DOI QR코드

DOI QR Code

Exploring the Relationships between Adolescents' Perceived Achievement Goals, ICT Use in Education, Academic Achievement, and Attitudes toward Learning

  • 투고 : 2015.08.26
  • 심사 : 2015.10.15
  • 발행 : 2015.10.30

초록

Perceived control and use of Information and Communication Technology (ICT) has long been known as important aspects of students' achievement. The purpose of this study was to explore the relationship between adolescents' perceived achievement goals, their Individual ICT use, ICT use for government-sponsored educational programs on television or the Internet, academic achievement and the attitude toward learning. Most previous research has employed cross-sectional data analysis using relatively small samples. For this purpose, this study used the datasets of the Seoul Education Longitudinal Study (SELS 2011) from Seoul Educational Research & Information Institute. We analyzed structural equation modeling (SEM) a nationally represented sample (4,346 eighth-grade students). The results of this study showed that students' perceived achievement goals had a positive relationship with their individual ICT use, and their use of ICT programs for government-sponsored educational programs on television or the Internet. Also, students' individual ICT use had a positive relationship with their achievement, but ICT use for government-sponsored educational programs on television or the Internet did not have a significant relationship with their achievement. That is, students' individual ICT use mediated the relationship between their perceived goals and academic achievement. In addition, results indicated that students' individual ICT use and ICT use for government-sponsored educational programs on television or the Internet had a positive relationship with their attitude toward learning. That is, both students' individual ICT use and ICT use for government-sponsored educational programs on television or the Internet mediated the relationship between their perceived goals and their attitude toward learning.

키워드

과제정보

This work was supported by the Dong-A University research fund.

