Newsletter of the Section on Statistical Education Section of the American Statistical Association

Contents of Volume 3 Number 2:

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

I am looking forward to seeing many of you in Anaheim. Roxy Peck has done a wonderful job putting together the Statistics Education program; see the article on page 3 for more details. Jerry Moreno has planned so many roundtable luncheons related to teaching statistics that they had to be divided between two days! The business meeting of the Section on Statistical Education will be from 6:00 p.m. to 7:30 p.m. on Wednesday, August 13 in the Palos Verdes Room A at the Hilton. Jerry Lyons at Springer has generously offered to provide refreshments for our meeting, so this will be a mixer in addition to a business meeting.

I would like to take this opportunity to update you on a project important to me -- the Journal of Statistics Education (JSE). JSE is an electronic journal devoted to post-secondary teaching of statistics. We published our first issue in 1993, making us the first electronic journal in statistics.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

The Stat Ed Section has an exciting and very full program planned for the Joint Statistical Meetings in August. Plan on staying an extra day to see Disneyland, because there is at least one Stat Ed sponsored session in every time slot!

We have four invited sessions planned. On Sunday, August 10 at 4:00 p.m. we have a session titled "Capstone Experiences in the Undergraduate Statistics Curriculum" that will take a look at three different approaches to providing a statistics capstone experience for students majoring in statistics. On Monday morning from 9 a.m. - 11 a.m. we will be sponsoring one of two special invited poster sessions. This is a new format for the ASA, and the Stat Ed session will feature externally funded projects in statistics education. The posters will address both the project itself and the funding process. Rounding out the invited program are a session chaired by Sandy Weisberg on "Using Graphics to Teach Statistics and Statistics to Teach Graphics" on Tuesday, August 12 from 10:30 p.m. - 12:20 p.m. and on Wednesday, August 13 from 8:30 a.m. - 10:20 a.m. an invited panel titled "What I Did/Didn't Learn in School and How I Have/Haven't Used it in My First Few Years in Industry".

In addition to the invited program, there are also three special contributed sessions. Carl Lee has organized a session featuring the winners of the Innovative Programs Using Technology Competition. Innovations in teaching introductory and general education statistics courses will be the theme of a session organized by Neil Schwertman, and Peter Bruce has organized a contributed panel on the use of resampling and simulation in the AP statistics course.

The regular contributed program is also full of interesting sessions. There are eight in all: Student Attitudes and Performance; Using the Internet, Spreadsheets, and Software in Statistics Courses; Techniques for Teaching Mathematical Statistics; Visualizing Concepts; Integrating Projects, Problems, and PC's into Introductory Statistics Courses; Teaching Non-Traditional Students; Statistics Programs: Past and Present; and Stat Ed Poster Session

As I said--it is a full program. In addition to the sessions described above, the Stat Ed Section is also co-sponsoring a number of particularly relevant sessions organized by other sections. So, I hope that we will see you at the Stat Ed sessions in Anaheim. And when you register, don't forget to check out the Stat Ed Roundtable Luncheons. This year they are scheduled over two different days so you can even fit in two! Jerry Moreno has done a great job of selecting discussion topics, so even with the opportunity to pick two, it will still be hard to choose.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

It really doesn't matter. It doesn't matter that the use of statistics has exploded in the area of computer technology, allowing data to be processed at a speed that was never imagined a short time ago. It doesn't matter that every scientific area is now incorporating statistics in a manner that has brought significant changes to the core of statistical studies. And it really doesn't matter that technology has also changed the very nature of the classroom in that now you can teach a course to students who are in another location.

All of it doesn't matter to the very essence of being a good teacher. Certainly, a good teacher may incorporate all these advancements into the classroom, at the same time keeping up with developments in the practice of statistics in a changing world. In the end, however, good teaching really involves the attitude of being there simply to help students learn and using all your resources to be prepared to meet their needs.

That, in essence, is the hope and practice of David S. Moore, the Shanti S. Gupta Distinguished Professor of Statistics at Purdue University and President-Elect of the American Statistical Association.

Moore received his A.B. from Princeton and the Ph.D. from Cornell, both in mathematics, and has written many research papers in statistical theory and served on the editorial boards of several major journals. He is the author of several leading texts, including The Practice of Statistics (co-authored with George McCabe). Moore has served as president of the International Association for Statistical Education, and has received the Mathematical Association of America's national award for distinguished college or university teaching of mathematics.

