online education: A survey of faculty and students. Radiologic Technology, 77(3), 183- 190.
  • Brooks, L. (2003). How the attitudes of instructors, students, course administrators, and course designers affects the quality of an online learning environment? Online Journal of Distance Learning Administration, 6(4).
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K.A., & Long, J.S. (Eds.), Testing structural equation models (pp. 136-162). Sage.
  • Cohen, A. (2017). Analysis of student activity in web‐supported courses as a tool for predicting dropout. Educational Technology Research and Development, 65(5), 1285–1304. https://doi.org/10.1007/ s11423‐017‐9524‐3
  • Collis, B., De Boer, W., & Slotman, K. (2001). Feedback for web‐based assignments. Journal of Computer Assisted Learning, 17, 306-313. https://doi.org/10.1046/j.0266-4909.2001.00185.x.
  • Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications.Journal of Applied psychology, 78(1), 98-104.
  • Crawford‐Ferre, H. G., & Wiest, L. R. (2012). Effective online instruction in higher education. Quarterly Review of Distance Education, 13(1), 11.
  • Davis BG (1993). Tools of Teaching. Jossey-Bass.
  • Dixson, M. D. (2010). Creating effective student engagement in online courses: What do students find engaging? Journal of Scholarship of Teaching and Learning, 10(2), 1–13.
  • Dixson, M. D. (2015). Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learning, 19(4), n4.
  • Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., & Thompson, G. (2012). Can online courses deliver in‐class results? A comparison of student performance and satisfaction in an online versus a face‐to‐face introductory sociology course. Teaching Sociology, 40(4), 312–331. https:// doi.org/10.1177/0092055X12446624
  • Dunne, E., & Owen, D. (eds) (2013). The Student Engagement Handbook: Practice in Higher Education. Emerald Group.
  • Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices 1, 2 (online). http//www.testpublishers.org.journal.html
  • Hampton, D., & Pearce, P. F. (2016). Student engagement in online nursing courses. Nurse Educator, 41(6), 294-298. https://doi.org/10.1097/NNE.0000000000000275
  • Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. The Journal of Educational Research, 98(3), 184–192. https://doi.org/10.3200/JOER.98.3.184-192
  • Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320-341. https://doi.org/10.1111/ bjet.12235
  • Hoskins, S. L., & van Hooff, J. C. (2005). Motivation and ability: which students use online learning and what influence does it have on their achievement? British Journal of Educational Technology, 36(2), 177-192. doi:10.1111/j.1467-8535.2005.00451.x
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  • Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford, New York.
  • Lim, J. M. (2016). Predicting successful completion using student delay indicators in undergraduate self‐paced online courses. Distance Education, 37(3), 317–332. https://doi.org/10.1080/01587919. 2016.1233050
  • Meyers, L., S., Gamst, G., & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation. Sage.
  • Limperos, A. M., Buckner, M. M., Kaufmann, R., & Frisby, B. N. (2015). Online teaching and technological affordances: An experimental investigation into the impact of modality and clarity on perceived and actual learning. Computers & Education, 83, 1–9. https://doi.org/10.1016/j.compedu.2014.12.015
  • Narciss, S., Proske, A., & Körndle, H. (2007). Promoting self-regulated learning in web-based learning environments. Computers in Human Behavior, 23 (2007), 1126-1144. https://doi.org/10.1016/j.chb.2006.10.006
  • Newman, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). Taking students seriously. Student engagement and achievement in American secondary schools. New York: Teachers College, Columbia University.
  • Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., … Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27–35. https://doi.org/10.1016/j.iheduc.2013.08.001
  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco: Jossey-Bass.
  • Plous, S. (2000). Tips on creating and maintaining an educational World Wide Web site. Teaching of Psychology 27(1), 63– 70. https://doi.org/10.1207/S15328023TOP2701_13
  • Pollack, P. H., & Wilson, B. M. (2002). Evaluating the impact of internet teaching: Preliminary evidence from American national government classes. PS. Political Science and Politics, 35(3), 561–566.
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Shuey, S. (2002). Assessing Online Learning in Higher Education. Journal of Instruction Delivery Systems, 16(2), 13-18.
  • Soffer, T., & Cohen, A. (2019). Students’ engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378–389. https://doi.org/10.1111/jcal.12340
  • Soffer, T., & Nachmias, R. (2018). Effectiveness of learning in online academic courses compared with face‐to‐face courses in higher education. Journal of Computer Assisted Learning, 34, 534–543. https://doi.org/10.1111/jcal.12258
  • Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.
  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological methodology, 8, 84-136.
  • Wijekumar, K., Ferguson, L., & Wagoner, D. (2006). Problems with assessment validity and reliability in wed-based distance learning environments and solutions. Journal of Educational Multimedia and Hypermedia, 15(2), 199–215.
  • Wu, Y. (2016). Factors impacting students' online learning experience in a learner‐centred course. Journal of Computer Assisted Learning, 32(5), 416–429. https://doi.org/10.1111/jcal.12142
  • Wu, Z. (2020). How a top Chinese university is responding to coronavirus. https://www.weforum.org/agenda/2020/03/coronavirus-china-the-challenges-of- online-learning-for-universities/
  • Xie, K., Durrington, V., & Yen, L. L. (2011). Relationship between students' motivation and their participation in asynchronous online discussions. Journal of Online Learning and Teaching, 7(1), 17-29.
  • Zhong, R. (2020, March 17). The coronavirus exposes education’s digital divide. https://www.nytimes.com/2020/03/17/technology/china-schools-coronavirus.html