참고문헌

  1. Arbuckle, J. L. (2006). Amos for Windows. Analysis of moment structures (Version 7.0). Chicago, IL: SmallWaters Corp.
  2. Aristovnik, A. (2012). The impact of ICT on educational performance and its efficiency in selected EU and OECD countries: A non-parametric analysis. The Turkish Online Journal of Educational Technology, 3(11), 144-152.
  3. Attewell, P., & Battle, J. (1999). Home computers and school performance. The Information Society, 15(1), 1-10.
  4. Aypay, A. (2010). Information and communication technology (ICT) usage and achievement of Turkish students in PISA 2006. The Turkish Online Journal of Educational Technology, 9(2), 116-124.
  5. Bandalos, D. L., & Finney, S. J. (2001). Item parceling issues in structural equation modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 269-296). Hillsdale, MI: Erlbaum.
  6. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
  7. Biagi, F., & Loi, M. (2013). Measuring ICT use and learning outcomes: Evidence from recent econometric studies. European Journal of Education, 48(1), 28-42.
  8. Bocconi, S., Kampylis, P., & Punie, Y. (2012). Innovating teaching and learning practices: Key elements for developing creative classrooms in Europe. eLearning Papers, 30, 1-13. Retrieved January 5, 2015 from http://openeducationeuropa.eu/en/article/Innovating-Teaching-and-Learning-Practices%3A-Key-Elements-for-Developing-Creative-Classrooms-in-Europe
  9. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Ed.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
  10. Chandra, V., & Lloyd, M. (2008). The methodological nettle: ICT and student achievement. British Journal of Educational Technology, 39(6), 1087-1098.
  11. Collins, L. M, Schafer, J. L., & Kam, C-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Structural Equation Modeling, 6(4), 330-351.
  12. Compeau, D. R., & Higgins, C. A. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
  13. Curran, P. J., West, S. G., & Finch, J. (1996). The robustness of test statistics to non-normality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16-29.
  14. Dewey, J., Husted, T. A., & Kenny, L. W. (2000). The ineffectiveness of school inputs: A product of misspecification? Economics of Education Review, 19(1), 27-45.
  15. Dickhauser, O., & Stiensmeier-Pelster, J. (2003). Gender differences in the choice of computer courses: Applying the expectancy-value model. Social Psychology of Education, 6(3), 173-189.
  16. EBS (2014). About EBS (Educational Broadcasting System): 2020 VISION. Retrieved May 6, 2014, from http://global.ebs.co.kr/eng/about/vision
  17. Gumus, S. (2013). Investigating the factors affecting information and communication technology (ICT) usage of Turkish students in PISA 2009. The Turkish Online Journal of Educational Technology, 12(1), 102-107.
  18. Gunduz, H. B. (2010). Digital dvide in Turkish primary schools: Sakarya sample. Turkish Online Journal of Educational Technology & Society, 9(1), 43-53.
  19. Hakkarainen, K., Ilomaki, L., Lipponen, L., Muukkonen, H., Rahikainen, M., Tuominen, T., Lakkala, M., & Lehtinen, E. (2000). Students' skills and practices of using ICT: Results of a national assessment in Finland. Computers & Education, 34(2), 103-117.
  20. Hendriks, P. (1999). Why share knowledge? The influence of ICT on the motivation for knowledge sharing. Knowledge and Process Management, 6(2), 91-100
  21. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in convariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
  22. Isman, A. (2012). Technology and technique: An educational perspective. The Turkish Online Journal of Educational Technology & Society, 11(2), 207-213.
  23. James, R. K., Lamb, C. E., Householder, D. L., & Bailey, M. A. (2000). Integrating science, mathematics, and technology in middle school technology-rich environments: A study of implementation and change. School Science and Mathematics, 100(1), 27-35.
  24. Kline, R. B. (2010). Principles and practices of structural equation modeling (3rd ed.). New York: Guilford Press.
  25. Kuhlemeier, H., & Hemker, B. (2007). The impact of computer use at home on students' Internet skills. Computers & Education, 49(2), 460-480.
  26. Liu, T. C., Wang, H. Y., Liang, J. K., Chan, T. W., Ko, H. W., & Yang, J. C. (2003). Wireless and mobile technologies to enhance teaching and learning. Journal of Computer Assisted Learning, 19(3), 371-382.
  27. Liu, X. (2004). Socio-cultural context for online learning: A case study viewed from activity theory perspective. Paper presented at the Association for Educational Communications and Technology Conference, Chicago, IL (pp. 606-613). Bloomington, IN: Association for Educational Communications and Technology.
  28. Mac Callum, K., Jeffrey, L., & Kinshuk (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior, 39, 8-19.
  29. McCarthy, M. (2000). Computers and the internet: Tools for lifelong learning. Journal of Renal Nutrition, 10(1), 44-48.
  30. Meelissen, M. R. M., & Drent, M. (2008). Gender differences in computer attitudes: Does the school matter? Computers in Human Behavior, 24(3), 969-985.
  31. Papanastasiou, E. (2002). Factors that differentiate mathematics students in Cyprus, Hong Kong, and the USA. Educational Research and Evaluation, 8(1), 129-146.
  32. Papanastasiou, E., Zembylas, M., & Vrasidas, C. (2003). When computer use is associated with negative science achievement. Journal of Science Education and Technology, 12(3), 325-332.
  33. Rainer, R. K., Laosethakul, K., & Astone, M. K. (2003). Are gender perceptions of computing changing over time? Journal of Computer Information Systems, 43(4), 108-114.
  34. Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. Journal of Educational Research, 99(6), 323-337.
  35. Schweingruber, H., Brandenburg, C., & Miller, L. (2001). Middle school students' technology practices and preferences: Re-examining gender differences. Journal of Educational Multimedia and Hypermedia, 10(2), 125-140.
  36. Shih, H. P. (2006). Assessing the effects of self-efficacy and competence on individual satisfaction with computer use: An IT student perspective. Computers in Human Behavior, 22(6), 1012-1026.
  37. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-312.
  38. Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in crossnational consumer research. Journal of Consumer Research, 25(1), 78-90.
  39. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173-180.
  40. Steiger, J. H., & Lind, J. C.(1980). Statistically-based tests for the number of common factors. Paper presented at the annual Spring Meeting of the Psychometrics Society, Iowa City, IA.
  41. Tang, P. S., & Ang, P. H. (2002). The diffusion of information technology in Singapore schools: A process framework. New Media & Society, 4(4), 457-478.
  42. Tondeur, J., Valcke, M., & Van Braak, J. (2008). A multidimensional approach to determinants of computer use in primary education: Teacher and school characteristics. Journal of Computer Assisted Learning, 24(6), 494-506.
  43. Tsai, C. C., & Lin, C. C. (2004). Taiwanese adolescents'perceptions and attitudes regarding the Internet: Exploring gender differences. Adolescence, 39(156), 725-734.
  44. Tsai, M. J., & Tsai, C. C. (2010). Junior high school students' Internet usage and self-efficacy: A re-examination of the gender gap. Computers & Education, 54(4), 1182-1192.
  45. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1-10.
  46. Underwood, J. (2004). Research into information and communications technologies: Where now? Technology, Pedagogy and Education, 13(2), 135-145.
  47. Vekiri, I. (2010). Socioeconomic differences in elementary students' ICT beliefs and out-of-school experiences. Computers & Education, 54(4), 941-950.
  48. Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education, 51(3), 1392-1404.
  49. Volman, M., van Eck, E., Heemskerk, I., & Kuiper, E. (2005). New technologies, new differences. Gender and ethnic differences in pupils' use of ICT in primary and secondary education. Computers & Education, 45(1), 35-55.
  50. Vrasidas, C., & Glass, G. V. (2002). A conceptual framework for studying distance education. In C. Vrasidas & G. V. Glass (Eds.), Current perspectives in applied information technologies: Distance education and distributed learning (pp. 31-56). Greenwich, CT: Information Age Publishing, Inc.
  51. Vrasidas, C., & McIsaac, M. (2001). Integrating technology in teaching and teacher education: Implications for policy and curriculum reform. Educational Media International, 38(2/3), 127-132.
  52. Weaver, G. C. (2000). An examination of the National Educational Longitudinal Study (NELS: 88) database to probe the correlation between computer use in school and improvement in test scores. Journal of Science Education and Technology, 9(2), 121-133.
  53. Wenglinsky, H. (1998). Does it compute? The relationship between educational technology and student achievement in mathematics. Princeton, NJ: Educational Testing Service Policy Information Center.
  54. West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with non-normal variables: Problems and remedies. In R. Hoyle (Eds.), Structural equation modeling: Concepts, issues and applications (pp. 56-75). Newbury Park, CA: Sage.
  55. Wilhelm, A. G. (2004). Everyone should know the basics: Equalizing opportunities and outcomes for disadvantaged youths through ICT in education. In A. Karpati (Eds.), Promoting equity through ICT in education: Project, problems, prospects (pp. 81-96). Budapest, Hungary: OECD and Hungarian Ministry of Education.
  56. Wittwer, J., & Senkbeil, M. (2008). Is students' computer use at home related to their mathematical performance at school? Computers & Education, 50(4), 1558-1571.
  57. Ziden, A. A., Ismail, I., Spian, R., & Kumutha, K. (2011). The effects of ICT use in teaching and learning on students' achievement in Science subject in a primary school in Malaysia. Malaysia Journal of Distance Education, 13(2), 19-32.