It is easy to understand why he has been so honored when reviewing his attitude about the goals of teaching. "The hallmark of good teaching should first of all reflect the changing state of the subject," Moore said. "The instruction should be based on the current state of the subject matter."

But it involves more than that, he added. It involves the effort to become prepared to teach in a way that students will not only learn, but change any negative attitudes they have about the subject. The key is not just delivery, but involvement.

In his computer class, for example, he insists that the students do the work right along with him. And if he is teaching a class that is not strictly theory, he prepares good examples with interesting data that comes from real situations, allowing students to see that the material is, indeed, practical in the "real" world.

"The key is preparation," he said. "I strive to be clear and attentive to the students. I take time to think through what I want to do and preparing. Anyone who wants to can be a good teacher. Poor teaching is simply not caring enough to do thorough preparation."

Moore shows students on the introductory level that learning statistics is a good idea by showing them the practical side of the science. "In the introductory level we work with data that is more enjoyable for students. I like to hear students say, 'I didn't think I could do it--but I could do it!' Because most need to take statistics but they don't see it as an important tool."

"So in the introductory course I try to change students' attitudes," he said. He does this, partly, by showing a video about once a week with "real people using real data."

Moore was also the content developer for the Annenberg/ Corporation for Public Broadcasting college-level statistics telecourse and for a series of video modules intended to aid the teaching of statistics in schools. He sees both benefit and caution in what technology has brought to the teaching of statistics. "The illustration of statistics is changing the attitude toward statistics," he said. With graphical, multi-media software "we can ask students to manipulate graphics, respond to questions, and students can control the pace. It brings a lot of control and a lot of interaction."

But this new advancement has brought some major rethinking and studying of what core statistical skills will be for future students. "One thing that is happening is that more people are needing quantitative skills. And we are always getting a new set of students."

"But the content of the graduate level courses has also changed tremendously," he added. "Is it fast enough? In the past, the essential preparation was mathematics...but now it is more and more essential to know computer science. But they still need math." He said "Usually these things are resolved over time. But it is difficult because things are changing so quickly. The core is going to be still open to question."

"The future of the discipline is up in the air. The impact of technology is so great. Pharmaceutical studies, molecular genetics...they all require specialized knowledge, and statisticians are moving more apart from each other. It is going to become more and more difficult to understand what we mean when we say the 'field' of statistics."

But all the future changes will still require one thing: teachers that truly care that their students learn what they need to succeed. What does Moore hope students will remember about him? "That he was always prepared, that he cared that we learned," he said.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

1. Background:

   As of June, 1997, I will have been at the University of Iowa for 50 years. During the first three years of those fifty, I served as a graduate assistant at what was then called the State University of Iowa (SUI). With the name Hogg, people often observed that they could see why I was attracted to SUI, due to that pig called "sooo-eee". However, with my Ph.D. in hand, I became an Assistant Professor of Mathematics in 1950. Later in 1965 we started the Department of Statistics and Actuarial Science. While the Actuarial Science in the name was added later, we always included the actuaries in this new department, and that has been a very satisfactory "marriage".

   In addition to being the Executive Officer of Iowa's department for 19 years, I have visited many other Departments of Statistics. I have learned a few things, some from the good and some from the evil, namely the various mistakes, many of those being mine. Finally, during the period January-May, 1997, I visited statistics units at 14 universities; some of these were in Industrial Engineering, Business Colleges, and Mathematics, but most were Departments of Statistics. While these observations are fresh in my mind, I decided to write two reports: the first dealing with Statistics Programs and a second concerning Continuous Quality Improvement in Higher Education, which will appear elsewhere.

   Before focusing on statistics programs, let me address a feeling that I have about statisticians and the statistical profession. Frankly, I do not find enough cooperation among the members of our community. Are we really supportive and flexible enough when the opportunities arise to help other statisticians? These might involve visits to other universities, special research or teaching opportunities, or evaluations of grant proposals. Do we really reach out to help? I guess that I want us to be a family of statisticians working together for the good of the professions. We should want to share information, benchmark other programs, and recruit more young people as professional statisticians. Moreover, we should do much more to sell statistics to the general public. Statisticians can be collaborator (even leaders) on major projects, and yet very few have any notion that this is possible. Instead many of us are faced with a reward system that almost forces us to be loners; certainly the tenure system is laden with fear.

