Advertisement

The Influence of the Teaching and Learning Environment on the Development of Generic Capabilities Needed for a Knowledge-Based Society

  • David Kember
  • Doris Y. P. LeungEmail author
Article

Abstract

The effect of the teaching and learning environment on the development of generic capabilities was examined through a survey of 1756 undergraduate students at a university in Hong Kong. The survey assessed students' perceptions of the development of the six capabilities of critical thinking, self-managed learning, adaptability, problem solving, communication skills, and interpersonal skills and groupwork. Students were also asked to rate the quality of nine facets of the teaching and learning environment. Structural equation modelling was used to test a model of the influence of teaching on the nurturing of the six capabilities. The model grouped the nine facets of teaching and learning under the three higher-order latent variables of teaching, teacher–student relationship, and student–student relationship. The model showed a good fit to the data, indicating that the teaching and learning environment had a significant impact on the development of the generic capabilities while the students were taking their degree. The teaching latent variable had the strongest effect on the development of all six of the capabilities. A suitable teaching environment was characterised by a focus on understanding, the active participation of students in learning activities, a coherent curriculum, and assessment which focused on analytical skills and self-learning capability. Strong student–student relationships nurtured communication and interpersonal skills. There was a mutually reinforcing effect between the type of teaching, teacher–student relationships and student–student relationships.

Key Words

active learning assessment curriculum generic skills graduate capabilities structural equation modelling teacher–student interaction teaching and learning environment 