ÇEVRİMİÇİ ÖĞRENCİ BAĞLILIK ÖLÇEĞİNİN TÜRKÇE’YE UYARLANMASI: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Year 2022, Volume: 12 Issue: 1, 41 - 56, 14.01.2022
https://doi.org/10.17943/etku.936669

Abstract

Çevrimiçi öğrenme ortamlarının etkililiğini belirleyen en önemli faktörlerden biri bağlılıktır. Bağlılık; öğrenci başarısını, motivasyon ve memnuniyetini arttırırken, soyutlanma hissini azaltmaktadır. Bu çalışmanın amacı Dixson (2010) tarafından geliştirilen çevrimiçi öğrenci bağlılık ölçeğinin (Online Student Engagement Scale) Türkçe uyarlamasını yapmak, bununla birlikte geçerli ve güvenilir bir ölçme aracı ortaya çıkarmaktır. Çalışma ilk olarak özgün ölçeğin Türkçeye çevrilmesiyle başlamış, dilsel eşdeğerlik ve ön pilot uygulamasıyla ölçeğe son hali verilmiştir. Araştırmanın katılımcılarını üniversitede okuyan 254 öğrenci oluşturmaktadır. Bu araştırmada, yapı geçerliliğini göstermek için doğrulayıcı faktör analizine başvurulmuştur. Türkçe ölçekte 19 maddenin tümü ve 4 faktörlü (beceriler, duygu, katılım ve performans) yapı korunmuş ve yapının özgün ölçekle uyumlu olduğu gözlenmiştir. Uyum ölçümlerinin kabul edilebilir uyumun üstünde iyi uyum gösterdiği görülmektedir. Cronbach alfa değerleri α=,77 ile α=,87 arasında değişmektedir. Elde edilen değerlerle ölçeğin yapı geçerliliğinin sağlandığı görülmüştür. Özellikle çevrimiçi öğrenme uygulamalarının yoğun bir şekilde kullanıldığı günümüzde bu çalışma, öğrencilerin çevrimiçi öğrenmeye bağlılıklarının tespit edilmesinde önemli bir ölçek sunmaktadır. Buradan yola çıkarak da çevrimiçi öğrenmede en etkili unsurlardan biri olan çevrimiçi bağlılığı artırmak adına öğretim tasarımcıları ve öğretmenler öğrencileri çevrimiçi öğrenme konusunda teşvik eden, yönlendiren stratejiler geliştirebilirler.

