본문 바로가기
KERD
논문KCI 등재

Factors Influencing the Online Learning Behaviors of Middle School Students in South Korea

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류문헌정보학

초록

This study presented the factor analysis on constructing the new factors affecting the middle school students’ online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students’ online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students’ online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students’ online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

Abstract

This study presented the factor analysis on constructing the new factors affecting the middle school students’ online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students’ online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students’ online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students’ online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

참고문헌 (46)

  1. Abbas, J., "The impact of social media on learning behavior for sustainable education : evidence of students from selected universities in Pakistan", Sustainability, 11(6), pp.1683~ (2019)
  2. Al Kurdi, B., "An empirical investigation into examination of factors influencing university students’ behavior towards elearning acceptance using SEM approach", International Journal of Interactive Mobile Technologies(iJIM), 14(2), pp.19~41 (2020)[DOI]
  3. Ali, M. K., "Utilization of E-Learning-Based ICT Learning using the Google Classroom application during the COVID-19 pandemic", Journal of Educational Research and Evaluation, 4(4), pp.373~380 (2020)[DOI]
  4. Ansari, J. A. N., "Exploring the role of social media in collaborative learning the new domain of learning", Smart Learning Environments, 7(1), pp.9~ (2020)[DOI]
  5. Audrin, C., "Key factors in digital literacy in learning and education : a systematic literature review using text mining", Education and Information Technologies, 27(6), pp.7395~7419 (2022)[DOI]
  6. Blaschke, L. M., "Using social media to engage and develop the online learner in self-determined learning", Research in Learning Technology, 22(0) (2014)[DOI]
  7. Briz, L., "Learning with mobile technologies-Students’ behavior", Computers in Human Behavior (2016)[DOI]
  8. Burford, S., "The impact of mobile tablet devices on human information behaviour", Journal of Documentation, 70(4), pp.622~639 (2014)[DOI]
  9. Chen, G., "Study on the influence of middle school students’self-efficacy on the willingness to use online learning platform", The International Journal of Electrical Engineering & Education, 0(0), pp.1~23 (2021)[DOI]
  10. Chen, Z., "Relationship between students’ online learning behavior and course performance : what contextual information matters", Physical Review Physics Education Research, 16(1), pp.010138~ (2020)[DOI]
  11. Dafoulas, G. A., "Delivery of E-Learning through Social Learning Networks International Conference e-Learning 2014", Multi Conference on Computer Science and Information Systems 2014 (2014)
  12. Field, A, "Discovering Statistics Using IBM SPSS Statistics" (2013), SAGE Publications
  13. Franke, T., "A personal resource for technology interaction : development and validation of the affinity for technology interaction(ATI)scale", International Journal of Human-Computer Interaction, 35(6), pp.456~467 (2019)[DOI]
  14. Gopal, R., "Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19", Education and Information Technologies, 26(6), pp.6923~6947 (2021)[DOI]
  15. Henson, R. K., "Use of exploratory factor analysis in published research : common errors and some comment on improved practice", Educational and Psychological Measurement, 66(3), pp.393~416 (2006)[DOI]
  16. "Frontiers in Psychology", 2021, ~, https:
  17. Jin, F., "Affinity for technology and teenagers’ learning intentions", Association for Computing Machinery (ACM), pp.48~55 (2020)
  18. Kolokytha, E., "Social media networks as a learning tool", Procedia Economics and Finance, 19, pp.287~295 (2015)[DOI]
  19. Lepp, A., "The relationship between cell phone use and academic performance in a sample of U. S. college students", SAGE Open, 5(1), pp.2158244015573169~ (2015)[DOI]
  20. Liang, K., "Online behavior analysis-based student profile for intelligent e-learning", Journal of Electrical and Computer Engineering, 2017, pp.9720396~ (2017)[DOI]