   The American Statistical Association (ASA) should be, and is to some extent, addressing some of these concerns. However, we need much more substantial efforts at a national level to achieve the necessary progress. A short session involving departmental chairs and heads at our annual meeting is not enough to discuss seriously major concerns about the directions of our profession. Let us do as many other professions do and get key people together at least once per year and brainstorm about appropriate actions that will benefit the profession. Statistics has been fairly strong in the past and super for me personally for 50 years; I only hope that the young people of today can have that same viable option that I had fifty years ago.

2. Present Graduate Programs:

   Various statisticians can come up with different ideas about the nature of statistics, but I think most of us would agree that the following is close to what we want to do in the practice of statistics: (a) Create measures for problems under consideration; (b) Collect data through surveys, experimentation, or observations recognizing that some uncertainty exists in these data; (c) Analyze the data and provide information, again with an element of uncertainty; (d) Prepare a report with recommendations, beginning with a brief executive summary (that is, KISS: Keep It Simple Statistician).

   It has been my observation that we spend most of the time in our academic programs on point (c). The usual Applied M.S. program is something like this: 2 or 3 courses in probability and mathematical statistics; 3 or 4 courses in regression, design and analysis of experiments, and multivariate analysis. These courses include response surfaces and computing (that is, the use of some statistical software); 3-5 electives from time series, nonparametric statistics, data analysis, sampling, statistical quality control, consulting, and categorical data. A more theoretical M.S. program for Ph.D. students would contain some mathematical analysis and an advanced probability course or two in place of some of the electives.

   Most universities with statistics departments have created some sort of statistical consulting center; such a center provides valuable experience for the students. I believe that these centers should be more aggressive and provide experiences for more students than they do at present. Also most statistics departments seemed to have reasonable computing facilities, but these must be improved continuously as advances are made in technology.

   It is clear that not enough is done to recruit students (particularly Americans) to our profession. While this might be a job for ASA, each of our departments can contribute some to this effort by visiting nearby undergraduate programs in math (or even high schools). Each such effort helps, even though most of us would be thinking primarily of our own department. It certainly must concern all of us to see the closing of an occasional department of statistics in this country. We believe that statistics is important, and we must attract a sufficient number of students. In my opinion, most of our programs are not flexible and modern enough. I will say more about this later because if we do not change, we will see more such closings.

3. Systems Approach:

   The department should decide on its mission, purpose, aim, goals etc., and have a team to design a curriculum to reflect these (see Section 7 on Curriculum Review). After consulting appropriate persons (students, alums, businesses, other faculty in and out of department, etc.) create a "core" for each desirable program.

   In particular, a Board of Advisors consisting of alumni and other influential friends might be most worthwhile. To maximize any system we must recognize that we are dealing with many interdependent parts, and we can not just try to maximize each of them. We really want to create a community of scholars, working together and recognizing that all of us do not have the same strengths or interests. Hence these cores reflect what we think best for the students in each of our programs. Some courses (core and elective) would possibly be team taught.

   My guess is that a first-year statistics core in graduate school will consist of some studies that deal with theoretical, applied, and computational skills. Possibly we have not stressed the latter enough in the past, but clearly we must consider the present and future technologies and take advantage of them. These include knowledge of excellent statistical software, spreadsheets, managing data bases, and data mining, in addition to being more concerned about the quality of these data bases. Others (computer science, electrical engineering, business, etc.) will be (or are) teaching these if we are not interested. With large data sets, nonparametric methods can be used to determine the "middles." There is still a concern about the variation (skewed, heavy tailed) and statisticians can help, if we will, about predictions concerning future observations.

   Certainly no one course should be "owned" by one professor if others are capable of teaching it. We might look forward to the day in which an expert in some subject who is at another university and teaches his/her specialty to those at other universities; this possibility is closer than most of us believe. In this regard, I find that we require too many courses for the Ph.D. degree. Beyond the Casella and Berger level, we need a good theory sequence, a Linear Model/Multivariate sequence, and a strong probability sequence. After that students can take electives, maybe given through Topics, possibly taught by one of these outside experts. These courses might include topics such as nonparametric regression (estimation and graphics in general), spatial statistics, computer intensive methods (particularly with Bayesian methods and resampling), empirical processes, non-linear dynamics, stochastic differential equations, and sequential methods (including meta analysis).

4. The Statistical Community:

   (a) Senior faculty should be mentors for junior faculty and graduate students in research and in teaching. Graduate students should, at least once per semester, receive some report on their progress, but this would be better given on a continuous basis.

