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Current challenges in statistics: large-lecture coursesDouglas A. Zahn aa Department of Statistics , Florida State University , Tallahassee, 32306-3033, FLPublished online: 27 Jun 2007.
To cite this article: Douglas A. Zahn (1990) Current challenges in statistics: large-lecture courses, Communications inStatistics - Theory and Methods, 19:11, 4383-4418, DOI: 10.1080/03610929008830447
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COMMUN. STATIST.-THEORY METH., 19(11), 4 3 8 3 - 4 4 1 8 (1990)
CURRENT CHALLENGES IN STATISTICS: LARGE-LECTURE COURSES
Douglas A. Zahn
Department of Statistics Florida State University
Tallahassee. FL 32306-3033
Key Words and Phrases: quality improvement; commitment; one team; barriers; term project; video taping.
ABSTRACT
Each semester approximately 80% of the students taught by the Florida State University Department of Statistics are enrolled in STA 3014: Fundamental Business Statistics. During the academic year this course is taught in large lecture sections of 250 students each. It is either the only statistics course or one of two statistics courses taken in their undergraduate career for probably 90% of these students. A similar situation exists in many statistics departments around the nation.
These large introductory courses offer us the opportunity to introduce the power of statistics to a large fraction of our future business leaders. In the past it appears that this opportunity has often been missed. In fact, some suggest that these courses help contribute to the general public's negative attitude toward statistics courses, the discipline of statistics, and statisticians.
Hence, I propose that one of the current challenges in statistics is the challenge of improving the quality of these courses so that statistics may con- tribute to the improvement of quality and productivity in the United States, a vital national issue. In this paper I report on my experierices in grappling with this challenge in STA 3014.
4 3 8 3
Copyright @ 1991 by M a r c e l D e k k e r , I n c .
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1. INTRODUCTION.
Z A H N
Each semester approximately 80% of the students taught by the Florida State University Department of Statistics are enrolled in STA 3014: Fundamental Business Statistics. During the academic year this course is taught in large lecture sections of 250 students each. It is either the only statistics course or one of two statistics courses taken in their undergraduate career for probably 90% of these students. A similar situation exists in many statistics departments around the nation.
Large introductory courses offer us the opportunity to introduce the power of statistics to a large fraction of our future business leaders. In the past it appears that this opportunity has often been missed. In fact, some suggest that these courses help contribute to the general public's negative attitude toward statistics courses, the discipline of statistics, and statisticians. This attitude is particularly unfortunate, given the contribution that statistics, properly used by a large fraction of employees, could make to the improvement of quality and productivity in the United States, a vital national issue. Hence, I propose that one of the current challenges in statistics is to improve the quality of these courses.
Several factors led me to consider this challenge. By 1984 I had been working for several years with statistical consultants, seeking to help them improve the quality of their services. One of their persistent problems is clients who come to see them much later than would have been useful. Often this type of client has taken an undergraduate statistics course and decided either that statistics was worthless or that he or she was terrified of the subject and, in either case, would avoid it. By applying the basic quality improvement principle of moving upstream from the problem, I considered teaching one of these large undergraduate courses.
A second factor was the dissatisfaction I repeatedly heard from all participants in the current course:
* faculty were displeased with student mathematical skills, level of effort, and attitude;
* teaching assistants were displeased with these items and with being asked to do something for which they were given no training; and
* students were displeased with both faculty and teaching assistant attitudes, as well as reporting that they could see no applications for the tools covered in class and had little confidence in their.ability to use them anyway.
The third factor resulted from my hearing from a vide variety of individuals that "Statistics was the worst course I ever took." My observations described in the previous paragraph led me to think that we were well on our way to creating this evaluation of statistics in yet another generation of students. D
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CURRENT CHALLENGES IN STATISTICS 4385
A fourth factor appeared as I learned more about the quality revolution in Japan. I discovered that the backbone of the effort is the fact that virtually everyone knows and uses the basic tools of quality improvement. This led to a shift in my objectives for the course: previously I had thought it was enough to help students become better consumers of published statistics, but now I shifted to also wanting to help them be able to produce at least basic studies using sound statistical practice.
After considering these factors for some time, as well as concerns about lecturing to a large group twice a week and managing all the details associated with such a large class, I volunteered in 1984 to teach one of the sections of STA 3014. In this article I report on my experiences in grappling with the challenge of improving the quality of this course.
Since 1984 I have implemented both on-line and off-line quality improvement procedures in my course. In this article, as in the course, I use Joiner's triangle (1985) as a guide for discussing this process. This triangle (Figure 1) represents three critical elements of quality improvement in any organization: commitment to quality, teamwork, and use of the scientific method. Section 2, 3, and 4 discuss actions taken and barriers encountered relating to commitment to quality, teamwork, and use of the scientific method, respectively. Section 5 discusses findings from my quality improvement efforts. Section 6 presents some thoughts on where to go from here.
Before discussing commitment to quality, I will first sketch the environ- ment in which I am working:
* The Statistics Department has a primary commitment to research and scholarly activity, particularly in theoretical areas of mathematical statistics and probability.
