EXAMINING THE ONLINE LEARNING CHALLENGES AND ITS RELATIONSHIP ON LEARNING PERFORMANCE: CASE AT SELECTED SCHOOLS

Autor

  • Andrew Sija University of Technology Sarawak, School of Business & Management, Malaysia

DOI:

https://doi.org/10.46827/ejoe.v9i1.5323

Słowa kluczowe:

learning resources, learning environment, self-regulation, technological sufficiency, students’ learning performance

Abstrakt

This study aims to investigate the challenges derived from implementing online learning and its effect on students' learning performances. The key challenges mentioned in this study were the learning resources, learning environment, self-discipline and technological sufficiency. In specific, it is to assess the relationship between the selected independent variables i.e. the key challenges with the dependent variable (learning performances). Research suggests that online learning has been shown to increase retention of information among students and consume less time. It is the objective of the researcher in this study to illustrate and present the effectiveness, relevancy and applicability of online learning as an alternative to sustainable education in government schools. Although quite a number of studies have been performed and some researchers have delved deep into their interest in investigating these issues, however, researchers still found limited information from online learning specifically on the challenges which crucially affect students’ learning performance. To achieve this goal, a total of 210 samples of students from two government secondary schools have been taken into consideration, and (from SMK Baru Bintulu and SMK Bintulu). The data collection technique was carried out by distributing questionnaires and all data were gathered, processed and analyzed using statistical technique SPSS version 26. The descriptive analysis was used to gauge various responses across various dimensions such as in the respondent’s demographic information, additionally, inferential analysis was mainly for examining the relationship between the challenges (learning resources, learning environment, self-discipline and technological sufficiency) and student’s learning performance. The results revealed learning environment challenge is the strongest predictor in influencing student’s learning performance with unstandardized coefficients, β = .356, t = 4.726 p <.05, followed by self-regulation challenge with β = .316, t = 4.182, p <.05. The most significant contribution of this study is that it explores for the first time the investigation of challenges and effect of online learning on students’ learning performance among students at government schools, this has brought a crucial impact and pushed to the government to take immediate action regarding the matter.

 

Biogram autora

  • Andrew Sija - University of Technology Sarawak, School of Business & Management, Malaysia
    Dr. Andrew Sija is a lecturer at the University of Technology Sarawak, Malaysia. He has more than 20 years of experience in various sectors within the education and financial services industry. He holds a Doctorate Degree in Business Administration, a Certified Trainer in Business Accounting, PSMB (Malaysia) and a Certified Instructor in Accounting for Hospitality Industry, AHLEi (USA). His research interests are accounting, banking, education and business management.

Bibliografia

Alenezi, A. R., Karim, A. A., & Veloo, A. (2011). Institutional support and e-learning acceptance: An extension of the technology acceptance model. International Journal of Instructional Technology and Distance Learning, 8(2), 3–16.

Almaiah M.A., Al-Khasawneh A., & Althunibat A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261–5280, doi: 10.1007/s10639-020-10219-y

Balog, N. (2018). Impacts of the Learning Environment on Developer’s Progress. Accessed 4/10/2019 from https://www.codingdojo.com/blog/impacts-of-the-learning-nvironment.

Becton, L. (2017). Strategies for Building a Productive and Positive Learning Environment: Accessed 7/10/2019 from: https://www.educationcorner.com/building-a-positive-learning-environment.html

Bruce. C. & Flynn, T. (2013). Assessing the Effects of Collaborative Professional Learning: Efficacy shifts in a three-year mathematics study. Alberta Journal of Educational Research, 58(4), 691-709.

Closs, L., Mahat, M., & Imms, W. (2021). Learning environments’ influence on students’ learning experience in an Australian Faculty of Business and Economics. Learning Environments Research. https://doi.org/10.1007/s10984-021-09361-2

Conole, G., de Laat, M., Dillon, T., & Darby, J. (2008). “Disruptive technologies”, “pedagogical innovation”: What’s new? Findings from an in-depth study of students’ use and perception of technology. Computers & Education, 50(2), 511–524. doi:10.1016/j.compedu.2007.09.009

Duruji, M.M., Azuh, D., & Oviasogie, F. (2014). Learning Environment and Academic Performance of Secondary School Students in External Examinations: A Study Of Selected Schools In Ota, Nigeria. Proceedings of EDULEARN 14 Conferences, Barcelona, Spain. 7th-9thJuly, PP.5042-5053.

DiDonato, N.C. (2013). Effective self-and co-regulation in collaborative learning groups: an analysis of how students regulate problem-solving of authentic interdisciplinary tasks. Instructional Science, 41(1), 25-47.

Gopal, R., Singh, V., & Aggarwal, A. (2021). Impact of Online Classes on the Satisfaction and Performance of Students during the Pandemic Period of COVID-19. Educ. Inform. Technol.

Greene, J.A., Hutchison, L.A., Costa, L.J., & Crompton, H. (2012). Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic. Contemporary Educational Psychology, 37(4), 307-320.

Hao, Y. (2016). Exploring undergraduates’ perspectives and flipped learning readiness in their flipped classrooms. Computers in Human Behavior, 59, 82-92.