   (b) Advanced graduate students should help the beginning students. It would be worthwhile to have a weekly seminar for all graduate students. Three or four students would report each week on topics appropriate for the levels of the students in that program. The faculty advisor would assign the topics (possibly with help of students) and require attendance of all students in the program. The graduate students would get to know each other so as to help one another and hopefully create a little "esprit de corps." Such a seminar would also help improve the "people skills" of the graduate students; this would also be true with involvement in the consulting service.

   (c) Faculty members should discuss their experiences in various courses with other instructors, particularly with those who follow teaching the same courses. It is important that we agree on topics in one course that is a prerequisite for another; otherwise the instructor of the second course has big problems.

   (d) We should ask for feedback from students (minute papers, punctuated lectures, quality teams reporting each week) and give them feedback on the feedback. Students cannot tell us what to teach, but they know when they are bored or confused. I am convinced that all of us want to be better teachers, and we should discuss among community members how to improve. As an example, would it be helpful to put notes on the web? The students would like this, but attendance might be worse than it is now in large lectures. (Note: I have found that providing students with solutions of quiz and test questions immediately afterwards is beneficial.) In general, there should be more interaction among students and their instructors. (See Hogg's "Continuous Quality Improvement in Higher Education" for more suggestions.)

   (e) Leaders should be helpful in facilitating the professional development of others. People want to feel good about themselves and their efforts; so real effective leaders should try to end discussions (some can be painful) on some kind of positive note. It never hurts to ask about a spouse or the children; such a personal interest lets the other person know that you care about him or her and his/her family. This is important (and I'm not always certain that I was real good at this in the past; I must improve).

   (f) To address some of these items (and others like the reward structure), an occasional retreat of the faculty might be very valuable. These (as well as other meetings) can be overdone, but sometimes they are needed to discuss seriously the goals of the unit and the best ways of achieving them. Maybe these retreats, usually held most successfully off- campus, would make department members feel more like being on one team that is an important part of the university.

   (g) The present reward and tenure structure is such that many tend to be more loyal to the profession than to the university. More should be done to interact with others on campus, possibly collaborating with faculty in other fields. Such interaction would make us feel as if we belong to the university community.

5. Partnerships in the Extended Community:

   We must search for these partners. As statisticians we have a certain advantage in cross-disciplinary activities as most researchers will collect data and will need these analyzed to get the maximum amount of information from them. These partners can be from our own campus, possibly resulting in joint research (grants, contracts, etc.) for faculty or cross- disciplinary theses (co-majors) for our students. Off-campus partnerships can lead to consulting, internships, or projects involving some unstructured problems. Often if these are close enough to campus, M.S. or Ph.D. theses could result from these involvements in substantial problems. Certainly stronger and more aggressive statistical consulting services with strong faculty involvement will help promote some of these partnerships. And these outside involvements certainly can not hurt, but almost always improve, the people skills.

   In this regard, I wish that some statisticians would be entrepreneurs. We must sell the value of statistical thinking to others. We must explain to others the power of statistics as being very supportive to good research involving the collection of data. Often examples and case studies could be useful in such situations, encouraging students in other fields to take more statistics. Of course, some of our Ph.D. students should be involved in cross-disciplinary research, possibly through co- majors with another area.

   Then too we can convince others that statisticians can help in their programs. For example, if a Business College is preparing students as Quality Managers with courses in human relationships, planning, and budgeting, appropriate statistical methods could also be most useful in such a program. The Japanese recognized the importance of the technical aspects in this area of quality improvement and used it. Moreover, there are many areas, in addition to Business, that need statistical help. Certainly joint appointments in these areas would be worthwhile in various situations. It has always been amazing to me why joint appointments are much more successful at some universities than others. Maybe it is due to different cultures or leadership.

   Of course, situations at some universities could call for efforts larger than helping a few individual programs. It could be that certain colleges (Medicine, Business, Education) have very little statistical help for their research. Depending upon the situation, the organization of a Statistical Institute (or Center) might be very appropriate. This might be difficult to sell such a unit, but then some of us should be entrepreneurs. Give it a try.

