 
Week 3 - Outcomes and Standards
|  | Statistics Can be Useful and Enjoyable! |
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MS KJ BYUN
New South Wales, Australia
ONE OF THE MOST difficult tasks facing the modern educator is that of providing material that is relevant and interesting, while at the same time being challenging. This is not always easy, since in many subject areas the students are only enrolled because they are required to, and have a preconceived notion that they are in for a particularly boring time. All they are really interested in is obtaining a passing grade at the end - regarding the learning of anything useful as only a remote possibility. This paper considers this aspect in the teaching of statistics, a subject area that virtually no university student does by choice in their first year but one which ends up with hundreds of majors. In particular, a description is provided of the way in which students at Macquarie University, in Sydney, are motivated to learn more about this fascinating field of study.
Although the education system is in a constant state of change, not always for the better, the task of presenting lecture material in an exciting manner remains the same. Students seem much less accepting these days that a particular topic is something they simply have to know, with the term 'boring' being used if the subject doesn't 'turn them on'. The ideal that universities are places for keen students to enter in search of knowledge through further study and research seems to have been lost along the way. For many students, the main reason for obtaining a degree is to gain employment and a good meal ticket.
The Power of Numbers
Twenty-five years ago the role of an academic was much more relaxed. Salaries were relatively good and student-staff ratios were at an acceptable level. But no more. With an increased number of students, and a decreased number of staff, the pressure is on academics to attract students, since their department budgets are directly linked to sheer numbers enrolled. If you don't attract the students, you don't get the money and your staff numbers decrease further. It's a merry-go-round.
As all will testify, despite the scramble to maintain student numbers, it is still important that academic standards be maintained. Sadly, this is not as easy as it might sound. Some students, desperate for a passing grade and a high grade point average, may well be attracted to courses they see as 'soft options', where the material is easy and failure is rare.
Juggling Standards and Shrinking Budgets
So what is the everyday academic to do in the face of this mounting tide of pressure in juggling standards and shrinking budgets? How does one attract students to a subject area that may have a reputation, maybe undeserved, of being inherently 'boring'? Each year huge number of students fail or drop out of their courses simply because they were not ready for, or could not cope with, the material being taught. In short, many were simply turned off by the whole university scene, with courses that were of little interest to them (especially when the course may not have been their first choice) or the amount of effort required.
Standards and Funding Tension
There are also those students who do not quite make the required cut-off mark to enter a tertiary institution and can avail themselves of the growing number of college type institutions that have deals with, or are part of, larger universities. In return for paying full fees, these students can study at the college and are, essentially, guaranteed entry into second year of university if they manage to pass. In particular, these colleges attract many international students who are keen to study at an Australian university but did not have the necessary marks for direct entry. These students also provide a rich source of much-needed funding.
It is inevitable that the role of universities and academics must change to meet the expectation of their prospective clients, as we progress into the 21st century. For many, universities are now simply an extension of high school, as opposed to a place of higher learning where the students have a thirst for scholarship and knowledge.
The Power of 'Compulsory'
The experience to which I can relate is that of teaching the largest subject at a tertiary institution in Australia. This is the unit in 'Introductory Statistics', which has some 2,500 students pass through its doors each year at Macquarie University, in Sydney. The reason for such a large crowd is not because of the subject's overwhelming popularity. Rather, it is the fact that the subject is a compulsory prerequisite for many other fields of study. Frankly, if this were not the case, it may well have the lowest enrolment of any first year subject!
Before this century the subject of 'statistics' was merely regarded as a branch of mathematics and very few people viewed it as being worthy of a subject in its own right. However, with the growing demise and increasing unpopularity of mathematics among students, the role of statistics is growing rapidly in importance.
Growing Importance of Statistics
Because of its practical nature, there is now a market demand for properly trained statisticians across a wide variety of fields. To quote H. G. Wells, 'Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write' (Griffiths, Stirling, Weldon, 1998). Perhaps, at the time, many saw this as just another piece of science fiction.
The measure of a good teacher is in making a subject that may well not be particularly exciting into one where the students will cry out for more. This is particularly true of statistics, where the audience is 'captive'. They would certainly not be there unless they were forced. Changing the negative attitude of the masses into a positive one requires great skill, time and dedication. Without this, the teacher is doomed to failure, as are many of the students.
Statistics are Useful
Fortunately, the subject of statistics lends itself to an array of practical examples to which many students can relate. It was a stroke of good fortune that the 2000 Olympics landed in Sydney, since the events provide a seemingly endless supply of data that are crying out to be analysed. Even to those students who have little or no interest in sport (and there aren't many), the examples manage to provide an attractive and welcome relief from the humdrum stereotype of statistical examples involving agricultural experiments and the like.
