Skip to main content

Advertisement

Log in

A study on factor analysis to support knowledge based decisions for a smart class

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

A smart class provides an environment that enables collaboration, sharing, and participation between teachers and students. Thanks to the great attention paid to the smart class idea, with a view to providing effective and efficient learning for students, many state-of-the-art technologies have been applied to the field of education. However, simple infrastructure construction and the introduction of state-of-the-art technology have many limitations in obtaining the desired effects of a smart class. This study aims to discover the important elements that allow a smart class to achieve positive effects in education and to support the design and application of a smart class based on the derived elements. In the study, an integrated teaching and learning assistance system was applied to a smart class. A smart class environment was constructed that was applied and test operated in an elementary school for 4 weeks. Through the test operation of the smart class, the important elements of an effective smart class were determined to be system playfulness, perceived usefulness, perceived ease of use, and attitude toward class.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Future education 2020 report, Retrieved from http://www.futureofed.org/forecast/2020_forecast.pdf

  2. Yu J, Jung G, Kim J (2009) Newest university informatization trend analysis library. Korea, Korea Education and Research Information Service

    Google Scholar 

  3. Ryu YU, Kim JK, Choi IY (2013) The role of IT in Korea’s economic development. Inf Technol Manag 14(1):3–6

    Article  Google Scholar 

  4. Lee J, Lee D, Moon J, Park M-C (2013) Factors affecting the perceived usability of the mobile web portal services: comparing simplicity with consistency. Inf Technol Manag 14(3):43–57

    Article  Google Scholar 

  5. Eun J (2010) Realization of smart mobile power; survey of actual state of use of smartphones. Korea Internet & Security Agency, Korea

    Google Scholar 

  6. Norman DA (1990) The design of everyday things. Doubleday Currency, NY

    Google Scholar 

  7. Hyeon-Cheol K (2011) Issue of the development of smart education contents quality control and teaching-learning models, KERIS issue report research data, RM 2011-20, 2011

  8. Prensky M (2001) Digital natives, digital immigrants, vol 9, 5. MCB University Press, Bradford

    Google Scholar 

  9. Bo-Gyeong G, Hyeon-Jin K, Hee-Jeon S, Jong-Won J, Eun-Hwan L (2011) Future School 2030 model for the introduction of future school systems. Korea Education & Research Information Service, Korea

    Google Scholar 

  10. Lee J (2011) The way to matching talent—a smart strategy to promote education. Ministry of Education Science and Technology, Republic Korea

    Google Scholar 

  11. Jo J, Lim H (2014) A comparative analysis of the effectiveness of learning English vocabulary between smart device and printed material. Int J of Intelligent Information and Database Systems 8(2):150–161

    Article  Google Scholar 

  12. Yahaya CAB (2009) What is smart school. Universiti Pendidikan Sultan Idris, Saya Cantik

    Google Scholar 

  13. Perkins D. Project zero: smart schools. Graduate School of Education in Harvard University. Retrieved from http://www.pz.gse.harvard.edu/smart_schools.php

  14. Jung K, Lee S (2010) 2009–2010 e-learning white paper. Ministry of Knowledge Economy & National IT Industry Promotion Agency & Korea Association of Consilience Education, Republic of Korea

    Google Scholar 

  15. Park SY, Nam MW (2012) Analysis of the structural relationship between university students’ intention to use mobile learning and influence factors applied with IT acceptance models. J Korean Assoc Edu Inf Media 18(1):51–75

    Google Scholar 

  16. Miyazaki S, Idota H, Miyoshi H (2012) Corporate productivity and the stages of ICT development. Inf Technol Manag 13(1):17–26

    Article  Google Scholar 

  17. Jo J, Park K, Lee D, Lim H (2014) An integrated teaching and learning assistance system meeting requirements for smart education. Wireless Pers Commun 79(4):2453–2467

    Article  Google Scholar 

  18. Kim H-s, Lee K (2014) Development of social studies’ teaching model for effective use of paper and digital textbooks. Soc Stud Educ 53(3):51–67

    Google Scholar 

  19. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:983–1003

    Article  Google Scholar 

  20. Lewis W, Agarwal R, Sambamurthy V (2002) Spheres of influence and cognitive interpretations of information technology: an empirical study of knowledge workers. University of Maryland Working Paper

  21. Gun-Kwon S, Sung-Jin S (2011) The impact of innovation propensity and IT application ability on the adoption of the home trading system. J Korean Acad Soc Account 16(1):207–226