   Often a simple way to wave the statistical flag when there are enough statisticians in a Mathematics Department, say, is simply to rename the department as Mathematics and Statistics. Sometimes certain mathematicians object to this change. It is difficult for me to see why this is opposed as such a move would clearly help to recruit students to that department. Many departments have done this very successfully. As a matter of fact, it might be extremely helpful to work with the mathematicians and introduce a course in "Introduction to Mathematical Sciences" so that those with mathematical ability can see the possible options for them in the future.

6. Service Courses:

   In our profession, we have the opportunity to teach many service courses and we should try to find additional ones when appropriate. Since they are often our "bread and butter" activity (for graduate student support), we must try to improve them on a continuous basis. In particular, we must address how best to deal with the large lecture courses. None of us is really happy with the present situation. Yet we must recognize that we can not use TAs to teach smaller sections because this would defeat the university's mission to have more faculty in those freshman/sophomore courses. There are people in the profession that believe we should try to emphasize "statistical thinking" rather than recording lots of statistical techniques. Many instructors find that student projects truly help in this thinking. I'm inclined to agree with them, but I recognize that the big majority of students taking those courses simply want a grade (hopefully A or B) to satisfy a requirement rather than learn a little statistics. Maybe we should be satisfied if a few (perhaps the top 25%) understand our message and thus teach to them. Then tell the others how to get "that grade" by being able to "plug in" a few numbers in some formula. Maybe by doing the latter, the students will get some idea about the error structure of a statistic and thus understand why statisticians always put a "plus or minus" after our estimates. Nevertheless, I do believe that we should address the problem of large lectures (they are here to stay) and maybe a little brainstorming or benchmarking will help improve the situation somewhat. For example, in teaching statistics (as with mathematics and languages) the students can not miss the first 4 weeks and expect to pick up immediately as they might in history. This might suggest that we create modules so that different students can proceed at different speeds. After all, all do not learn in the same way. Our smaller and somewhat more advanced service courses in statistical methods and mathematical statistics are in better shape, but we should continue to check with our "customers" (other departments) to make certain that we are doing the best possible job.

7. Curriculum Review:

   I believe that in most statistics departments the curriculum has developed in a somewhat ad hoc fashion, and it is revised from time to time by making minor modifications of the previous plan. Most often the curriculum does not represent the department's goals, even in cases in which these are spelled out. Accordingly I would urge each statistics department, possibly in cooperation with other statistics departments, to assess seriously its curriculum. To help us do this, I have modified an outline that the Southeastern University and College Coalition for Engineering Education (SUCCEED) has created for engineering departments. This modification was made from an outline given in SUCCEED's Executive Summary, and its full report has not yet been finalized.

   (a) Strategic Planning. The members of the department should meet and discuss seriously the situation. Often this can best be done in a one-day retreat away from campus so that distractions such as phone calls can be eliminated. Hopefully the faculty can agree on such things as the purpose, mission, goals, and aims of the department. Does the present curriculum satisfy these? If not, some general principles could possibly be agreed upon and consideration given to guiding principles of the revision. Such a revision might consist of minor adjustment to the present program; but, on the other hand, it might involve a major re-engineering that, for example, might involve a team-taught core program for first-year graduate students (and possibly some very good undergraduate students). However, at the end of this phase, a decision would be made whether or not there is support to continue the consideration of a curriculum revision.

   (b) Preparation. If the decision is to continue, a Curriculum Design Team (CDT) should be formed. While input from junior faculty members, as well as others, is important, it is probably best not to have untenured faculty serving on the CDT. In analyzing the existing curriculum, it would be very important to get feedback from recent alumni in order to find out the present program's good and bad features. The CDT should also benchmark other existing programs that seem to be cutting edge of the profession. With as much background as possible on new and old areas, we can progress to designing a new curriculum.

   (c) The Design of the New Curriculum. With the information found in (b), some consensus process should be established to help the faculty select a new curriculum which reflects the goals of the department. As might be expected, "turf battles" might be fought, but the CDT and chair of the department would need to resolve these as well as possible. Once this is done course-specific issues should be addressed. At the conclusion of this work, an overall structure of the new curriculum will result with the identification of its component parts and the division of the subject material into course-sized segments.

   (d) Beginning of the New Curriculum. Once agreed upon, the chair and the faculty will begin to implement the new curriculum, with much depending upon the timing and funding of this new venture. Assuming the funding is available, the CDT will lay out a schedule that will implement the new program within two years. It should be understood, as the new curriculum is taught, that it should be continuously assessed and improved.

