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Research in Engineering Education Symposium 2011 - rees2009

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Universidad Politécnica de Madrid (UPM) Pág<strong>in</strong>a 588 de 957<br />

Improv<strong>in</strong>g the Learn<strong>in</strong>g Process <strong>in</strong> Statistical Decision-Mak<strong>in</strong>g Laboratory<br />

Javier Portillo<br />

javierp@grpss.ssr.upm.es<br />

Universidad Politécnica de Madrid, Madrid<br />

Spa<strong>in</strong><br />

Juan Besada<br />

besada@grpss.ssr.upm.es<br />

Universidad Politécnica de Madrid, Madrid<br />

Spa<strong>in</strong><br />

Ana Bernardos<br />

abernardos@grpss.ssr.upm.es<br />

Universidad Politécnica de Madrid, Madrid<br />

Spa<strong>in</strong><br />

Abstract: Statistics has been traditionally a difficult subject to teach and learn.<br />

Even the students with good mathematical skills may have problems <strong>in</strong><br />

understand<strong>in</strong>g the ma<strong>in</strong> pr<strong>in</strong>ciples of randomness, distribution functions and<br />

statistical significance. Additionally, it is not strange that students which have a<br />

good understand<strong>in</strong>g about probability theory does not know how to use it <strong>in</strong> the<br />

statistical problem (i.e. once a random sample has been obta<strong>in</strong>ed, and the aim is<br />

to <strong>in</strong>fer the values of parameters or distribution functions which better model<br />

the obta<strong>in</strong>ed data, <strong>in</strong> order to make decisions based <strong>in</strong> this <strong>in</strong>formation). This<br />

paper proposes a work <strong>in</strong> progress to improve the student understand<strong>in</strong>g and<br />

<strong>in</strong>volv<strong>in</strong>g <strong>in</strong> the process of statistical decision mak<strong>in</strong>g, participat<strong>in</strong>g <strong>in</strong> a<br />

statistical decision mak<strong>in</strong>g laboratory. The method is based <strong>in</strong> the follow<strong>in</strong>g<br />

pr<strong>in</strong>ciples:<br />

• The use of real economic data <strong>in</strong> order to predict the behavior of economic<br />

<strong>in</strong>dexes, allow<strong>in</strong>g the students to compare the real results with the results of the<br />

laboratory work. This comparison should be realized <strong>in</strong> a formal way, us<strong>in</strong>g<br />

decision theory pr<strong>in</strong>ciples (hypothesis test methodology).<br />

• The use of the same statistical tools learned <strong>in</strong> the course <strong>in</strong> order to analyze<br />

and compare the results presented by the students. That is to say, if the numeric<br />

results from different students or student groups are very sparse, this fact<br />

perhaps <strong>in</strong>dicates a por work def<strong>in</strong>ition by the teacher. If the results obta<strong>in</strong>ed<br />

are very homogeneous but are far from the real world results, this perhaps<br />

<strong>in</strong>dicates a common basic problem <strong>in</strong> the presentation of the theory and the<br />

laboratory work to the students. This is an important po<strong>in</strong>t, s<strong>in</strong>ce the students<br />

use the tools used <strong>in</strong> the laboratory to evaluate the performance of their<br />

laboratory results, and to propose improvements, which is a clear motivation<br />

po<strong>in</strong>t.<br />

Proceed<strong>in</strong>gs of <strong>Research</strong> <strong>in</strong> Eng<strong>in</strong>eer<strong>in</strong>g <strong>Education</strong> <strong>Symposium</strong> <strong>2011</strong><br />

Madrid, 4 th - 7 th October <strong>2011</strong>

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