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Çevrimiçi Öğrenme Öz-Yeterliği: Öğrencilerin Öğrenmesini Etkileyen Faktörlerin Araştırılması

Year 2023, Issue: 58, 2814 - 2829, 27.12.2023
https://doi.org/10.53444/deubefd.1326894

Abstract

Salgın süreci dünyadaki tüm insanlara önemli zorluklar yaratmış ve eğitim kurumları da bu beklenmedik durumdan doğrudan etkilenmiştir. Bu süreç ayrıca, çevrimiçi eğitimin ve teknolojik araçların eğitimsel faaliyetlerde kullanımının önemini gözler önüne sermiştir. Bu bağlamda, bu çalışmanın amacı öncelikle, öğrencilerin çevrimiçi öz-yeterlik seviyelerini belirleyip bunu farklı değişkenler açısından değerlendirmek ve sonrasında, öğrencilerin çevrimiçi özyeterliklerini etkileyen faktörlere yönelik görüşlerini ortaya çıkarmaktır. Çalışmanın nicel bulgularına göre, katılımcı beyanına dayalı öz-yeterlik seviyeleri genel olarak oldukça yüksektir. Öğrencilerin öz-yeterlik seviyeleri ile cinsiyetleri arasında anlamlı bir fark bulunmamasına rağmen, yaş ve eğitim seviyesi istatistiksel olarak anlamlı değişkenlerdir. Nitel bulgular değerlendirildiğinde ise, katılımcı öğrenciler çevrimiçi özyeterliklerini etkileyen faktörler üzerinde görüş bildirirken olumlu ve olumsuz faktörlere değinmişlerdir. Olumlu olan faktörler için öğretmenlerinden ve ilgili kaynaklardan aldıkları destek ve derslere katılımın kolaylığından bahsetmişken, olumsuz olarak da motivasyon sorunları ile teknolojik problemleri özyeterliklerini etkileyen faktörler olarak açıklamışlardır.