8. Conclusion:

   I am certain that I have not mentioned everything that should be looked at as we consider necessary changes. However, if we look at our department as a system, we might be able to utilize our members more effectively, even giving some larger teaching loads depending upon the abilities involved. Of course, different assignments would need to be taken into account in rewarding those individuals. We do need improvement, however, and some might enjoy reading my "Continuous Quality Improvement for Higher Education" along with this report;. I am hopeful for the future as we make appropriate changes.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

In December of 1996 ASA President Lynne Billard appointed a committee of seven ASA members to help advise the National Council of Teachers of Mathematics (NCTM) as it undertakes to update and revise the Standards that it produced for K-12 mathematics about a decade ago. The NCTM Standards comprise three volumes and describe standards for curriculum, evaluation, assessment, and instruction. ASA is one of several professional organizations in the mathematical sciences that have, at the request of NCTM, formed advisory groups for this standards update. The purpose of this article is to update you on our progress and to encourage those interested in the Standards to attend an "Open discussion meeting on NCTM Standards update" from 12:30 p.m. to 1:30 p.m., Wednesday, August 13, in the Santa Monica Room of the Hilton at the Joint Statistical Meetings.

During December and January, this ASA Advisory Review Group (ARG) developed a response to a set of 4 questions posed by NCTM regarding updating the Standards. These questions asked: (1) What is the proper "view" of mathematics that we should teach K-12 students? (2) Do the Standards convey a sense of consistency and growth in content themes as students move across grade levels? (3) Do the Standards adequately reflect the mathematical understanding of a student graduating in the 21st century?, and (4) How could an updated Standards blend the ideas described currently in the three sets of Standards?

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

While the American Statistical Association is primarily a professional organization, 1997 finds the ASA Center for Statistical Education continuing its commitment to bringing statistics to students, encouraging the use of statistics in the classroom, and promoting the value of statistical practices and methods to teachers and students alike.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

The Quantitative Literacy program has successfully affected the teaching of many mathematics teachers around the country. Now ASA is working with science teachers to enhance science education through the use of statistical ideas. Following the pattern of the QL program, the Science Education and Quantitative Literacy (SEAQL) project involves high school science teachers, middle school science teachers, and statisticians as leaders of workshops aimed at enhancing the preparation of high school and middle school science teachers.

Science students routinely collect large amounts of data that are used to answer specific questions. In a typical science class, each student completes a procedure and determines some sort of answer, for example, the density of a substance. Rarely are class data compared to anything other than an accepted value, as found in a reference book. SEAQL seeks to foster genuine exploration of data in science laboratory activities that promote a view of science as exploration and modeling, rather than only as confirmation of facts that are already known. We advocate using boxplots, median-fit lines, and related tools in the analysis of science data in order to place emphasis on discovery.

Teachers using SEAQL ideas might have each student add his or her data to a class stem-leaf diagram, which is then turned into a boxplot. The class can then discuss the data, noting the median and deviations from the median, outliers, skewness, and other features. This helps the students develop an appreciation of inherent variation, measurement bias, and accuracy. Indeed, experience suggests that students are more inclined to try to be accurate in their measurements when they know that their data will end up as part of a class boxplot -- no one wants to be an outlier!

In SEAQL workshops teachers are taught data analysis techniques using the Exploring Data QL book and are given experience using these techniques with data that are generated during the workshop. During the workshops, which last between two and four weeks, we conduct science labs in biology, physics, chemistry, earth science, and general science that are, for the most part, familiar to the teachers. We then use QL ideas in analyzing the data.

Other aspects of the workshops include instruction in the use of graphing calculators and calculator-based-lab equipment, such as a temperature probe for gathering data during a heat of reaction experiment, discussion of non- standard labs that teachers have used with success, group projects in which participants gather and analyze data of their own choosing, time for teachers to prepare lesson plans as they consider how they will use SEAQL in their science classes, and brief consideration of statistical aspects of experimental design.

In 1994 the SEAQL project received an NSF grant that runs through 1997. The first SEAQL workshop was held in 1995 at Johns Hopkins University. Two workshops were held during the summer of 1996 -- one at John Carroll University in Cleveland and one at San Jose State University in San Jose. In 1997 we are conducting workshops at Wesleyan University in Middletown, CT, and again at John Carroll University in Cleveland. We conduct follow-up sessions with the workshop participants during the academic year. Participants have reported considerable success in using SEAQL ideas in their classes and are very enthusiastic.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

(Stochastics is used here in the European sense as a generic term encompassing probability, statistics and combinatorics.)