References

  1. Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modelling. Annual Review of Psychology, 31, 419–456.CrossRefGoogle Scholar
  2. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.CrossRefGoogle Scholar
  3. Bentler, P. M. (1995). EQS: Structural equations program. Encino, CA: Multivariate Software.Google Scholar
  4. Bligh, D. A. (1980). Methods and techniques in post-secondary education [Special issue]. Educational Studies and Documents, 31. Paris: UNESCO.Google Scholar
  5. Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley.Google Scholar
  6. Brown, G., & Atkins, M. (1988). Effective teaching in higher education. London: Routledge.Google Scholar
  7. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–161). Newbury Park, CA: Sage.Google Scholar
  8. Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Thousand Oaks, CA: Sage.Google Scholar
  9. Candy, P. C., & Crebert, R. G. (1991). Lifelong learning: An enduring mandate for higher education. Higher Education Research and Development, 10(1), 3–18.Google Scholar
  10. Chapman, A. (1999). Theoretical and practical integration of literacy and numeracy in a university academic programme. Teaching in Higher Education, 4, 363–382.Google Scholar
  11. Daly, W. T. (1994). Teaching and scholarship: Adapting American higher education to hard times. Journal of Higher Education, 65, 45–57.Google Scholar
  12. de la Harpe, B., Radloff, A., & Wyber, J. (2000). Quality and generic (professional) skills. Quality in Higher Education, 6, 231–243.Google Scholar
  13. Education Commission. (1999). Learning for life. Hong Kong Special Administrative Region: Author.Google Scholar
  14. Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136.Google Scholar
  15. Hayduk, L. A. (1987). Structural equations modeling with LISREL: Essentials and advances. Baltimore, MD: Johns Hopkins University Press.Google Scholar
  16. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
  17. Jackson, N. (2000). Programme specification and its role in an outcomes model of learning. Active Learning in Higher Education, 1(2), 132–151.CrossRefGoogle Scholar
  18. Jamshidian, M., & Bentler, P. M. (1998). A quasi-Newton method for minimum trace factor analysis. Journal of Statistical Computation and Simulation, 62, 73–89.Google Scholar
  19. Jaques, D. (1991). Learning in groups. London: Kogan Page.Google Scholar
  20. Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998). Active learning: Cooperation in the college classroom. Edina, MN: Interaction Book Company.Google Scholar
  21. Kember, D., Armour, R., Jenkins, W., Lee, K., Leung, D. Y. P., Li, N., Murphy, D., Ng, K. C., Siaw, I., & Yum, J. C. K. (2001). Evaluation of the part-time student experience. Hong Kong: The Open University of Hong Kong.Google Scholar
  22. Kember, D., & Leung, D. Y. P. (2005). The influence of active learning experiences on the development of graduate capabilities. Studies in Higher Education, 30, 155–170.Google Scholar
  23. Leckey, J. F., & McGuigan, M. A. (1997). Right tracks – wrong rails: The development of generic skills in higher education. Research in Higher Education, 38, 365–378.CrossRefGoogle Scholar
  24. Leung, D. Y. P., & Kember, D. (2005). The influence of the part-time study experience on the development of generic capabilities. Journal of Further and Higher Education, 29(2), 91–101.Google Scholar
  25. Longworth, N., & Davies, W. K. (1996). Lifelong learning. London: Kogan Page.Google Scholar
  26. Medlin, J., Graves, C., & McGowan, S. (2003). Using diverse professional teams and a graduate qualities framework to develop generic skills within a commerce degree. Innovations in Education and Teaching International, 40(1), 61–77.CrossRefGoogle Scholar
  27. Miller, M. B. (1995). Coefficient alpha: A basic introduction from the perspective of classical test theory and structural equation modeling. Structural Equation Modeling, 2, 255–273.Google Scholar
  28. Norusis, M. J. (2002). SPSS11.0 guide to data analysis. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  29. Oliver, R., & McLoughlin, C. (2001). Exploring the practice and development of generic skills through web-based learning. Journal of Educational Multimedia and Hypermedia, 10, 207–225.Google Scholar
  30. Pascarella, E. T., & Terenzini, P. T. (1991). How college affects students: Findings and insights from twenty years of research. San Francisco: Jossey Bass.Google Scholar
  31. Raykov, T. (1997). Scale reliability, Cronbach's alpha, and violations of essential tau-equivalence with fixed congeneric components. Multivariate Behavioral Research, 32, 329–353.Google Scholar
  32. Raykov, T. (1998). Coefficient alpha and composite reliability with interrelated nonhomogeneous items. Applied Psychological Measurement, 22, 375–385.Google Scholar
  33. Raykov, T., & Shrout, P. E. (2002). Reliability of scales with general structure: Point and interval estimation using a structural equation modeling approach. Structural Equation Modeling, 9, 195–212.CrossRefGoogle Scholar
  34. Satorra, A., & Bentler, P. M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. In Proceedings of the American Statistical Association (pp. 308–313). Alexandria, VA: American Statistical Association.Google Scholar
  35. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks, CA: Sage.Google Scholar
  36. Schmitt, M. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8, 350–353.CrossRefGoogle Scholar
  37. Shapiro, A. (1982). Weighted minimum trace factor analysis. Psychometrika, 47, 243–264.Google Scholar
  38. Tait, H., & Godfrey, H. (1999). Defining and assessing competence in generic skills. Quality in Higher Education, 5, 245–253.Google Scholar
  39. Yan, L. W. F. (2001). Learning out of the classroom: The influence of peer group work on learning outcomes. Unpublished PhD thesis, The Hong Kong Polytechnic University, Hong Kong.Google Scholar
  40. Yan, L., & Kember, D. (2003). The influence of the curriculum and learning envir- onment on the learning approaches of groups of students outside the classroom. Learning Environments Research, 6, 285–307.CrossRefGoogle Scholar
  41. Yan, L., & Kember, D. (2004a). Avoider and engager approaches by out-of-class groups: The group equivalent to individual learning approaches. Learning and Instruction, 14, 27–49.CrossRefGoogle Scholar
  42. Yan, L., & Kember, D. (2004b). Engager and avoider behavior in types of activities performed by out-of-class learning groups. Higher Education, 48, 419–438.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Centre for Learning Enhancement and ResearchThe Chinese University of Hong KongShatin

Personalised recommendations