The Emergence of Student Models from an Analysis of Ethical Decision Making in a Scenario-Based Learning Environment

  • Mike Winter
  • Gord McCalla
Conference paper
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 407)


Too often, professional ethics issues are trivialized in software engineering education. To begin to remedy this situation, we have built two interactive, adaptive learning scenarios that place students in the role of a software project manager confronting many critical project decisions, each with an ethical dimension. As students move through a scenario, making and justifying their decisions, their behaviour can be monitored and used both to adapt the scenario to each student as they proceed, and in post hoc analysis to identify different classes of ethical behaviour. In this paper we discuss five different classes of student behaviour that emerged from the analysis of protocols collected during the use of these scenarios in a third year undergraduate software engineering class. We speculate that the existence of these general student models can be used in several ways to further enhance the learning of ethics


Ethical Decision Unethical Behaviour Student Behaviour Software Development Process Student Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abratt, R. N., Higgs, D. and Nicola, S. (1992). An examination of the ethical beliefs of managers using selected scenarios in a cross-cultural environment. Journal of Business Ethics, 11: 29–35.CrossRefGoogle Scholar
  2. Appel, F. (1998). Including the social and ethical implications of computing in the computer science curriculum. ACM Computers and Society, 28: 56–57.CrossRefGoogle Scholar
  3. Ascher, M. (1986). Ethical conflicts in the computing field — an undergraduate course. ACM Computers and Society, 16: 19–22.CrossRefGoogle Scholar
  4. Colby, A. and Kohlberg, L. (1987). The Measurement of Moral Judgement: Volume 1, Theoretical Foundations and Research Validation. Cambridge: Cambridge University Press.Google Scholar
  5. Gotterbarn, D. and Riser, R. (1997). Ethical activities in computer science course: goals and issues. ACM Computers and Society. 27: 10–15.CrossRefGoogle Scholar
  6. Kallman, E. A. and Grillio, J. P. (1996). Ethical Decision Making and Information Technology: An Introduction with Cases. New York: Mc-Graw Hill.Google Scholar
  7. Mislevy, R.J., and Gitomer, D.H. (1996). The role of probability-based inference in an intelligent tutoring system. User-Modeling and User-Adapted Interaction. 5: 253–282.CrossRefGoogle Scholar
  8. Reidenbach, E. R. and Robin, D. P. (1988). Some initial steps toward improving the measurement of ethical evaluations of marketing activities. Journal of Business Ethics. 7: 871–879.CrossRefGoogle Scholar
  9. Rich, E. (1979). User modeling via stereotypes. Cognitive Science. 3: 329–354.CrossRefGoogle Scholar
  10. Stevens, R.E., Harris, J. O. and Williamson, S. (1993). A comparison of ethical evaluations of business school faculty and students: a pilot study. Journal of Business Ethics. 12: 611–619.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Mike Winter
    • 1
  • Gord McCalla
    • 1
  1. 1.ARIES Laboratory, Department of Computer ScienceUniversity of SaskatchewanCanada

Personalised recommendations