This small group of about 20 people is formed of researchers with a special interest in psychological aspects of the teaching and learning of stochastics - a topic which has attracted increasing attention in recent years. Membership covers a remarkably large number of countries and language groups. It might be seen as essentially a focused subset of those people who receive the Newsletter of the International Study Group for Research on Learning Probability and Statistics.

After two years as a Discussion Group, the Group has developed into a Working Group with the aim of preparing a document looking at some form of codifying stochastics research. It is clear that newcomers to this field experience difficulty in accessing and evaluating the relevant material, and that much significant work not written in English is not as widely known as it deserves. It is also clear that our research is not having as much influence in the classroom as it should.

Plans for this document are still being developed. At the meeting of PME in Finland in July 1997, some possible approaches to preparing the document will be put forward for discussion. We want the document to be more than an annotated bibliography; we want it to link the literature together in a way which will make it more accessible to both researchers and teachers and which will provide an authoritative basis for further work. It will probably be structured around a small number of critical papers in each aspect of the subject. We anticipate that it will also help to identify those areas still in need of careful investigation.

The Group is also having talks with another PME Working Group - that on Advanced Mathematical Thinking. It is possible that we may contribute a chapter on Advanced Statistical Thinking to a book which that Group is planning. This represents a significant link between Mathematicians and Statisticians which we see as particularly valuable.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

The American Educational Research Association (AERA) is composed of 10 Divisions and more than 100 Special Interest Groups (SIGs) focusing on particular aspects of education. Within each Division, there may be as many as 6 sections addressing different aspects of the Divisional topic. Members of AERA belong to one or more Divisions and one or more SIGs. Most individuals conduct research in educational areas in academic arenas ranging from elementary to post- secondary and report the results of their research at the annual meeting of AERA. The researchers may be faculty members in Schools of Education in colleges and universities, teachers or administrators in school districts, staff of federally- and state-funded research centers, staff of large testing companies, or members of local, state, and federal departments of education. There is an international presence with researchers from many countries presenting results of their research in their home countries.

Division C (Learning and Instruction) has a section that has an interest in statistical education: Section 2 Mathematics. Within this section, the emphasis is on research on learning, instruction, and assessment including problem solving, concept and strategy growth and change, as well as psychological, social, and cultural factors in mathematics learning. A SIG that often cosponsors sessions with Division C is Research in Mathematics Education (RME). Recent sessions focusing on mathematics learning have addressed adolescent understanding of sampling in the context of a survey, visual manipulatives for propositional reasoning, exploring students' informal knowledge of statistics in the middle school, assessing mathematics knowledge with concept maps and interpretive essays, and mathematical thinking.

At the elementary-, middle-, and secondary-school levels of instruction, educators are concerned with the results of research using the National Assessment of Educational Progress (NAEP) data. For example, Sharon Bobbitt (National Center for Educational Statistics) reported on schools effects, teacher preparation, and their impact on students' math self- concept at the 1995 annual meeting, and Frank Jenkins' (ETS) NAEP mathematics attribution study was presented at the 1997 meeting in Chicago. The annual meeting of AERA also is held in conjunction with the National Council of Measurement in Education. During the joint sessions, as well as for each individual meeting, technical and theoretic aspects of the NAEP assessment are addressed, for example, innovative item types from a NAEP field test, evaluation of content validity, methods of evaluating differential item functioning and bias, and scoring of performance items.

Two other SIGs focus on statistical education at the post- secondary level or in higher education: Educational Statisticians (ES) and Professors of Educational Research (PER). The SIG-ES has had an emphasis on the teaching of statistics since its inception in the 1970s. The president of SIG-ES either organizes or designates a session on teaching statistics. Initially the concentration was on how specific statistical topics could or should be taught and on evaluating statistics texts used in education and psychology. More recently the focus has shifted to the learning of statistics and understanding and meeting the needs of the learner, for example, William Mickelson's (University of Idaho) presentation on bridging the gap between students and statistics addressing cognition, affect, and the role of teaching methods. The results of using Cognitive Apprenticeship models in statistics courses (John Willett, Judith Singer, Susan Prion, and Patricia Busk) have been reported at the 1995 annual meeting and cooperative learning groups (Robert Abbott) have been reported at the 1992 annual meeting.

