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ABSTRACT

Empirical skills are playing an increasingly important role in the computing profession and our society. But while traditional computer science curricula are effective in teaching software design skills, little attention has been paid to developing empirical investigative skills such as forming testable hypotheses, designing experiments, critiquing their validity, collecting data, explaining results, and drawing conclusions. In this paper, we describe an initiative at Dickinson College that integrates the development of empirical skills throughout the computer science curriculum. At the introductory level, students perform experiments, analyze the results, and discuss their conclusions. In subsequent courses, they develop their skills at designing, conducting and critiquing experiments through incrementally more open-ended assignments. By their senior year, they are capable of forming hypotheses, designing and conducting experiments, and presenting conclusions based on the results.

REFERENCES

• 1: Owen Astrachan , David Reed, AAA and CS 1: the applied apprenticeship approach to CS 1, ACM SIGCSE Bulletin, v.27 n.1, p.1-5, March 1995
• 2: Chi, M.T.H., M. Bassock, M.W. Lewis, P. Reimann, and R. Glaser (1989). "Self-explanations: How students study and learn examples in learning to solve problems." Cognitive Science 13: 145-182.
• 3: Matthew C. Clarke, Teaching the empirical approach to designing human-computer interaction via an experiential group project, ACM SIGCSE Bulletin, v.30 n.1, p.198-201, Mar. 1998
• 4: Allen B. Downey, Teaching experimental design in an operating systems class, ACM SIGCSE Bulletin, v.31 n.1, p.316-320, March 1999
• 5: Miller, C., J. Lehman, and K. Koedinger (1999). "Goals and l~rning in microworlds." Cognitive Science 23(3).
• 6: Nhouyvanisvong, A., and K. Koedinger (1998). "Goal specificity and learning: Reinterpretation of the data and cognitive theory." Proceedings of the 20th Annual Conference of the Cognitive Science Society, Erlbaum: 764-769.
• 7: Richard E. Pattis, A philosophy and example of CS-1 programming projects, ACM SIGCSE Bulletin, v.22 n.1, p.34-39, Feb. 1990
• 8: Steven Robbins , Kay A. Robbins, Empirical exploration in undergraduate operating systems, ACM SIGCSE Bulletin, v.31 n.1, p.311-315, March 1999
• 9: G. Michael Schneider, Computational science as an interdisciplinary bridge, ACM SIGCSE Bulletin, v.31 n.1, p.141-145, March 1999
• 10: Sweller, J. (1998). "Cognitive load during problem solving: Effects on learning." Cognitive Science 12: 257-285.
• 11: Walter F. Tichy, Should Computer Scientists Experiment More?, Computer, v.31 n.5, p.32-40, May 1998 [doi>10.1109/2.675631]
• 12: Tucker, A.B., editor (1992). "Report of the ACM/IEEE-CS Joint Curriculum Task Force." The Association for Computing Machinery, New York.
• 13: Vollmeyer, R., B. Burns, and K. Holyoak (1996). "The impact of goal specificity on strategy use and the acquisition of problem structure." Cognitive Science 20: 75-100.
• 14: Joseph L. Zachary, The gestalt of scientific programming: problem, model, method, implementation, assessment, ACM SIGCSE Bulletin, v.29 n.1, p.238-242, March 1997
• 15: Marvin V. Zelkowitz , Dolores R. Wallace, Experimental Models for Validating Technology, Computer, v.31 n.5, p.23-31, May 1998 [doi>10.1109/2.675630]

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Curriculum

Additional Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Computer science education

General Terms:
Design, Experimentation, Human Factors, Management, Performance, Theory