References

  • Banna, J., Lin, M. F. G., Stewart, M., & Fialkowski, M. K. (2015). Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course. Journal of Online Learning and Teaching, 11(2), 249.
  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International journal of Research in Marketing, 13(2), 139-161.
  • Bowen, S. (2005). Engaged learning: Are we all on the same page. Peer Review, 7(2), 4–7.
  • Britt, R. (2006). Online education: A survey of faculty and students. Radiologic Technology, 77(3), 183- 190.
  • Brooks, L. (2003). How the attitudes of instructors, students, course administrators, and course designers affects the quality of an online learning environment? Online Journal of Distance Learning Administration, 6(4).
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K.A., & Long, J.S. (Eds.), Testing structural equation models (pp. 136-162). Sage.
  • Cohen, A. (2017). Analysis of student activity in web‐supported courses as a tool for predicting dropout. Educational Technology Research and Development, 65(5), 1285–1304. https://doi.org/10.1007/ s11423‐017‐9524‐3
  • Collis, B., De Boer, W., & Slotman, K. (2001). Feedback for web‐based assignments. Journal of Computer Assisted Learning, 17, 306-313. https://doi.org/10.1046/j.0266-4909.2001.00185.x.
  • Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications.Journal of Applied psychology, 78(1), 98-104.
  • Crawford‐Ferre, H. G., & Wiest, L. R. (2012). Effective online instruction in higher education. Quarterly Review of Distance Education, 13(1), 11.
  • Davis BG (1993). Tools of Teaching. Jossey-Bass.
  • Dixson, M. D. (2010). Creating effective student engagement in online courses: What do students find engaging? Journal of Scholarship of Teaching and Learning, 10(2), 1–13.
  • Dixson, M. D. (2015). Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learning, 19(4), n4.
  • Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., & Thompson, G. (2012). Can online courses deliver in‐class results? A comparison of student performance and satisfaction in an online versus a face‐to‐face introductory sociology course. Teaching Sociology, 40(4), 312–331. https:// doi.org/10.1177/0092055X12446624
  • Dunne, E., & Owen, D. (eds) (2013). The Student Engagement Handbook: Practice in Higher Education. Emerald Group.
  • Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices 1, 2 (online). http//www.testpublishers.org.journal.html
  • Hampton, D., & Pearce, P. F. (2016). Student engagement in online nursing courses. Nurse Educator, 41(6), 294-298. https://doi.org/10.1097/NNE.0000000000000275
  • Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. The Journal of Educational Research, 98(3), 184–192. https://doi.org/10.3200/JOER.98.3.184-192
  • Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320-341. https://doi.org/10.1111/ bjet.12235
  • Hoskins, S. L., & van Hooff, J. C. (2005). Motivation and ability: which students use online learning and what influence does it have on their achievement? British Journal of Educational Technology, 36(2), 177-192. doi:10.1111/j.1467-8535.2005.00451.x
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  • Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford, New York.
  • Lim, J. M. (2016). Predicting successful completion using student delay indicators in undergraduate self‐paced online courses. Distance Education, 37(3), 317–332. https://doi.org/10.1080/01587919. 2016.1233050
  • Meyers, L., S., Gamst, G., & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation. Sage.
  • Limperos, A. M., Buckner, M. M., Kaufmann, R., & Frisby, B. N. (2015). Online teaching and technological affordances: An experimental investigation into the impact of modality and clarity on perceived and actual learning. Computers & Education, 83, 1–9. https://doi.org/10.1016/j.compedu.2014.12.015
  • Narciss, S., Proske, A., & Körndle, H. (2007). Promoting self-regulated learning in web-based learning environments. Computers in Human Behavior, 23 (2007), 1126-1144. https://doi.org/10.1016/j.chb.2006.10.006
  • Newman, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). Taking students seriously. Student engagement and achievement in American secondary schools. New York: Teachers College, Columbia University.
  • Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., … Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27–35. https://doi.org/10.1016/j.iheduc.2013.08.001
  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco: Jossey-Bass.
  • Plous, S. (2000). Tips on creating and maintaining an educational World Wide Web site. Teaching of Psychology 27(1), 63– 70. https://doi.org/10.1207/S15328023TOP2701_13
  • Pollack, P. H., & Wilson, B. M. (2002). Evaluating the impact of internet teaching: Preliminary evidence from American national government classes. PS. Political Science and Politics, 35(3), 561–566.
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Shuey, S. (2002). Assessing Online Learning in Higher Education. Journal of Instruction Delivery Systems, 16(2), 13-18.
  • Soffer, T., & Cohen, A. (2019). Students’ engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378–389. https://doi.org/10.1111/jcal.12340
  • Soffer, T., & Nachmias, R. (2018). Effectiveness of learning in online academic courses compared with face‐to‐face courses in higher education. Journal of Computer Assisted Learning, 34, 534–543. https://doi.org/10.1111/jcal.12258
  • Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.
  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological methodology, 8, 84-136.
  • Wijekumar, K., Ferguson, L., & Wagoner, D. (2006). Problems with assessment validity and reliability in wed-based distance learning environments and solutions. Journal of Educational Multimedia and Hypermedia, 15(2), 199–215.
  • Wu, Y. (2016). Factors impacting students' online learning experience in a learner‐centred course. Journal of Computer Assisted Learning, 32(5), 416–429. https://doi.org/10.1111/jcal.12142
  • Wu, Z. (2020). How a top Chinese university is responding to coronavirus. https://www.weforum.org/agenda/2020/03/coronavirus-china-the-challenges-of- online-learning-for-universities/
  • Xie, K., Durrington, V., & Yen, L. L. (2011). Relationship between students' motivation and their participation in asynchronous online discussions. Journal of Online Learning and Teaching, 7(1), 17-29.
  • Zhong, R. (2020, March 17). The coronavirus exposes education’s digital divide. https://www.nytimes.com/2020/03/17/technology/china-schools-coronavirus.html
There are 42 citations in total.

Details

Primary Language Turkish
Subjects Other Fields of Education
Journal Section Articles
Authors

Elif Polat 0000-0002-6086-9002

Sinan Hopcan 0000-0001-8911-3463

Tuğba Kamalı Arslantaş 0000-0002-6135-641X

Publication Date January 14, 2022
Published in Issue Year 2022 Volume: 12 Issue: 1

Cite

APA Polat, E., Hopcan, S., & Kamalı Arslantaş, T. (2022). ÇEVRİMİÇİ ÖĞRENCİ BAĞLILIK ÖLÇEĞİNİN TÜRKÇE’YE UYARLANMASI: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI. Eğitim Teknolojisi Kuram Ve Uygulama, 12(1), 41-56. https://doi.org/10.17943/etku.936669