INFLUENCE OF ATTRIBUTES OF SELF-REGULATED LEARNING ON E-LEARNING IN SECONDARY SCHOOL STUDENTS

Authors

  • Vidhu Vijayan Research Scholar, School of Education, Sharda University
  • Dr. Harikrishnan M Assistant Professor, School of Education, Sharda University
  • Dr. Arti Koul Kachroo Dean, School of Education, Sharda University

DOI:

https://doi.org/10.29121/shodhkosh.v5.i5.2024.3337

Keywords:

Self Regulated Learning, Online Learning, Secondary School, Skills

Abstract [English]

Online learning, a solitary experience for the students? Are they studying in isolation?Is it satisfying?How can we design an approach that is more learner-centric?Students vary in their learning styles and characteristics.How can we make them more successful?
If students are made aware of their learning style and characteristics,can't they learneffectively through a virtual platform?If students are aware of SRL(Self-regulation learning) skills,they will be able to plan their learning correctly,which will lead to an increase in their academic performance.
The newness of online education has not allowed course content introduction effectively.Teachers need to guide the students to develop Self-regulation skills. Teachers must modify their lesson plan; a matching teaching style should be implemented after understanding the varied learning style.So this study addresses the influence of learner SRL skills on e-learning effectiveness among secondary school students.

References

Adnan, M. and Anwar, K., 2020. Online Learning amid the COVID-19 Pandemic: Students' Perspectives. Online Submission, 2(1), pp.45-51. DOI: https://doi.org/10.33902/JPSP.2020261309

Barman, A. and MuhamedYusoff, Y., 2014. Learning style awareness and academic performance of students. South‐East Asian Journal of Medical Education, 8(1), pp.47-51. DOI: https://doi.org/10.4038/seajme.v8i1.124

Biard, N., Cojean, S. and Jamet, E., 2018. Effects of segmentation and pacing on procedural learning by video. Computers in Human Behavior, 89, pp.411-417. DOI: https://doi.org/10.1016/j.chb.2017.12.002

Brodersen, R.M. and Melluzzo, D., 2017. Summary of Research on Online and Blended Learning Programs That Offer Differentiated Learning Options. REL 2017-228. Regional Educational Laboratory Central.

Colchester, K., Hagras, H., Alghazzawi, D. and Aldabbagh, G., 2017. A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), pp.47-64. DOI: https://doi.org/10.1515/jaiscr-2017-0004

Cooze, M. and Barbour, M.K., 2005. Learning styles: A focus upon e-learning practices and pedagogy and their implications for designing e-learning for secondary school students in Newfoundland and Labrador.

Daimary, P., 2020. E-Learning in Schools during COVID-19 Pandemic in Rural Areas. International Journal of Management, 11(10), pp. DOI: https://doi.org/10.34218/IJM.11.12.2020.040

Dhawan, S., 2020. Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), pp.5-22. DOI: https://doi.org/10.1177/0047239520934018

Drachsler, Hendrik & Kirschner, Paul. (2011). Learner Characteristics. Encyclopedia of the Sciences of Learning, pp.2, Springer, Editors: N. M. Seel DOI: https://doi.org/10.1007/978-1-4419-1428-6_347

Domingo, M.G. and Garganté, A.B., 2016. Exploring the use of educational technology in primary education: Teachers' perception of mobile technology learning impacts and applications' use in the classroom. Computers in Human Behavior, 56, pp.21-28. DOI: https://doi.org/10.1016/j.chb.2015.11.023

Hanus, M.D. and Fox, J., 2015. Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Computers &Education, 80, pp.152-161. DOI: https://doi.org/10.1016/j.compedu.2014.08.019

Holley, D. and Oliver, M., 2010. Student engagement and blended learning: Portraits of risk. Computers & Education, 54(3), pp.693-700. DOI: https://doi.org/10.1016/j.compedu.2009.08.035

Hubalovsky, S., Hubalovska, M. and Musilek, M., 2019. Assessment of the influence of adaptive E-learning on learning effectiveness of primary school pupils. Computers in Human Behavior, 92, pp.691-705. DOI: https://doi.org/10.1016/j.chb.2018.05.033

Huber, B., Tarasuik, J., Antoniou, M.N., Garrett, C., Bowe, S.J., Kaufman, J. and Team, S.B., 2016. Young children's transfer of learning from a touchscreen device. Computers in Human Behavior, 56, pp.56-64. DOI: https://doi.org/10.1016/j.chb.2015.11.010

Idrizi, E. and Filiposka, S., 2018. VARK Learning Styles and Online Education: Case Study. Learning, pp.5-6.