References

  • Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2020.1813180
  • Aldhahi, M. I., Baattaiah, B. A., & Alqahtani, A. S. (2021). Predictors of electronic learning self-efficacy: A cross-sectional study in Saudi Arabian universities. Frontiers in Education, 6, 614333. https://doi.org/10.3389/feduc.2021.614333
  • Ali, W. (2020). Online learning and remote learning in higher education institutions: A necessity in light of COVID-19 pandemic. Higher Education, 10(3). https://doi.org/10.5539/hes.v10n3p16.
  • Alivernini, F., & Lucidi, F. (2011). Relationship between social context, self-efficacy, motivation, academic achievement, and intention to drop out of high school: A longitudinal study. The Journal of Educational Research, 104(4), 241–252. https://doi.org/10.1080/00220671003728062
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company
  • Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245-281. https://doi.org/10.3102/00346543065003245
  • Cho, M. H., & Jonassen, D. (2009). Development of the human interaction dimension of the Self-Regulated Learning Questionnaire in asynchronous online learning environments. Educational Psychology, 29, 117–138.
  • Camas, L., Valero, A., & Vendrell, M. (2021). The teacher-student relationship in the use of social network sites for educational purposes: a systematic review. Journal of New Approaches in Educational Research, 10(1), 137-156. https://doi.org/10.7821/naer.2021.1.591
  • Carter Jr, R. A., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: Strategies for remote learning strategies. Information and Learning Sciences, 121(5/6), 321-329. https://doi.org/10.1108/ILS04-2020-0114
  • Chu, L. (2003). The effects of web page design instruction on computer self-efficacy of preservice teachers and correlates. Journal of Educational Computing Research, 28(2), 127-142. https://doi.org/10.2190%2FK79G-2PYY-VVU6-X988
  • Chu, R. J., & Chu, A. Z. (2010). Multi-level analysis of peer support, internet self-efficacy and e-learning outcomes—The contextual effects of collectivism and group potency. Computers & Education, 55(1), 145–154. https://doi.org/10.1016/j.compedu.2009.12.011
  • Chuyung, S. Y. (2007). Age and gender differences in online behaviour, self-efficacy and academic performance. The Quarterly Review of Distance Education, 8(3), 213-222.
  • Crawford, J., Butler-Henderson, K., Rudolph, J., & Glowatz, M. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Teaching and Learning (JALT), 3(1). https://doi.org/10.37074/jalt.2020.3.1.7.
  • Creswell, J. W. (2014). Educational research: Planning, conducting and evaluating quantitative and qualitative research. Pearson Education Limited.
  • Dhawan, S. (2020). Online learning: A panacea in the time of Covid-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22. https://doi.org/10.1177%2F0047239520934018
  • 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. https://doi.org/10.1080/01587919.2011.565496
  • Gerhardt, M. W., & Brown, K. G. (2006). Individual differences in self-efficacy development: The effects of goal orientation and affectivity. Learning and Individual Differences, 16, 43-59.
  • Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. The Academy of Management Review, 17(2), 183–211. https://doi.org/10.2307/258770
  • Gredler, M. (2007). Learning and instruction: Theory into practice. Pearson.
  • Gregori, P., Mart.nez, V., & Moyano-Fern.ndez, J. J. (2018). Basic actions to reduce dropout rates in distance learning. Evaluation and Program Planning, 66, 48–52. https://doi.org/10.1016/j.evalprog plan.2017.10.004
  • Hodges, C. (2008). Self-efficacy, motivational email, and achievement in an asynchronous math course. Journal of Computers in Mathematics and Science Teaching, 27(3), 265-285.
  • Huang, R. H., Liu, D. J., Guo, J., Yang, J. F., Zhao, J. H., & Wei, X. F. (2020). Guidance on flexible learning during campus closures: Ensuring course quality of higher education in COVID-19 outbreak. Smart Learning Institute of Beijing Normal University.
  • 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.
  • Johnson, G. M., & Davies, S. M. (2014). Self-regulated learning in digital environments: Theory, research, praxis. British Journal of Research, 1(2), 1-14.
  • Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research & Development, 59(5), 593–618. https://doi.org/10.1007/s11423-010-9177-y
  • Li, H. (2007). Efficacy, computer self-efficacy, and satisfaction with e-learning courses. (Unpublished doctoral dissertation). University of South Dakota.
  • 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
  • Maathuis Smith, S., Wellington, S., Cossham, A., Fields, A., Irvine, J., Welland, S., & Innes, M. (2011). Obtaining high retention and completion rates in a New Zealand ODL environment: A case study of strategies employed by Information and Library Studies Faculty at the Open Polytechnic. Journal of Open, Flexible, and Distance Learning, 15(1), 31-45.
  • Mackey, A., & Gass, S. (2005). Second language research: Methodology and design. Routledge.
  • Malureanu, A., Panisoara, G., & Lazar, I. (2021). The relationship between self-confidence, self-efficacy, grit, usefulness, and ease of use of eLearning platforms in corporate training during the COVID-19 pandemic. Sustainability, 13(12), 6633. http://dx.doi.org/10.3390/su13126633
  • Mason, J. (2002). Qualitative researching, 2nd ed. SAGE Publications.
  • Mathew, V., & Chung, E. (2021). University students’ perspectives on open and distance learning (ODL) implementation amidst COVID-19. Asian Journal of University Education, 16(4), 152-160. https://doi.org/DOI 10.24191/ajue.v16i4.11964.
  • Miles, M.B., & Huberman, A.M. (1994). Qualitative data analysis: An expanded sourcebook. SAGE Publications.
  • Moore, M. G., & Kearsley, G. (2005). Distance education: A systems view. Thomson Wadsworth.
  • Özdamar, K. (2004). Paket programlar ile istatistiksel veri analizi. Kaan.
  • Özüdoğru, G. (2022). The effect of distance education on self-efficacy towards online technologies and motivation for online learning. Journal of Learning and Teaching in the Digital Age, 7(1), 108-115. https://doi.org/10.53850/joltida.1003915
  • Pallant, J. (2001). SPSS survival manual. Open University Press.
  • Parkes, M., Stein, S., & Reading, C. (2015). Student preparedness for university e-learning environments. Internet and Higher Education, 25, 1–10. https://doi.org/10.1016/j.iheduc.2014.10.002
  • Patton, M. Q. (2002). Qualitative research and evaluation methods. Sage Publications, Inc.
  • Ramsin, A., & Mayall. H. J. (2019). Assessing ESL learners’ online learning self-efficacy in Thailand: Are they ready? Journal of Information Technology Education: Research, 18, 467-479. https://doi.org/10.28945/4452
  • Shen, D., Cho, M-H., Tsai, C L., Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Ewitducation, 19, 10-17. http://dx.doi.org/10.1016/j.iheduc.2013.04.001.
  • Sim, S. P. L., Sim, H. P. K., & Quah, C. S. (2021). Online learning: A post Covid-19 alternative pedagogy for university students. Asian Journal of University Education, 16(4), 137-151https://doi.org/10.24191/ajue.v16i4.11963
  • Singh, G., & Quraishi, S. (2021). COVID-19 lockdown: Challenges faced by Indian students. Psychological Studies, 66, 303-307.
  • Taipjutorus, W., Hansen, S., & Brown, M. (2012). Improving Learners’ Self-efficacy in a learner-controlled online learning environment: a correlational study. M. Brown, M. Harnett & T. Stewart (Ed.). Future Challenges, sustainable futures: Proceedings ASCILITE Wellington. 907-911.
  • Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323.
  • Wei, H. C., & Chou, C. (2020) Onlinelearning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48-69. https://doi.org/ 10.1080/01587919.2020.1724768
  • Yavuzalp, N., & Bahçivan, E. (2020). The online learning self-efficacy scale: Its adaptation into Turkish and interpretation according to various variables. TOJDE, 21(1), 31-44.
  • Yukselturk, E., Ozekes, S., & Türel, Y. (2014). Predicting dropout student: An application of data mining methods in an online education program. European Journal of Open Distance E-Learn, 17(1), 118–133. https://doi.org/10.2478/eurodl-2014-0008
  • Wu, D., & Hiltz, S. R. (2004). Predicting learning from asynchronous online discussions. Journal of Asynchronous Learning Networks, 8(2), 139- 152.
  • 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.or g/10.1080/08923647.2016.1193801

