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Presentation 4H2. Statistics Education for Future Managers: Needs, Obstacles, Possible Solutions

Corinne Hahn (France)
Patrick Dassonville (France)


Presentation Abstract
When considering the general issue of training future managers in statistics, it might be useful to make a distinction between those who will work in industry and those who will work in general management.

Indeed, most managers do not deal with complex statistical issues in person. Yet, they must master the core concepts of statistics and be familiar with the main objectives of statistical methods. They should be in a position to 1- raise the right questions, and 2- analyse and interpret the solutions proposed by statisticians.

Thus, devising a training program for future managers is complex for the following reasons:

- It proves impossible to teach the theoretical bases of the statistical methods used in businesses, since most French management students do not master the necessary math. That can only amplify the "epistemological anxiety" (Wilenski, 1997) which students feel in front of concepts whose legitimacy they question.

- Moreover, it does not suffice to present examples of how statistical problems are solved in businesses to elicit the students' interest and allow them to become familiar with the concepts. It has long been known that it is difficult to bridge the gap between theory and realities in the field (Boaler, 1994, Hahn, 2000, Lave, 1998, Nunes, 1993).

- Lastly, the use of new technologies (Galbraith & Haines, 1998) adds to legitimacy-related concerns. Yet, these tools must be used in statistics. Under specific conditions, though, it has been observed that they do motivate students (Kulik, 1994).

Given the above-mentioned difficulties, some institutions of higher learning are content with presenting a few recipes which will hopefully allow students to interpret the output of "technological black boxes" (Keitel et al., 1993).

Even though the authors of this paper believe strongly that it is not necessary for future managers to master the mathematical aspects of statistics, they are convinced that it is not enough to teach students how to manage this relation of " input/output " (Keitel et al., 1993). Training in statistics must be adapted to the students' needs, in particular to help them make the right decisions in their future corporate environment.

Besides, the authors of this paper think it is necessary to implement new pedagogical approaches which address each student's difficulties and anxieties.

In the course of their presentation, the authors first show a few examples of the types of on-the-job problems future managers are likely to meet in such fields as marketing and strategy which illustrate how poor knowledge of statistical concepts and an inability to analyse statistical data can lead to grave mistakes.
Then, they explain how a field survey has led them to adjust the spirit and the contents of the program they offer, which stresses data analysis more than the fundamentals of the statistical inference.

They also review the difficulties which are specific to the teaching of statistics in schools of management. Over the past few years, these difficulties have led them to experiment with several systems which are based on information and communication technologies (Dassonville, 1997, Dassonville & Hahn, 2000).
Lastly, they present an assessment of the latest system they have devised and experimented with: an e-learning platform which in particular involves collective and individual synchronous tutoring

Boaler, J. (1994). 'The Role of Contexts in the Mathematics Classroom : Do they Make Mathematics More "Real" ?', For the Learning of Mathematics 13(2), 12-17.

Dassonville P. (1997). Learning correlation et PCA with a CD ROM, Bulletin of the International Statistical Institute, 51st session, Book 3.

Dassonville P., Hahn C. (2000). The Multimedia Tool : a transitional medium between the Mathematician's Culture and the Professional's Culture in teaching PCA in a Business School, in Cultural diversity in Mathematics Education, Ahmed, Kraemer and Williams eds, Horwood, United Kingdom.

Galbraith P., Haines C. (1998). Disentangling the Nexus : Attitudes to Mathematics and Technology in a Computer Learning Environment, Educational Studies in Mathematics, 36-3, pp 275-290.

Hahn C. (2000). Teaching Mathematics to Apprentices : Exploring Content and Didactical Situations, in Education for Mathematics in the Workplace, Annie Bessot and Jim Ridgeway eds, Kluwer, Netherland.

Keitel C., Kotzmann E., Skovsmose O. (1993). Beyond the Tunnel Vision, analysing the Relationship between Mathematics, Society and Technology, in Learning from Computers : Mathematics Education and Technology, Springer, Berlin.

Kulik J. A. (1994). Meta-analytic Studies of Findings on Computer-based Instruction, in Baker and O'Neil eds, Technology Assessment in Education and Training, Lawrence Erlbaum, Hillsdale, N.J.

Lave, J. (1988). Cognition in Practice: Mind, Mathematics and Culture in Everyday Life, Cambridge University Press, Cambridge.

Nunes, T., Schliemann A., & Carraher D.(1993). Street Mathematics and School Mathematics, Cambridge University Press, Cambridge.

Wilenski U. (1997). What is normal anyway ?, Educational Studies in Mathematics, 33-2, pp 171-202.


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