    Google Scholar 

  22. Yadav MS, Varadarajan R (2005) Interactivity in the electronic marketplace: an exposition of the concept and implications for research. J Acad Mark Sci 33:585–603

    Article  Google Scholar 

  23. Wang J, Senecal S (2007) Measuring perceived website usability. J Internet Commer 6(4):97–112

    Article  Google Scholar 

  24. Igraria M, Baroudi JJ, Parasuraman S (1996) A motivational model of microcomputer usage. J Manag Inf Syst 13(1):127–143

    Article  Google Scholar 

  25. Bruner GC II, Kumar A (2005) Applying T.A.M. to consumer usage of handheld Internet devices. J Bus Res 58:553–558

    Article  Google Scholar 

  26. Lin C-C (2013) Exploring the relationship between technology acceptance model and usability test. Inf Technol Manag 14(3):243–255

    Article  Google Scholar 

  27. In-Jun H, Seong-Il L (2010) The study of knowing the intention to adopt smartphone by extending Technology Acceptance Model. In: Korean Institute of Industrial Engineers, 2010 Conference, pp. 1–8

  28. Kim S-H (2010) Effects of perceived attributes on the purchase intention of smart-phone. J Korea Contents Assoc 10(9):318–326

    Article  Google Scholar 

  29. Lee YI (2010) A study on the smart-phone TAM and satisfaction of college students. J Korea Res Acad Distrib Manag 13(5):93–110

    Google Scholar 

  30. Soo CM (2011) A study on the influence of factors such as personal innovativeness, social influence and user interface on smart phone acceptance: based on an expanded technology acceptance model. The Graduate School of Ewha Womans University

  31. Suh C-K, Seong S-J (2004) Individual characteristics affecting user`s intention to use internet shopping mall. Asia Pac J Inf Syst 14(3):1–22

    Google Scholar 

  32. Kim HJ, Jung CH (2008) The impacts of commodity and user characteristics on customers’ intention to reuse in mobile banking services. J Bus Educ 21:215–246

    Google Scholar 

  33. Oliver RL (1997) Satisfaction: a behavioral perspective on the consumer. McGraw-Hill, NY

    Google Scholar 

  34. Lee YN, Lee LY, Park HH, Park SH (2007) The study on brand image by relationship quality (Satisfaction, trust, commitment) of family restaurants effects on brand loyalty: focusing on customers at their twenties. J Korea Hotel Resort Assoc 6(2):221–238

    Google Scholar 

  35. Gun-Kwon S, Sung-Jin S (2011) The effects of online HTS interactivity on customer’s system acceptance. J Korean Acad Soc Account 16(3):177–196

    Google Scholar 

  36. Lawshe CH (1975) A quantitative approach to content validity. Pers Psychol 28:563–575

    Article  Google Scholar 

  37. Wilson FR, Pan W, Schumsky DA (2012) Recalculation of the critical values for Lawshe’s content validity ratio. Measure Eval Couns Develop 45(3):197–210

    Article  Google Scholar 

  38. Kim J (2011) Curriculum evaluation based on middle school informatics textbook analysis. Ph.D: Graduate School at Korea University, Korea

    Google Scholar 

  39. Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88(3):588

    Article  Google Scholar 

  40. Kim G-S (2013) Effect of difference education quality on student satisfaction and student loyalty. J Korean Soc Qual Manag 41(1):53–68

    Article  Google Scholar 

  41. Korean Society for Educational Evaluation (2004) Educational evaluation glossary, Hakji Co.

  42. Picciano AG (2002) Beyond student perceptions: issues of interaction, presence, and performance in an online course. J Asynchronous Learn Netw 6(1):21–40

    Google Scholar 

  43. Kim M-R, Kim J-S (2007) Analysis of students’ attitude and satisfaction level toward afterschool e-homestudy. J Korea Contents Assoc 7(10):44–58

    Article  Google Scholar 

  44. Arbaugh JB (2000) Virtual classroom characteristics and student satisfaction with internet-based MBA courses. J Manag Educ 24(1):32–54

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the ICT R&D program of MSIP/IITP. [2015, Development of distribution and diffusion service technology through individual and collective Intelligence to digital contents.]

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Heuiseok Lim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jo, J., Park, J., Ji, H. et al. A study on factor analysis to support knowledge based decisions for a smart class. Inf Technol Manag 17, 43–56 (2016). https://doi.org/10.1007/s10799-015-0222-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-015-0222-8

Keywords

Navigation