A New Perspective to University Students' Online Learning Self-Efficacy: A Structural Equation Modeling

Bu araştırmanın amacı üniversite öğrencilerinin çevrimiçi öğrenme özyeterlikleri ile akademik özyeterlikleri arasındaki ilişkiyi yapısal eşitlik modellemesi kullanarak incelemek ve çevrimiçi öğrenme özyeterliği için istatistiksel olarak anlamlı bir model oluşturabilmektir. Araştırmada, nicel araştırma yöntemlerinden kesitsel tarama modeli kullanılmıştır. Araştırmanın örneklemi 2022-2023 eğitim öğretim yılında, eğitim fakültesinin çeşitli programlarında ve farklı sınıf düzeylerinde öğrenim gören 322 üniversite öğrencisi oluşmaktadır. Araştırmada veri toplama aracı olarak; demografik bilgi formu, akademik özyeterlik ölçeği, çevrimiçi öğrenme ortamlarında öğrenci bağlılık ölçeği, çevrimiçi öğrenme sistemleri kabul ölçeği ve çevrimiçi öğrenmeye yönelik öz-yeterlik ölçeği kullanılmıştır. Araştırmadan elde edilen sonuçlara göre akademik özyeterlik, çevrimiçi öğrenme ortamlarında öğrenci bağlılığı ve çevrimiçi öğrenme sistemleri kabulü üzerinde pozitif ve anlamlı bir etkiye sahipken çevrimiçi öğrenme ortamlarında öğrenci bağlılığı ve çevrimiçi öğrenme sistemleri kabulü ise çevrimiçi öğrenme özyeterliği üzerinde pozitif ve anlamlı bir etkiye sahiptir. Ayrıca akademik özyeterlik, çevrimiçi öğrenme ortamlarında öğrenci bağlılığı için çevrimiçi öğrenme ortamlarında öğrenci bağlılığı ise çevrimiçi öğrenme özyeterliği için daha güçlü bir yordayıcıdır.

Anahtar Kelimeler:

çevrimiçi öğrenme özyeterliği, akademik özyeterlik, çevrimiçi öğrenme ortamlarında öğrenci bağlılığı, çevrimiçi öğrenme sistemleri kabulü, yapısal eşitlik modeli

A New Perspective to University Students' Online Learning Self-Efficacy: A Structural Equation Modeling

The aim of this paper is to examine the relationship between university students' online learning self-efficacy and academic self-efficacy using structural equation modeling and to create a statistically significant model for online learning self-efficacy. In the study, the cross-sectional survey model, one of the quantitative research methods, was used. The sample of the study consists of 322 university students studying in various programs and at different grade levels in the faculty of education in the 2022-2023 academic year. Demographic information form, academic self-efficacy scale, student’s engagement scale in online learning environments, online learning systems acceptance scale and online learning self-efficacy scale were used as data collection tools. The results obtained from the study indicated that academic self-efficacy had a positive and significant effect on student’s engagement in online learning environments and online learning systems acceptance, while student’s engagement in online learning environments and online learning systems acceptance had a positive and significant effect on online learning self-efficacy. In addition, academic self-efficacy was a stronger predictor for student’s engagement in online learning environments, and student’s engagement in online learning environments was a stronger predictor for online learning self-efficacy.

Keywords:

online learning self-efficacy, academic self-efficacy, student’s engagement in online learning environments, online learning systems acceptance, structural equation model,

