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Economics Students’ Approaches to Learning in Different Assessment Regimes

Economics Students’ Approaches to Learning in Different Assessment Regimes . Tommy Tang & Tim Robinson Queensland University of Technology, Australia. Paper presented at the Third International Conference in Developments in Economics and Business Education, Cambridge, UK 1-2 Sept 2005.

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Economics Students’ Approaches to Learning in Different Assessment Regimes

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  1. Economics Students’ Approaches to Learning in Different Assessment Regimes Tommy Tang & Tim Robinson Queensland University of Technology, Australia Paper presented at the Third International Conference in Developments in Economics and Business Education, Cambridge, UK 1-2 Sept 2005.

  2. Outline • 1. Introduction • Input-output approach and its limitations • Biggs’ 3-P Model of Learning 2. The study 3. Findings & Discussions 4. Conclusion

  3. Introduction Input-output Approach Q = f(X1, X2, X3, … ) where Q = Learning output (marks) X1 = Aptitude X2 = Age X3 = Parental occupation … Limitations

  4. Biggs 3-P Model of Learning Presage Process Product Feedback Student characteristics Learning approach Learning outcome Teaching context Feedback (Biggs 1989, modified)

  5. “An econometric approach which assesses the impact of easily measurable independent variables on a single dependent variable may simply ‘miss’ important responses to teaching change.” (Shanahan et al., 1997)

  6. The Study • Research Objective: • Construct instruments to investigate • students’ perceptions of assessment, and • learning strategies for assessment Construction of Instrument: SPQ (Biggs), ASI (Entwistle & Ramsden), MSLQ (Pintrich et al), LAQ (Thomas) Focus groups Student surveys

  7. Subjects: • Students in: • Economics 1 • Economics 2 • Business cycles & economic growth • Sample size • Week 1: n = 1146 • Week 2: n = 648

  8. Findings & Discussions

  9. Perceptions of Assessment Exploratory Factor Analysis (EFA) MCQ Essay Assignment Essay Exam (KMO=0.747) (KMO = 0.805) (KMO = 0.793)

  10. Confirmatory Factor Analysis (CFA) The final model

  11. Learning Strategies for Assessment EFA KMO = 0.835

  12. CFA (2/df = 2.22, CFI = 0.949 and RMSEA = 0.034) The final model

  13. CFA – 2nd order factor model (2/df = 2.610, CFI = 0.938, RMSEA = 0.038 )

  14. Perceptions of Assessment

  15. Learning Approaches for Assessment

  16. Stability of Perceptions ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

  17. Pre-course factors Conclusion Implication for Future Research Assessment Approach (Stability)

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