Jovanovic, J., Mirriahi, N., Gaševic, D., Dawson, S. and Pardo, A. (2019). Predictive power of regularity of pre-class activities in a flipped classroom. The Internet and Higher Education Computers and Education, 134, 156-168.

Laurillard, D. (2002). Rethinking university teaching: A conversational framework for the effective use of learning technologies (2nd ed.). London: Routledge

Levy, Y. (2008). An empirical development of critical value factors (CVF) of online learning activities: An application of activity theory and cognitive value theory. Computers & Education, 51(4), 1664–1675. doi:10.1016/j.compedu.2008.04.003

Mensink, P. J., and King, K. (2020). Student access of online feedback is modified by the availability of assessment marks, gender and academic performance. Br. J. Educ. Technol, 51, 10-22, doi: 10.1111/bjet.12752

Mondal. (2012). Important Factors that May Affect the Learning Process. Accessed 3/10/2019 from http://www.yourarticlelibrary.com/learning/7-important-factors-that-may-affect-the-learningprocess/6064

Moore, M., & Kearsley, G. (2012). Distance Education: A System View of Online Learning (3ed). Wadsworth: Cengage Learning

Motaghian H., Hassanzadeh A., Moghadam D.K. Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Computers & Education. 2013; 61:158–167. doi: 10.1016/j.compedu.2012.09.016

Nambiar D. (2020). The impact of online learning during COVID-19: Students’ and. The International Journal of Indian Psychology. 8(2), 12.

Narad, A., and Abdullah, B. (2016). Academic performance of senior secondary school students: Influence of parental encouragement and school environment. Rupkatha J. Interdiscipl. Stud. Human, 8, 12-19, doi: 10.21659/rupkatha.v8n2.02

Park, S. & Kim, N. H. (2020). University students’ self-regulation, engagement and performance in flipped learning. European Journal of Training and Development, 46(1), 22-40.

Sedek, M., Mahmud, R., Jalil, H. A., & Daud, S. M. (2012). Types and Levels of Ubiquitous Technology Use among ICT Undergraduates. Procedia - Social and Behavioral Sciences, 64, 255–264. doi:10.1016/j.sbspro.2012.11.030

Sletten, S.R. (2017). Investigating flipped learning: student self-regulated learning, perceptions, and achievement in an introductory biology course. Journal of Science Education and Technology, 26(3), 347-358.

Sun P.-C., Tsai R.J., Finger G., Chen Y.-Y., & Yeh D. (2008). What drives successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education. 50(4), 1183–1202. doi: 10.1016/j.compedu.2006.11.007

Sun, Z., Lu, L., & Xie, K. (2016). The Effects of Self-Regulated Learning on Students’ Performance Trajectory in the Flipped Math Classroom, International Society of the Learning Sciences, Singapore.

Sun, Z., Xie, K. and Anderman, L.H. (2018). The role of self-regulated learning in students’ success in flipped undergraduate math courses, The Internet and Higher Education, 36, 41-53.

Tam, Maureen. (Apr. 2001). Measuring Quality and Performance in Higher Education. Quality in Higher Education, 7(1), 47–54, doi: 10.1080/13538320120045076

Thompson, P. (2013). The digital natives as learners: Technology use patterns and approaches to learning. Computers & Education, 65, 12–33. doi:10.1016/j.compedu.2012.12.022

Travers, N.L., & Sheckley, B.G. (2000). Changes in students’ self-regulation based on different teaching methodologies. Paper presented at the 40th Association for Institutional Research Forum, Cincinnati, OH.

Usman, Y.D. (2016). Why Teachers Need Professional Development. Daily Sun Newspaper. 26th Sep, P.14. Available also at https://www.sunnewsonline.com

Waldman, C (2016). Four Elements for Creating a Positive Learning Environment; Available at https://all4ed.org/four-elements-for-creating-a-positive-learning-environment/ Accessed September 20/9/2019

Wheelbarger, J. J. (1974). Learning Resources Centers for Schools and. Retrieved from https://files.eric.ed.gov/fulltext/ED090995.pdf

Wilkinson, T. J., Ali, A. N., Bell, C. J., Carter, F. A., Frampton, C. M., & McKenzie, J. M. (2013). The impact of learning environment disruption on medical student performance. Medical Education, 47(2), 210–213. https://doi.org/10.1111/medu.12065

Williamson, G. (2015). Self-regulated learning: an overview of metacognition, motivation and behavior. Journal of Initial Teacher Inquiry, 1, 25-27.

Zhang Z., Cao T., Shu J., & Liu H. (2020). Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments. 1–14. doi: 10.1080/10494820.2020.1723113

Zheng, B., Ward, A., & Stanulis, R. (2020). Self-regulated learning in a competency-based and flipped learning environment: learning strategies across achievement levels and years. Medical Education Online, 25(1), doi: 10.1080/10872981.2019.1686949

Zimmerman, B.J. (1989). A social cognitive of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339.

Pobrania

Opublikowane

2024-04-13

Numer

Tom 9 Nr 1 (2024)

Dział

Articles

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