The first task of the students is to learn just what types of research questions can be asked. Then follows the appropriate data collection procedure, followed by a consideration of just what particular techniques are available for analysis. Once the statistical testing has been performed, the final step is to write up the results in a manner that can be readily understood, even by those who are essentially non-numerate.
Each of these chores is full of challenging and interesting questions in themselves. At the end of the day, the students should have a result that is not only of interest to them but one of which they can be proud. The learning process can be made fun, after all.
An initial question might be, given that Sydney was keen to have the Olympics in the first place, when would be the best time of the year to hold it? What are the practical considerations? A major factor here was going to be the weather, since many of the events are held outdoors. To this end, a little research utilising the resources of the Bureau of Meteorology shows that, in Sydney, September has less rain on average, and the least number of rainy days, than any other month. So the Olympics are held entirely in September 2000. It may also well be that weather conditions are far less variable in September, so that conditions may be far easier to predict.
But is rainfall the only criterion that should be used? Surely wind is also an important factor? If there were strong winds every day during athletics, this would be a problem on two fronts. If it were a tailwind, any record would be disallowed because of 'wind-assistance'. On the other hand, running into a healthy gale would ensure that no athletic records are broken. Perhaps then, wind considerations are just as important as rain for athletics. Even now, some athletes and members of the media are predicting such a scenario, not because of the weather, but due to the design of the Olympic Stadium itself leading to blustery and unpredictable windy conditions. Only time will tell as to whether these gloomy forecasts come to pass.
In any case, the Olympics will be held in September, and that is that. No changing the dates now! So what else might be of interest in the area of data analysis surrounding the Olympics? Some suggestions are listed below.
How fast will athletes have to run, jump or swim in order to win a gold medal? This is a topic that has long occupied researchers, coaches and athletes, but one which is very topical. It also provides an excellent opportunity for students to not only learn forecasting techniques but to make a comparison of their suitability. Moreover, they have the added excitement of seeing just how close they were to being correct.
In general, students are able to select their own events of interest, with the most popular including swimming and athletics. Previous Olympic data may be readily obtained from books, magazines and even the World Wide Web.
Is it possible to detect the effect of drug testing in certain events? Is there any evidence that suddenly certain events were not having their records broken as often as might be expected? Could some of the records set many years ago be regarded as 'suspicious'? Are individual athletes or swimmers suddenly performing way above their general ability for no apparent reason? The detection of 'outlying' results is an important part of statistical analysis.
How are we able to take advantage of modern computer software to assist in making appropriate statistical analysis? One of the most common packages is Microsoft Excel for Windows, which is used for a variety of tasks, including data entry, spreadsheets, prediction, graphs and the construction of confidence intervals. Familiarity with software such as this also serves the students well when they finally enter the workforce.
Is there a limit on the times that athletes and swimmers can perform? For example, is it ever likely that a human being can run 100 metres in eight seconds or less? Could they ever swim 1500 metres in under fourteen minutes? These types of questions can also be discussed, often in a lively fashion, by the use of basic statistical techniques.
Is it possible that women will surpass men in certain events? The most likely of these is in endurance sports, such as long distance swimming, where male and female times have edged closer together over the years. Once again, statistics plays a large role in trying to answer questions such as these.
Statistics is Not an Exact Science
There are many other types of statistical questions that can be considered using Olympic data that have served to motivate the students to get to the final answer. Unfortunately, statistics is not an exact science, so there is often no definite conclusion, only strong opinions. However, it does make for a lively debate.
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ABOUT THE AUTHOR
Ms KJ Byun is a lecturer in statistics in the Division of Economic and Financial Studies at Macquarie University. She specialises in the area of statistics in sport and is in charge of the sports and medicine sections of the popular first year unit 'Gambling, Sport and Medicine', which attracts hundreds of students each year across the university. KJ also plays an active role in the popular sequence of units in Operations Research where she teaches at both second and third-year level. In the year 2000 she undertook a new role as lecturer in charge of the new final year unit 'Logistics and Project Management' and is currently enrolled in a PhD in this field. She has acted as a consultant to major companies and her recreational activities include reading, dancing, bushwalking, tennis and squash.
She can be contacted by email at:
Zbyun@efs.mq.edu.au
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REFERENCE
Griffiths D, Stirling W D, Weldon K L. Understanding Data: Practical Practice of Statistics, 1998, p. 3
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