Online Learning Self-Efficacy: Investigation of the Factors Affecting Student Learning

Year 2023, Issue: 58, 2814 - 2829, 27.12.2023
https://doi.org/10.53444/deubefd.1326894

Abstract

The process of pandemic brought about important challenges to all the people in the world and educational institutions have also been affected directly by this unexpected situation. It has also revealed the significance of online education and the use of technological tools for educational purposes. In this context, this study aims to investigate the learners’ online learning self-efficacy within the scope of demographic variables and it also attempted to identify the perceptions of students related to tha factors that influenced their online learning self-efficacy. According to the quantitative findings, the participants’ self-reported online self-efficacy levels were found to be quite high. Even though no statistically significant relationship was found between learners’ online self-efficacy and gender, age and school level were found to be statistically significant variables.When the qualitative findings were taken into account, itt was found out during interviews that, learners mentioned some negative and positive factors affecting their online learning self-efficacy. Support they obtained through their course instructors and resources presented to them were revealed as enabling factors whereas technical issues and motivation problems were listed as disabling factors.

References

  • Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2020.1813180
  • Aldhahi, M. I., Baattaiah, B. A., & Alqahtani, A. S. (2021). Predictors of electronic learning self-efficacy: A cross-sectional study in Saudi Arabian universities. Frontiers in Education, 6, 614333. https://doi.org/10.3389/feduc.2021.614333
  • Ali, W. (2020). Online learning and remote learning in higher education institutions: A necessity in light of COVID-19 pandemic. Higher Education, 10(3). https://doi.org/10.5539/hes.v10n3p16.
  • Alivernini, F., & Lucidi, F. (2011). Relationship between social context, self-efficacy, motivation, academic achievement, and intention to drop out of high school: A longitudinal study. The Journal of Educational Research, 104(4), 241–252. https://doi.org/10.1080/00220671003728062
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company
  • Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245-281. https://doi.org/10.3102/00346543065003245
  • Cho, M. H., & Jonassen, D. (2009). Development of the human interaction dimension of the Self-Regulated Learning Questionnaire in asynchronous online learning environments. Educational Psychology, 29, 117–138.
  • Camas, L., Valero, A., & Vendrell, M. (2021). The teacher-student relationship in the use of social network sites for educational purposes: a systematic review. Journal of New Approaches in Educational Research, 10(1), 137-156. https://doi.org/10.7821/naer.2021.1.591
  • Carter Jr, R. A., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: Strategies for remote learning strategies. Information and Learning Sciences, 121(5/6), 321-329. https://doi.org/10.1108/ILS04-2020-0114
  • Chu, L. (2003). The effects of web page design instruction on computer self-efficacy of preservice teachers and correlates. Journal of Educational Computing Research, 28(2), 127-142. https://doi.org/10.2190%2FK79G-2PYY-VVU6-X988
  • Chu, R. J., & Chu, A. Z. (2010). Multi-level analysis of peer support, internet self-efficacy and e-learning outcomes—The contextual effects of collectivism and group potency. Computers & Education, 55(1), 145–154. https://doi.org/10.1016/j.compedu.2009.12.011
  • Chuyung, S. Y. (2007). Age and gender differences in online behaviour, self-efficacy and academic performance. The Quarterly Review of Distance Education, 8(3), 213-222.
  • Crawford, J., Butler-Henderson, K., Rudolph, J., & Glowatz, M. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Teaching and Learning (JALT), 3(1). https://doi.org/10.37074/jalt.2020.3.1.7.
  • Creswell, J. W. (2014). Educational research: Planning, conducting and evaluating quantitative and qualitative research. Pearson Education Limited.
  • Dhawan, S. (2020). Online learning: A panacea in the time of Covid-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22. https://doi.org/10.1177%2F0047239520934018
  • 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. https://doi.org/10.1080/01587919.2011.565496
  • Gerhardt, M. W., & Brown, K. G. (2006). Individual differences in self-efficacy development: The effects of goal orientation and affectivity. Learning and Individual Differences, 16, 43-59.
  • Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. The Academy of Management Review, 17(2), 183–211. https://doi.org/10.2307/258770
  • Gredler, M. (2007). Learning and instruction: Theory into practice. Pearson.
  • Gregori, P., Mart.nez, V., & Moyano-Fern.ndez, J. J. (2018). Basic actions to reduce dropout rates in distance learning. Evaluation and Program Planning, 66, 48–52. https://doi.org/10.1016/j.evalprog plan.2017.10.004
  • Hodges, C. (2008). Self-efficacy, motivational email, and achievement in an asynchronous math course. Journal of Computers in Mathematics and Science Teaching, 27(3), 265-285.