  21. Liu, Y., "Social media tools as a learning resource", Journal of Educational Technology Development and Exchange, 3(1), pp.101~114 (2010)[DOI]
  22. Luaran, J. E., "A study on the student’s perspective on the effectiveness of using e-learning", Procedia-Social and Behavioral Sciences, 123, pp.139~144 (2014)[DOI]
  23. "Frontiers in Psychology", 2017, ~, https:
  24. Mills, L., "Measuring Learning Preferences within the Integrated Communications Learning Landscape", ~ (2012)
  25. Mills, L. A., "Learning with social media: measurement tools for understanding information behavior in technology pervasive environments of the 21st century", Paper presented at iConference 2013 proceedings, pp.593~600 (2013)
  26. Moghavvemi, S., "Social media as a complementary learning tool for teaching and learning : the case of Youtube", The International Journal of Management Education, 16(1), pp.37~42 (2018)[DOI]
  27. Muthuprasad, T., "Students’ perception and preference for online education in India during COVID-19 pandemic", Social Sciences & Humanities Open, 3(1), pp.100101~ (2021)[DOI]
  28. 나경식, "Exploring Cognitive, Affective, and Physical Aspects of Early Adolescents’ Health Information Seeking Behaviors", 한국문헌정보학회지, 55(2), pp.289~324 (2021), 한국문헌정보학회[DOI]
  29. Nguyen, T., "The effectiveness of online learning : beyond no significant difference and future horizons", MERLOT Journal of Online Learning and Teaching, 11(2), pp.309~319 (2015)
  30. "Frontiers in Computer Science", 2019, ~, https:
  31. Pavel, A. P., "ICT and e-learning : catalysts for innovation and quality in higher education", Procedia Economics and Finance, 23, pp.704~711 (2015)[DOI]
  32. Qazi, A., "The role of information & communication technology in elearning environments : a systematic review", IEEE Access, pp.1~1 (2021)[DOI]
  33. Razzaq, A., "Smartphone habits and behaviors in supporting students self-efficacy", International Journal of Emerging Technologies in Learning(iJET), 13(2), pp.94~109 (2018)[DOI]
  34. Robinson, R. S., "Encyclopedia of Quality of Life and Well-Being Research", pp.5243~5245 (2014), Springer Netherlands
  35. Shrestha, N., "Factor analysis as a tool for survey analysis", American Journal of Applied Mathematics and Statistics, 9(1), pp.4~11 (2021)[DOI]
  36. Song, Y, "How does College Students’ Online Learning Behavior Impact Their Academic Performance", 2018 13th International Conference on Computer Science & Education (ICCSE) (2018)
  37. Sukmanasa, W., "The effect of digital literature on students’ learning independence in the 2019 COVID situation", Jurnal Pendidikan dan Pengajaran Guru Sekolah Dasar(JPPGuseda), 5, pp.60~65 (2022)[DOI]
  38. Sung, Y. T., "The effects of integrating mobile devices with teaching and learning on students’ learning performance : a meta-analysis and research synthesis", Computers & Education, 94, pp.252~275 (2016)[DOI]
  39. Thai, N. T. T., "Face-to-face, blended, flipped, or online learning environment? Impact on learning performance and student cognitions", J. Comput. Assist. Learn, 36(3), pp.397~411 (2020)[DOI]
  40. Wang, C., "Learning performance and behavioral patterns of online collaborative learning : Impact of cognitive load and affordances of different multimedia", Computers & Education, 143, pp.103683~ (2020)[DOI]
  41. Wei, H. C., "Online learning performance and satisfaction : do perceptions and readiness matter", Distance Education, 41(1), pp.48~69 (2020)[DOI]
  42. Yan, S., "COVID-19 and technology use by teenagers : a case study", Human Behavior with Emerging Technologies, 3(1), pp.185~193 (2021)[DOI]
  43. Yang, C., "Regional differences of online learning behavior patterns", The Electronic Library, 31(2), pp.167~187 (2013)[DOI]
  44. Yu, T. -K., "Understanding factors influencing information communication technology adoption behavior", Comput. Hum. Behav, 71(C), pp.196~208 (2017)[DOI]
  45. Zawaideh, F. H., "The effect of mobile learning on the development of the students’ learning behaviors and performance at Jordanian university", International Journal of Business and Management Invention, 6(3), pp.1~7 (2017)
  46. Zhang, X., "An analysis of online students’ behaviors on course sites and the effect on learning performance : a case study of four LIS online classes", Journal of Education for Library and Information Science, 57(4), pp.255~270 (2016)

관련 논문AI추천

이 간행물의 다른 논문 (18건)