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How novices formulate models. Part I: qualitative insights and implications for teaching

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Journal of the Operational Research Society

Abstract

Teaching novices how to formulate mathematical models for ill-structured problems is a challenging task. Little is known about how novices approach ill-structured problems and how their performance differs from that of experts. We audiotaped 28 MBA students while they worked through four ill-structured modelling problems. The task in each problem was to begin to develop a model that could ultimately be used for forecasting or analysis of alternative courses of action. We analysed transcripts of these think-aloud protocols both quantitatively and qualitatively. We observed five behaviours that are not typical of experts and that limit the effectiveness of our subjects. These include: over-reliance on given numerical data, taking shortcuts to an answer, insufficient use of abstract variables and relationships, ineffective self-regulation, and overuse of brainstorming relative to structured problem solving. We conclude that an effective modelling pedagogy should teach how to: formulate models both in the presence and the absence of data, abstract variables and relationships, employ control strategies for self-regulation, and use structured problem-solving methods.

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References

  • Atman CJ and Bursic KM (1998). Verbal protocol analysis as a method to document engineering student design processes. J Eng Educ 87: 121–132.

    Article  Google Scholar 

  • Atman CJ, Chimka JR, Bursick KM and Nachtman HL (1999). A comparison of freshman and senior engineering design processes. Des Stud 20: 131–152.

    Article  Google Scholar 

  • Chi MTH, Feltovich PJ and Glaster R (1981). Categorization and representation of physics problems by experts and novices. Cogn Sci 5: 121–152.

    Article  Google Scholar 

  • Clement JJ (1998). Expert novice similarities and instruction using analogies. Int J Sci Educ 20: 1271–1286.

    Article  Google Scholar 

  • Crismond D (2001). Learning and using science ideas when doing investigate-and-redesign tasks: A study of naïve, novice, and expert designers doing constrained and scaffolded design work. J Res Sci Teach 38: 791–820.

    Article  Google Scholar 

  • Glaser R (1990). Expert knowledge and the thinking process. Chemtech 20: 394–397.

    Google Scholar 

  • Glaser R and Chi MTH (1988). Overview. In: Chi MTH, Glaser R and Farr M (eds). The Nature of Expertise, XV-XXVIII. Lawrence Erlbaum Associates: Hillsdale: NJ.

    Google Scholar 

  • Heyworth RM (1999). Procedural and conceptual knowledge of expert and novice students for the solving of a basic problem in chemistry. Int J Sci Educ 21: 195–211.

    Article  Google Scholar 

  • Morris MT (1967). On the art of modelling. Mngt Sci 13: B-707–B-717.

    Article  Google Scholar 

  • Pidd M (1996). Tools for Thinking. Wiley: Chichester, UK.

    Google Scholar 

  • Polya G (1945). How to Solve It. Princeton University Press: Princeton, NJ.

    Google Scholar 

  • Powell SG (1995a). Teaching the art of modelling to MBA students. Interfaces 25: 88–94.

    Article  Google Scholar 

  • Powell SG (1995b). Six key modelling heuristics. Interfaces 25: 114–125.

    Article  Google Scholar 

  • Powell SG (1998). The studio approach to teaching the craft of modelling. Ann Opns Res 82: 29–47.

    Article  Google Scholar 

  • Reitman W (1965). Cognition and Thought. Wiley: New York.

    Google Scholar 

  • Savelsbergh ER, DeJong T and Ferguson-Hessler MGM (2002). Situational knowledge in physics: The case of electrodynamics. J Res Sci Teach 39: 928–951.

    Article  Google Scholar 

  • Schoenfeld A (1985). Mathematical Problem Solving. Academic Press: New York, NY.

    Google Scholar 

  • Schoenfeld A (1992). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. In: Grouws D. (ed). Handbook for Research on Mathematics Teaching and Learning. MacMillan: New York: NY. pp 334–370.

    Google Scholar 

  • Schön DA (1983). The Reflective Practitioner. Jossey-Bass: San Francisco, CA.

    Google Scholar 

  • Schön DA (1987). Educating the Reflective Practitioner. Jossey-Bass: San Francisco, CA.

    Google Scholar 

  • Simon HA (1973). The structure of ill-structured problems. Artif Intell 4: 181–201.

    Article  Google Scholar 

  • Slotta JD, Chi MTH and Joram E (1995). Assessing students' misclassifications of physics concepts: An ontological basis for conceptual change. Cognition Instruct 13: 373–400.

    Article  Google Scholar 

  • Voss JF and Post TA (1988). On the solving of ill-structured problems. In: Chi MTH, Glaser R and Farr MJ (eds). The Nature of Expertise. Lawrence Erlbaum Associates: Hillsdale: NJ. pp 261–285.

    Google Scholar 

  • Willemain TR (1994). Insights on modelling from a dozen experts. Opns Res 42: 213–222.

    Article  Google Scholar 

  • Willemain TR (1995). Model formulation: What experts think about and when. Opns Res 43: 916–932.

    Article  Google Scholar 

  • Willemain TR and Powell SG (2007). How novices formulate models Part II: A quantitative description of behaviour. J Opl Res Soc (forthcoming, 2007).

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Acknowledgements

We thank the Tuck MBA students who volunteered for this study. Special thanks also go to Professor Zeynep Aksehirli of the Tuck School of Business for conducting the exercises and helping to code the transcripts. Transcripts of the verbal protocols are available to interested researchers from the authors.

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Correspondence to S G Powell.

Appendix A. Problem statements

Appendix A. Problem statements

Boeing versus Airbus

The Boeing Company faces a critical strategic choice in its competition with Airbus Industries for the long-haul flight segment: should it design and build a super-747 model that can carry 550 passengers at speeds around 350 mph, or a plane that can fly at 95% of the speed of sound but carry only about 350 passengers? As a member of Boeing's Planning Group, your task is to build a model to investigate the trade-offs involved in this decision.

Red Cross

The Red Cross provides about 40% of the replacement blood supply for the United States. The available donor base has been shrinking for years, and although increased advertising has kept Red Cross supplies adequate, the time is approaching when demand will outstrip supply. For many years, the Red Cross has refused to pay donors for blood, on the grounds that to do so would ‘put the blood supply of the country at risk.’ However, Red Cross management has begun to consider changing its policy. Evaluate the impacts of a policy under which the Red Cross would pay each of its donors a set fee.

Alumni Giving

Your client is the planning office of a major university. Part of the job of the planning office is to forecast the annual donations of alumni through the university's long-established giving programme. Until now, the forecast has been made subjectively. The client wants you to develop a more objective approach. Your client has provided the attached sample of one of the annual reports produced by the planning office. The reports show, for each alumni class in that year:

  • number of alumni in class (Class Roll),

  • number of givers,

  • per cent of class giving,

  • total direct donations from class,

  • other gifts for class (eg, employer matching), and

  • class total.

Your client has also provided the attached graph of a recent year's class totals. Develop a model to estimate how much money the university's alumni will donate in each of the next 5 years.

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Powell, S., Willemain, T. How novices formulate models. Part I: qualitative insights and implications for teaching. J Oper Res Soc 58, 983–995 (2007). https://doi.org/10.1057/palgrave.jors.2602275

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  • DOI: https://doi.org/10.1057/palgrave.jors.2602275

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