  • Huang, R. H., Liu, D. J., Guo, J., Yang, J. F., Zhao, J. H., & Wei, X. F. (2020). Guidance on flexible learning during campus closures: Ensuring course quality of higher education in COVID-19 outbreak. Smart Learning Institute of Beijing Normal University.
  • 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.
  • Johnson, G. M., & Davies, S. M. (2014). Self-regulated learning in digital environments: Theory, research, praxis. British Journal of Research, 1(2), 1-14.
  • Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research & Development, 59(5), 593–618. https://doi.org/10.1007/s11423-010-9177-y
  • Li, H. (2007). Efficacy, computer self-efficacy, and satisfaction with e-learning courses. (Unpublished doctoral dissertation). University of South Dakota.
  • 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
  • Maathuis Smith, S., Wellington, S., Cossham, A., Fields, A., Irvine, J., Welland, S., & Innes, M. (2011). Obtaining high retention and completion rates in a New Zealand ODL environment: A case study of strategies employed by Information and Library Studies Faculty at the Open Polytechnic. Journal of Open, Flexible, and Distance Learning, 15(1), 31-45.
  • Mackey, A., & Gass, S. (2005). Second language research: Methodology and design. Routledge.
  • Malureanu, A., Panisoara, G., & Lazar, I. (2021). The relationship between self-confidence, self-efficacy, grit, usefulness, and ease of use of eLearning platforms in corporate training during the COVID-19 pandemic. Sustainability, 13(12), 6633. http://dx.doi.org/10.3390/su13126633
  • Mason, J. (2002). Qualitative researching, 2nd ed. SAGE Publications.
  • Mathew, V., & Chung, E. (2021). University students’ perspectives on open and distance learning (ODL) implementation amidst COVID-19. Asian Journal of University Education, 16(4), 152-160. https://doi.org/DOI 10.24191/ajue.v16i4.11964.
  • Miles, M.B., & Huberman, A.M. (1994). Qualitative data analysis: An expanded sourcebook. SAGE Publications.
  • Moore, M. G., & Kearsley, G. (2005). Distance education: A systems view. Thomson Wadsworth.
  • Özdamar, K. (2004). Paket programlar ile istatistiksel veri analizi. Kaan.
  • Özüdoğru, G. (2022). The effect of distance education on self-efficacy towards online technologies and motivation for online learning. Journal of Learning and Teaching in the Digital Age, 7(1), 108-115. https://doi.org/10.53850/joltida.1003915
  • Pallant, J. (2001). SPSS survival manual. Open University Press.
  • Parkes, M., Stein, S., & Reading, C. (2015). Student preparedness for university e-learning environments. Internet and Higher Education, 25, 1–10. https://doi.org/10.1016/j.iheduc.2014.10.002
  • Patton, M. Q. (2002). Qualitative research and evaluation methods. Sage Publications, Inc.
  • Ramsin, A., & Mayall. H. J. (2019). Assessing ESL learners’ online learning self-efficacy in Thailand: Are they ready? Journal of Information Technology Education: Research, 18, 467-479. https://doi.org/10.28945/4452
  • Shen, D., Cho, M-H., Tsai, C L., Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Ewitducation, 19, 10-17. http://dx.doi.org/10.1016/j.iheduc.2013.04.001.
  • Sim, S. P. L., Sim, H. P. K., & Quah, C. S. (2021). Online learning: A post Covid-19 alternative pedagogy for university students. Asian Journal of University Education, 16(4), 137-151https://doi.org/10.24191/ajue.v16i4.11963
  • Singh, G., & Quraishi, S. (2021). COVID-19 lockdown: Challenges faced by Indian students. Psychological Studies, 66, 303-307.
  • Taipjutorus, W., Hansen, S., & Brown, M. (2012). Improving Learners’ Self-efficacy in a learner-controlled online learning environment: a correlational study. M. Brown, M. Harnett & T. Stewart (Ed.). Future Challenges, sustainable futures: Proceedings ASCILITE Wellington. 907-911.
  • Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323.
  • Wei, H. C., & Chou, C. (2020) Onlinelearning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48-69. https://doi.org/ 10.1080/01587919.2020.1724768
  • Yavuzalp, N., & Bahçivan, E. (2020). The online learning self-efficacy scale: Its adaptation into Turkish and interpretation according to various variables. TOJDE, 21(1), 31-44.
  • Yukselturk, E., Ozekes, S., & Türel, Y. (2014). Predicting dropout student: An application of data mining methods in an online education program. European Journal of Open Distance E-Learn, 17(1), 118–133. https://doi.org/10.2478/eurodl-2014-0008
  • Wu, D., & Hiltz, S. R. (2004). Predicting learning from asynchronous online discussions. Journal of Asynchronous Learning Networks, 8(2), 139- 152.
  • 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.or g/10.1080/08923647.2016.1193801
There are 51 citations in total.

Details

Primary Language English
Subjects Learning Theories
Journal Section Articles
Authors

Meral Şeker 0000-0001-7150-4239

Banu İnan Karagül 0000-0001-8672-1383

Publication Date December 27, 2023
Published in Issue Year 2023 Issue: 58

Cite

APA Şeker, M., & İnan Karagül, B. (2023). Online Learning Self-Efficacy: Investigation of the Factors Affecting Student Learning. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi(58), 2814-2829. https://doi.org/10.53444/deubefd.1326894