Kaur, N., Dwivedi, D., Arora, J. and Gandhi, A., 2020. Study of the effectiveness of e-learning to conventional teaching in medical undergraduates amid COVID-19 pandemic. National Journal of Physiology, Pharmacy and Pharmacology, 10(7), pp.1-5. DOI: https://doi.org/10.5455/njppp.2020.10.04096202028042020

Khalil, R., Mansour, A.E., Fadda, W.A., Almisnid, K., Aldamegh, M., Al-Nafeesah, A., Alkhalifah, A. and Al-Wutayd, O., 2020. The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students' perspectives. BMC medical education, 20(1), pp.1-10. DOI: https://doi.org/10.1186/s12909-020-02208-z

Kim, H.Y., 2020. More than tools: Emergence of meaning through technology enriched interactions in classrooms. International Journal of Educational Research, 100, p.101543. DOI: https://doi.org/10.1016/j.ijer.2020.101543

Kintu, M.J., Zhu, C. and Kagambe, E., 2017. Blended learning effectiveness: the relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14(1), DOI: https://doi.org/10.1186/s41239-017-0043-4

Lim, H., Lee, S.G. and Nam, K., 2007. Validating E-learning factors affecting training effectiveness. International Journal of Information Management, 27(1), pp.22-35. DOI: https://doi.org/10.1016/j.ijinfomgt.2006.08.002

Margaryan, A., Bianco, M. and Littlejohn, A., 2015. Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, pp.77-83. DOI: https://doi.org/10.1016/j.compedu.2014.08.005

Mathivanan, S.K., Jayagopal, P., Ahmed, S., Manivannan, S.S., Kumar, P.J., Raja, K.T., Dharinya, S.S., and Prasad, R.G., 2021. Adoption of e-learning during lockdown in India. International Journal of System Assurance Engineering and Management, pp.1-10. DOI: https://doi.org/10.1007/s13198-021-01072-4

Mbarek, R. and Zaddem, F., 2013. The examination of factors affecting e-learning effectiveness. International Journal of Innovation and Applied Studies, 2(4), pp.423-435.

Ni, A.Y., 2013. Comparing the effectiveness of classroom and online learning: Teaching research methods. Journal of Public Affairs Education, 19(2), pp.199-215. DOI: https://doi.org/10.1080/15236803.2013.12001730

Noesgaard, S.S. and Ørngreen, R., 2015. The Effectiveness of E-Learning: An Explorative and Integrative Review of the Definitions, Methodologies, and Factors that Promote e-Learning Effectiveness. Electronic Journal of E-learning, 13(4), pp.278-290.

Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R., 2008. Learning Styles: Concepts and Evidence. Psychol. Sci. Public Interest, 9, pp.105–119 DOI: https://doi.org/10.1111/j.1539-6053.2009.01038.x

Picciano, A.G., 2017. Theories and frameworks for online education: Seeking an integrated model. Online Learning, 21(3), pp.166-190 DOI: https://doi.org/10.24059/olj.v21i3.1225

Ramírez Anormaliza, R.I., SabatéiGarriga, F. and Guevara Viejo, F., 2015. Evaluating student acceptance level of e-learning systems. In ICERI2015: Proceedings 8th International Conference of Education, Research and Innovation November 16th-18th, 2015-Seville, Spain. (pp. 2393-2399). International Association of Technology, Education and Development (IATED).

Rana, H. and Lal, M., 2014. E-learning: Issues and challenges. International Journal of Computer Applications, 97(5), pp DOI: https://doi.org/10.5120/17004-7154

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L. and Koole, M., 2020. Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Science and Education, 2(3), pp.923-945. DOI: https://doi.org/10.1007/s42438-020-00155-y

Sanchez, D.R., Langer, M. and Kaur, R., 2020. Gamification in the Classroom: Examining the impact of gamified quizzes on student learning. Computers & Education, 144, p.103666. DOI: https://doi.org/10.1016/j.compedu.2019.103666

Surma, T. and Kirschner, P.A., 2020. Virtual special issue computers in human behavior technology enhanced distance learning should not forget how learning happens. Computers in human behavior, 110, p.106390. DOI: https://doi.org/10.1016/j.chb.2020.106390

Suryawanshi, V. and Suryawanshi, D., 2015. Fundamentals of E-Learning Models: A Review. IOSR Journal of Computer Engineering, ISSN, pp.2278-0661

Tarhini, A., Hone, K.S. and Liu, X., 2013. Factors affecting students' acceptance of e-learning environments in developing countries: a structural equation modeling approach. DOI: https://doi.org/10.7763/IJIET.2013.V3.233

Taylor, P. and Maor, D., 2000. Assessing the efficacy of online teaching with the Constructivist Online Learning Environment Survey.

