Advertisement

Classroom 4.0: Understanding the New Battleground

  • Rohit Vishal KumarEmail author
Chapter

Abstract

The classroom of today is emerging as a new battleground between the teachers and the students. With a new generation of tech-savvy students—the teachers are facing a complex battle trying to impose the older “chalk and talk” methodology in the face of multiple sources of information. The new generation—increasingly born after 1990s—is extremely reliant on various digital tools for day-to-day work including learning. This has brought the teacher into direct conflict with Google and Wikipedia which are regarded as more learned than the person delivering the knowledge in the classroom. The rising social and digital tensions have made the classroom more of a battlefield with the traditional mode of learning and teaching fighting a losing battle against the new waves of digital invaders. This chapter proposes to look at how the students perceive the new classroom and what role does the various platforms play in terms of classroom interaction. Data for the research was collected from 198 students from various business schools. Exploratory factor analysis using parallel analysis was employed to understand the underlying dimensions and the interrelationships between them. An attempt was also made at using confirmatory factor analysis to understand the dynamics of the relationships. Four dimensions were identified and the paper tries to synthesize the outcomes with classroom teaching and learning in the Indian context.

Keywords

Classroom Management Learning Parallel analysis Higher education 

References

  1. Bearison, D. J., & Dorval, B. (2002). Constructive features of collaborative cognition collaborative cognition: Children negotiating ways of knowing (pp. 117–121). Westport, CT: Ablex.Google Scholar
  2. Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley & Sons.CrossRefGoogle Scholar
  3. Brennan, R., & Pearce, G. (2009). Educational drama: A tool for promoting marketing learning? International Journal of Management Education, 8(1), 1–9.Google Scholar
  4. Cho, E., & Kim, S. (2015). Cronbach’s coefficient alpha: Well known but poorly understood. Organisational Research Methods, 18, 207–230.CrossRefGoogle Scholar
  5. Christensen, C. R., & Hansen, A. J. (1987). Teaching and the case method. Boston, MA: Harvard Business School Press.Google Scholar
  6. Duff, A. H., Rogers, D. P., & Harris, M. B. (2006). International engineering students: Avoiding plagiarism through understanding the western academic context of scholarship. European Journal of Engineering Education, 31(6), 673.CrossRefGoogle Scholar
  7. Esteve, J. M. (2000). The transformation of the teachers’ role at the end of the twentieth century: New challenges for the future. Educational Review, 52(2), 197–207.CrossRefGoogle Scholar
  8. Garcia, T., & Pontrich, P. R. (1996). The effects of autonomy on motivation and performance in the college classroom. Contemporary Educational Psychology, 21(4), 477–486.CrossRefGoogle Scholar
  9. Gönül, F. F., & Solano, R. A. (2013). Innovative teaching: An empirical study of computer-aided instruction in quantitative business courses. Journal of Statistics Education, 21(1), 1–23.CrossRefGoogle Scholar
  10. Govindasamy, T. (2002). Successful implementation of e-learning pedagogical considerations. The Internet and Higher Education, 4, 287–299.CrossRefGoogle Scholar
  11. Hammond, J. S. (2002). Learning by the case method Case No. 9-376-241. Boston, MA: Harvard Business School Press.Google Scholar
  12. Hetzel, R. D. (1996). A primer on factor analysis with comments on patterns of practise and reporting. In B. Thompson (Ed.), Advances in social science methodology (Vol. 4, pp. 175–206). Greenwich, CT: JAI.Google Scholar
  13. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.Google Scholar
  14. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.CrossRefGoogle Scholar
  15. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
  16. Jain, A. K. (2005). Management education and case method as a pedagogy. Vikalpa, 30(1), 77–84.CrossRefGoogle Scholar
  17. Jakka, S. R., & Mantha, S. R. (2012). Case study method of teaching in management education. Journal of Business Management & Social Sciences Research, 1(3), 13–16.Google Scholar
  18. Jamovi Project. (2018). Jamovi (Version 0.9.2.8). Retrieved from https://www.jamovi.org
  19. Klopfer, E., Osterweil, S., Groff, J., & Haas, J. (2009). The instructional powers of digital games, social networks and simulations and how teachers can leverage them (p. 23). Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
  20. Kop, R., Fournier, H., & Mak, J. S. F. (2011). A pedagogy of abundance or a pedagogy to support human beings? Participant support on massive open online courses. The International Review of Research in Open and Distance Learning, 12(7), 74–93.CrossRefGoogle Scholar
  21. Kulathuramaiyer, N., & Maurer, H. (2008). Learning ecosystems for dealing with the copy-paste syndrome. Journal of Research in Innovative Teaching, 1(1), 1–23.Google Scholar
  22. Matsunaga, M. (2010). How to factor analyse your data right: Do’s, dont’s and how-to’s. International Journal of Psychological Research, 3(1), 97–110.CrossRefGoogle Scholar
  23. Maurer, H., Kappe, F., & Zaka, B. (2006). Plagiarism: A survey. Journal of Universal Computer Science, 12(8), 1050–1084.Google Scholar
  24. McDonald, R. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  25. Parson, K. (2017, June 7). The ultimate history of technology in education. Retrieved November 16, 2018, from http://www.ourict.co.uk/technology-education-history/
  26. Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in healthcare research. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  27. Raymond, E. (1996). The use of cases in management education Case No. 9-376-240. Boston, MA: Harvard Business School Publishing.Google Scholar
  28. Revelle, W., & Zinbarg, R. (2009). Coefficients alpha, beta, omega, and the glb: Comments on Sijtsma. Psychometrika, 76, 145–154.CrossRefGoogle Scholar
  29. Schwartz, D., & Fajardo, C. (2008). Adding voice/visual interaction to online classes. Journal of Research in Innovative Teaching, 1(1), 145–157.Google Scholar
  30. Shapiro, B. (1985). Hints for case teaching Case No. 9-585-012. Boston, MA: Harvard Business School Publishing.Google Scholar
  31. Shear, L., Novais, G., & Moorthy, S. (2010). Innovative teaching and learning research: Executive summary. Menlo Park, CA: SRI International.Google Scholar
  32. Tserendorj, N., Tudevdagva, U., & Heller, A. (2012). Integration of learning management system into university-level teaching and learning. Chemnitz, Germany: Chemnitz University of Technology.Google Scholar
  33. Weber, S. (2006). Das Google-copy-paste-syndrome (2008th ed.). Hannover: Heise Heinz.Google Scholar
  34. Young, M. R. (2005). The motivational effects of the classroom environment in facilitating self regulated learning. Journal of Marketing Education, 27(1), 25–40.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2020

Authors and Affiliations

  1. 1.International Management InstituteBhubaneswarIndia

Personalised recommendations