Often members of SIG-ES and SIG-PER will collaborate on sessions to address issues relating to statistical learning. In 1994, one joint session dealt with using computers and computer applications to facilitate instruction and learning of statistics and research design. Sessions in New Orleans (1994), New York (1996), and Chicago (1997) have included multimedia instruction in introductory statistics courses (Gerard Giraud), Internet resources for teaching statistics (J. Laurie Snell and Joan Garfield), and technological advances (John Behrens, Paul Vellman, and Jan de Leeuw).

Frequently a student is treated only as someone to instruct rather than as a learner whose attitudes and affect must be considered. Recent sessions have attempted to correct this deficit: Kathy Green's (University of Denver) research on affective components of attitude and statistics instruction; Mathew Mitchell's (University of San Francisco) research on situational interest in the statistics classroom; Christine DiStefano and Paul Schutz's (University of Georgia) patterns of knowledge, attitude, and strategy use in an instruction to statistics class; and Joe Wisenbaker and Janice Scott's (University of Georgia) modeling aspects of students' attitudes and achievement in introductory statistics courses. Statistical and mathematical anxiety have long been topics of research for SIG-ES and SIG-PER.

Professor David Moore challenged individuals attending the 1997 SIG-ES business meeting with his invited address on Synergy in Statistics Education: Context, Pedagogy, Technology. I am certain that his message is well known to those in ASA's Statistical Education section; his message was welcomed by many attending the address and viewed with skepticism by those who still cling to the old ways of teaching. Professor Moore's talk provided insights into ways that new activities can foster a conceptual understanding of statistics.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

I have been asked to write about my personal experience attending the IASE Roundtable Conference on Research on the Role of Technology in Teaching and Learning Statistics held in Granada, Spain from July 23-27, 1996. My most vivid memory is of the warm summer evening when our wonderful hosts, Carmen Batanero and Juan Godino, took us to an outdoor cafe for tapas and then to a hill overlooking the city of Granada where we had more tapas. This same hill was the setting for a classical guitar concert arranged for us by the Mayor of Granada and presented in a beautiful outdoor garden.

Concerning the conference itself, my biggest surprise was to learn that there was a lot more going on, in the development of computer software for teaching statistics and research on its use, than I was aware of from attending meetings in the United States. This was a truly international meeting that showed that exciting things are going on in this field all over the world. We can thank the modern miracle of e-mail for the fact that Joan Garfield both knew exactly the right people to bring together and was able to bring them together in such a wonderful setting. Thanks again to modern technology you will soon be able to read the proceedings of this conference both in hard copy and on the Internet.

The participants were a wonderfully congenial group and by the time we left we were all best of friends. I will especially remember this conference as the place that I met Dani Ben-Zvi from the Weizmann Institute of Science. In addition to telling us about his interesting research using technology to teach Israeli junior high school statistics, Dani's good spirit and great questions brought out the best in us. When Dani realized that many of us were talking about, but not showing off, our software, he ran all over the University collecting Macs and PCs to put on a special "show and tell" session where we could show people how our technology really worked.

My final impression from the conference was that it is really difficult to decide how effective computer software is in teaching statistics. A lot of things we do to improve our teaching are based on our own interests and what we like. They are not the result of careful studies to determine their effectiveness. What we learned at this meeting is that such research is now being done and promises to give us some answers to the very hard question: "what works?". Editors' note: An edited collection of papers presented at the conference along with summaries of group discussions will be available soon. Contact Joan Garfield for more information.

Newsletter for the Section on Statistical Education
Volume 3, Number 2 (Summer 1997)

For a number of years, the ASA has been a co-sponsor of the high school American Mathematics Competitions. These competitions form the selection process for the United States Mathematics Olympiad team. The first in the series of exams is the AHSME (American High School Mathematics Exam) which is taken by over 600,000 self-selected high school students each year. The top performers on this exam are invited to participate in the AIME (American Invitational Mathematics Exam) from which the top group is invited to take a third exam, the AMO (American Mathematics Olympiad). On the basis of their performance on these exams, a six person team is selected to represent the United States in the International Mathematics Olympiad. A non-competitive examination is also run for junior high school students.

To match the recent trend in our high school mathematics programs, the competition committees would like to put more emphasis on statistics. To implement this, there is a need for creative and original statistics questions. Those involved in the competition are looking forward to the summer of 2001 when the United States will serve as host to the international competitions. Over the next several years, there will be a call by the host committee for support from persons and corporations.