PDF

___

Alpaslan, M. M., & Ulubey, Ö. (2021). Examining the relations emotions, motivation, classroom engagement and achievement in mathematics. International Journal of Research in Education and Science 7(4), 1042-1057. https://doi.org/10.46328/ijres.1953
Allen, M., Bourhis, J., Burrell, N., & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: a meta-analysis. American Journal of Distance Education, 16(2), 83-97. https://doi.org/10.1207/S15389286AJDE1602_3
Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research (CIER), 9(1), 45-52. https://doi.org/10.19030/cier.v9i1.9549
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
Bakır, E. (2022). Modeling students' achievement emotions, emotion regulation strategies and engagement in online learning environments [Unpublished Doctoral Dissertation]. Hacettepe University.
Bandura, A. (1994). Self-efficacy. Ramachaudran, V. S. (Ed.). In Encyclopedia of human behavior (pp.71-81). Academic Press.
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company.
Bandura, A. (2010). Self-Efficacy. The Corsini Encyclopedia of Psychology. https://doi.org/10.1002/9780470479216.corpsy0836
Calaguas, N. P., & Consunji, P. M. P. (2022). A structural equation model predicting adults’ online learning self-efficacy. Education and Information Technologies, 27, 6233-6249. https://doi.org/10.1007/s10639-021-10871-y
Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages. Research in Higher Education, 47(1), 1-32. https://doi.org/10.1007/s11162-005-8150-9
Chemers, M. M., Hu, L., & Garcia, B. F. (2001). Academic self-efficacy and the first-year college student performance and adjustment. Journal of Educational Psychology, 93(1), 55-64. https://doi.org/10.1037/0022-0663.93.1.55
Chang, D., & Cheng Chien, W. (2015, April). Determining the relationship between academic self-efficacy and student engagement by meta-analysis. In 2015 International Conference on Education Reform and Modern Management (pp. 142-145). Atlantis Press. https://doi.org/10.2991/ermm-15.2015.37
Chyung, S. Y., Moll, A. J., & Berg, S. A. (2010). The role of intrinsic goal orientation, self-efficacy, and e-learning practice in engineering education. Journal of Effective Teaching, 10(1), 22-37. https://files.eric.ed.gov/fulltext/EJ1092160.pdf
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2012). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları (2. baskı). Pegem Akademi.
Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Unpublished Doctoral Dissertation]. Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318-340. https://doi.org/10.2307/249008
Deng, W., Lei, W., Guo, X., Li, X., Ge, W., & Hu, W. (2022). Effects of regulatory focus on online learning engagement of high school students: The mediating role of self‐efficacy and academic emotions. Journal of Computer Assisted Learning, 38(3), 707-718. https://doi.org/10.1111/jcal.1264212
Ergün, E., & Koçak Usluel, Y. (2015). The Turkish adaptation of student’s engagements scale in online learning environment: A study of validity and reliability. Educational Technology Theory and Practice, 5(1), 20-33. https://toad.halileksi.net/sites/default/files/pdf/cevrimici-ogrenmeye-yonelik-oz-yeterlikolcegi-toad.pdf
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191. https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html
Fraenkel, J. R., & Wallen, N. E. (2009). How to design and evaluate research in education (7th ed.). McGraw Hill Higher Education.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
Freund, R., & Littell, R. (2000). SAS system for regression. John Wiley & Sons.
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference: 11.0 update (4th ed.). Allyn & Bacon. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (2005). Multivariate Data Analysis (6th ed.). Pearson Education.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433. https://doi.org/10.1007/s11747-011- 0261-6
Homoki, E., Nyitrai, T., & Makó, Z. (2023). Online educational experiences in some majors of Eszterházy Károly University. Acta Educationis Generalis, 13(2), 82-95. https://doi.org/10.2478/atd-2023-0015
Ilgaz, H. (2008). The contribution of technology acceptance and community feeling to learner satısfaction in distance education. [Unpublished Master's Dissertation]. Hacettepe University. https://toad.halileksi.net/sites/default/files/pdf/cevrimici-ogrenme-sistemleri-kabulolcegi-toad.pdf
Jerusalem, M., & Schwarzer, R. (1981). Fragebogen zur Erfassung von" Selbstwirksamkeit. Skalen zur Befindlichkeit und Persoenlichkeit. In R. Schwarzer (Hrsg.). (Forschungsbericht No. 5). Berlin: Freie Universitaet, Institut fuer Psychologie.
Jöreskog, K.G., & Sörbom D. (1988). LISREL 7: A guide to the program and applications. SPSS Inc.
Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27(2), 119-132. https://doi.org/10.1111/j.1365-2729.2010.00387.x
Kalaycı, Ş. (2010). SPSS uygulamalı çok değişkenli istatistik teknikleri (5. baskı). Asil Yayın Dağıtım.
Khine, M. S. (Ed.) (2013). Application of structural equation modeling in educational research and practice. Sense Publishers.
Kline, R.B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Koca, M., & Koçak Usluel, Y. K. (2007). Teachers’ acceptance of and intention to use the information and communication technologies. Educational Science & Practice, 6 (11), 3-18. https://web.s.ebscohost.com/ehost/pdfviewer/pdfviewer? vid=0&sid=df302db8-e36f-4afd-bd00-af5400e0e970%40redis
Krause, K. L., & Coates, H. (2008). Students’ engagement in first‐year university. Assessment & Evaluation in Higher Education, 33(5), 493-505. https://doi.org/10.1080/02602930701698892 Lai, H. J. (2011). The influence of adult learners' self-directed learning readiness and network literacy on online learning effectiveness: A study of civil servants in Taiwan. Journal of Educational Technology & Society, 14(2), 98-106. https://www.jstor.org/stable/jeductechsoci.14.2.98