Teo T., 2011. Technology Acceptance Research in Education, SensePublishers, pp.1-5. DOI: https://doi.org/10.1007/978-94-6091-487-4_1

Tseng, H., Kuo, Y.C. and Walsh Jr, E.J., 2020. Exploring first-time online undergraduate and graduate students' growth mindsets and flexible thinking and their relations to online learning engagement. EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT. DOI: https://doi.org/10.1007/s11423-020-09774-5

van Alten, D.C., Phielix, C., Janssen, J. and Kester, L., 2020. Effects of self-regulated learning prompts in a flipped history classroom. Computers in Human Behavior, p.106318. DOI: https://doi.org/10.1016/j.chb.2020.106318

Vössing, J., Stamov-Roßnagel, C. and Heinitz, K., 2016. Images in computer-supported learning: Increasing their benefits for metacomprehension through judgments of learning. Computers in Human Behavior, 58, pp.221-230. DOI: https://doi.org/10.1016/j.chb.2015.12.058

Wang, X., Lin, L., Han, M. and Spector, J.M., 2020. Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107, p.106279. DOI: https://doi.org/10.1016/j.chb.2020.106279

Sharma, S., Dick, G., Chin, W. and Land, L., 2007. Self-regulation and e-learning.

Lynch, R. and Dembo, M., 2004. The relationship between self-regulation and online learning in a blended learning context. International Review of Research in Open and Distributed Learning, 5(2), pp.1-16. DOI: https://doi.org/10.19173/irrodl.v5i2.189

Araka, E., Maina, E., Gitonga, R. and Oboko, R., 2020. Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018). Research and Practice in Technology Enhanced Learning, 15(1), pp.1-21. DOI: https://doi.org/10.1186/s41039-020-00129-5

Lee, D., Watson, S.L. and Watson, W.R., 2020. The influence of successful MOOC learners’ self-regulated learning strategies, self-efficacy, and task value on their perceived effectiveness of a massive open online course. International Review of Research in Open and Distributed Learning, 21(3), pp.81-98. DOI: https://doi.org/10.19173/irrodl.v21i3.4642

Wan, Z., Compeau, D. and Haggerty, N., 2012. The effects of self-regulated learning processes on e-learning outcomes in organizational settings. Journal of Management Information Systems, 29(1), pp.307-340. DOI: https://doi.org/10.2753/MIS0742-1222290109

Sun, P. and Lee, C. (2018) ‘Examining student characteristics, self-regulated learning strategies, and their perceived effects on satisfaction and academic performance in MOOCs’, Journal of Online Learning and Teaching, 14(2), pp. 45-60.

Ahmed, N., Khan, M. and Ali, S. (2020) ‘Metacognitive abilities on e-learning outcomes among senior secondary school students: A comparative analysis across school types’, International Journal of Educational Technology, 16(1), pp. 22-35.

Brown, A. and Smith, J. (2019) ‘Understanding the role of self-regulated learning in academic success: A blended learning perspective in vocational education’, Journal of Vocational Education and Training, 71(4), pp. 544-561.

Chen, X. and Zhao, L. (2021) ‘Relationships between online self-regulation skills, satisfaction, and perceived learning among distance education learners’, Distance Education Review, 42(3), pp. 279-295.

Williams, S. and Taylor, R. (2022) ‘Investigating effects of perceived technology-enhanced environment on self-regulated learning’, Educational Technology Research and Development, 70(2), pp. 311-324.

Johnson, M. (2017) ‘Understanding the impact of self-regulation on perceived learning outcomes based on social cognitive theory’, Journal of Educational Psychology, 109(6), pp. 892-906.

Kim, H. and Park, J. (2020) ‘Students’ acceptance of e-learning: Extending the technology acceptance model with self-regulated learning and affinity for technology’, Computers & Education, 151, pp. 1-13.

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Published

2024-05-31

How to Cite

Vijayan, V., M, H., & Kachroo, A. K. (2024). INFLUENCE OF ATTRIBUTES OF SELF-REGULATED LEARNING ON E-LEARNING IN SECONDARY SCHOOL STUDENTS. ShodhKosh: Journal of Visual and Performing Arts, 5(5), 1482–1491. https://doi.org/10.29121/shodhkosh.v5.i5.2024.3337