Law, K. M., Lee, V. C., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218-228. https://doi.org/10.1016/j.compedu.2010.01.007
Lee, J. W., & Mendlinger, S. (2011). Perceived self-efficacy and its effect on online learning acceptance and student satisfaction. Journal of Service Science and Management, 4(3), 243-252. https://doi.org/10.4236/jssm.2011.43029
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. https://doi.org/10.1016/S0378-7206(01)00143-4
Lim, K., Kang, M., & Park, S. Y. (2016). Structural relationships of environments, individuals, and learning outcomes in Korean online university settings. International Review of Research in Open and Distributed Learning, 17(4), 315-330. https://doi.org/10.19173/irrodl.v17i4.2500
Lu, Y., Hong, X., & Xiao, L. (2022). Toward high-quality adult online learning: A systematic review of empirical studies. Sustainability, 14(4), 2257. https://doi.org/10.3390/su14042257
Margolis, H., & McCabe, P. P. (2004). Self-efficacy: A key to improving the motivation of struggling learners. The Clearing House, 77(6), 241-249. https://doi.org/10.1080/10459880309603362
Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. https://repository.alt.ac.uk/629/1/US_DepEdu_Final_report_2009.pdf
Ministry of National Education (MoNE) (2020, July 20). General competencies of the teaching profession. https://oygm.meb.gov.tr/meb_iys_dosyalar/2017_12/11115355_YYRETMENLYK_MESLEYY_GENEL_YETERLYKLERY.pdf
Nielsen, T., Dammeyer, J., Vang, M. L., & Makransky, G. (2018). Gender fairness in self-efficacy? A Rasch-based validity study of the General Academic Self-efficacy scale (GASE). Scandinavian Journal of Educational Research, 62(5), 664-681. https://doi.org/10.1080/00313831.2017.1306796
O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & quantity, 41(5), 673-690. https://doi.org/10.1007/s11135-006-9018-6
Oriol-Granado, X., Mendoza-Lira, M., Covarrubias-Apablaza, C. G., & Molina-Lopez, V. M. (2017). Positive emotions, autonomy support and academic performance of university students: The mediating role of academic engagement and self-efficacy. Revista de Psicodidactica, 22(1), 45-53. https://doi.org/10.1387/RevPsicodidact.14280
Pearl, J. (2012). The causal foundations of structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 68-91). The Guilford Press.
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(09), 64-86 https://onliejour.journals.publicknowledgeproject.org/index.php/i-jet/article/viewFile/8351/5150
Pintrich, P. R., & de Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
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, 91-97. https://doi.org/10.1016/j.iheduc.2016.01.001
Rodríguez-Muñoz, A., Antino, M., Ruiz-Zorrilla, P., & Ortega, E. (2021). Positive emotions, engagement, and objective academic performance: A weekly diary study. Learning and Individual Differences, 92, 102087. https://doi.org/10.1016/j.lindif.2021.102087
Schunk, D. H. (1985). Self-efficacy and classroom learning. Psychology in the Schools, 22(2), 208-223. https://doi.org/10.1002/1520-6807(198504)22:2<208::AID-PITS2310220215>3.0.CO;2-7
Schunk, D. H., & Pajares, F. (2009). Self-efficacy theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 35-53). Routledge.
Shevlin, M., & Miles, J. N. (1998). Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Personality and Individual differences, 25(1), 85-90. https://doi.org/10.1016/S0191-8869(98)00055-5
Simándi, S. (2017). Study circles in online learning environment in the spirit of learning-centered approach. Acta Educationis Generalis,7(2), 96-104. https://doi.org/10.1515/atd-2017-0017
Solberg, V. S., O'Brien, K., Villareal, P., Kennel, R., & Davis, B. (1993). Self-efficacy and Hispanic college students: Validation of the college self-efficacy instrument. Hispanic Journal of Behavioral Sciences, 15(1), 80-95. https://doi.org/10.1177/07399863930151004
Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effect of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85(4), 571-581. https://doi.org/10.1037/0022-0663.85.4.571
Sun, J. C.Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and selfregulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204. https://doi.org/10.1111/j.1467-8535.2010.01157.x
Şahin, I., & Shelley, M. (2008). Considering students’ perceptions: The distance education student satisfaction model. Educational Technology & Society, 11(3), 216-223. https://www.jstor.org/stable/jeductechsoci.11.3.216
Tabachnick, B. G., & Fidell, L.S. (2013). Using multivariate statistics (6th ed.). Pearson.
Tang, X., Wang, M. T., Parada, F., & Salmela-Aro, K. (2021). Putting the goal back into grit: Academic goal commitment, grit, and academic achievement. Journal of Youth and Adolescence, 50(3), 470-484. https://doi.org/10.1007/s10964-020-01348-1
Topal, M. (2020). The effect of online learning enhanced with gamification on student’s engagement to online learning environment, academic achievement and learning motivation. [Unpublished Doctoral Dissertation]. Sakarya University.
Tsai, C. C., Chuang, S. C., Liang, J. C., & Tsai, M. J. (2011). Self-efficacy in internet-based learning environments: A literature review. Journal of Educational Technology & Society, 14(4), 222-240. https://www.jstor.org/stable/pdf/jeductechsoci.14.4.222.Pdf
Yılmaz, M., Gürçay, D., & Ekici, G. (2007). Adaptation of the academic self-efficacy scale to Turkish. Hacettepe University Journal of Education, 33, 253-259. http://efdergi.hacettepe.edu.tr/shw_artcl-1047.html
Zimmerman, B. J. (1995). Self-efficacy and educational development. Self-efficacy in changing societies, 1(1), 202-231.
Zimmerman, B. J. (1995). Self-efficacy and educational development. Bandura, A. (Ed.), In Self-Efficacy in changing societies (pp.202-231). Cambridge University Press.
Zimmerman, W. A., & Kulikowich, J. M. (2016). Online learning self-efficacy in students with and without online learning experience. American Journal of Distance Education, 30(3), 180-191. https://doi.org/10.1080/08923647.2016.1193801