* It has a secondary commitment to graduate training in statistics. * It has a lower-level commitment to service teaching at graduate and
undergraduate levels. * Statistics 3014: Fundamental Business Statistics is taught in three
sections of 250 students each in the fall and spring semesters.
* Each section has a lecturer who delivers two 50-minute lectures a week and three teaching assistants (TA's).
* Each TA meets once a week for 50 minutes with three different recitation sections, each of which contains about 30 students.
* There is generally a departmental final examination.
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2. COMMITMENT TO QUALITY.
Z A H N
2.1. Actions Taken
I approach quality improvement in STA 3014 by regarding myself as Chief Executive Officer (CEO) of my company, FBS, Inc. The main activity of my company is to teach my section of STA 3014. This is not just one more gimmick to try to make introductory statistics fun to teach. Rather, the approach comes from the suggestion of an executive of Florida Power and Light who listened to me complain about my perception that there is a lack of a clear commitment to quality improvement in large-lecture courses by my senior management. He pointed out that, regardless of this, I could view myself as the CEO of my class and then, as CEO, state the company's commitment to quality. In my capacity as CEO, I have declared publicly (and do so each term to my new students) that I am committed to the systematic improvement of the quality of the services rendered by the firm.
This firm, like any other, delivers its service using a process which h:is inputs, a value-added step, and output. There are suppliers, workers, and customers. The students are both input and suppliers, my TA's and myself are workers, and the teachers of the courses for which this course is prerequisite are customers. Further reflection led me to see that the students are hiring me: they (or their parents) are paying part of my salary. Society as a whole, as determined by the legislature, pays the rest. Thus, the students are cuxoniers. Yet, they are also suppliers, providing themselves as the input for the course. Also, no value will be added by the course unless the students also do part of the work. Thus. they are also co-workers.
There are certainly other suppliers: the prerequisite algebra course. high school, middle school, primary school. There are also other customers: subsequent courses, future employers, customers of those employers, and society at large. Yet the input of all the earlier suppliers is filtered through and delivered by the student. Also, any contribution that the course makes to subsequent customers is made through the current students and whatever of the course that they take out with them.
Thus, I think of the students as the first generation suppliers and the first generation customers of the course. Figure 2 summarizes this, using a figure from Denling (1985).
Another essential step in quality improvement is deciding on an operational definition of high quality. Building on Boroto's (1990) description of an effective consultation, I propose that a high-quality course is one in which
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CURRENT CIIALLENGES LN STATISTICS
I . the material delivered is correct, 2. the students learn an implementable plan for
using the material, 3. the plan is implemented, and 4. the results stand up to external scrutiny.
This begs the question of what are the dimensions of quality for the course and when to measure them in order to see how the course measures up to this definition of quality. My most recent version of dimensions of quality for the course is the following list:
1. Students are participants in the creation of the course, not spectators at a performance.
2. The course provides an opportunity for the students to see the utility of statistics.
3. Students can use and interpret syllabus tools and concepts.
4. The syllabus reflects the needs of various customers: students, faculty teaching subsequent courses, future employers, and society at large.
5. Students do not encounter hassles during the course.
6. Students are regarded with dignity and treated with respect.
7. Students do a term project from start to finish in which they identify a business question to study, gather and analyze data relevant to it, and present oral and written results on their study.
These dimensions can be measured during or at the end of the course. I describe instruments I have developed to do this and some results of using them in Section 5 of this article.
Perhaps the most critical dimensions of quality are those that cannot be measured until five or ten years later when the student is on the job and i n ;i situation in which the 'course concepts and tools could be used. We v s a profession must start taking steps toward being able to do a formal long-term assessment.
2.2. Barriers Encountered
A barrier to implementing a commitment to quality in FBS, Inc. is the attitude present in all the participants that there is no solution to the quality problems presented by the large-lecture course. Faculty, TAs, and students seem to have the point of view that this course will not work well and that "I would
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4 3 8 8 Z A H N
rather not be here." So, just put in your time, suffer through it, and then at the end of the term you can check off the requirement for taking it or teaching it. This barrier, "There is no solution," is particularly insidious: it stops us from committing to improving the quality of the course and it leads us settling for an "easy" solution: "Nothing more can be done to improve the situation."
The barriers of tradition and scarcity are also present here. Traditionally, one of the problems in large-lecture courses has been that students feel that they are lost in a nameless crowd. Scarcity is present as a barrier in that some think we just d o not have enough resources to do this course well.
3. ALL ON ONE TEAM
3.1. Actions Taken
Extensive work with statistical consultants has convinced me of h o ~ . essential it is for a professional to establish what services his or her customers hope to receive before beginning to work with the customers. The first step in a successful consultation is to determine what the client wants and needs so that the consultant and client will be on one team working toward a shared goal (Boroto 1990; Boroto and Zahn 1989a). The challenge in STA 3014 is to adapt these ideas to a course with 250 students.
3.1.1. Teaching Assistants
One place I have used them is in dealing with TA's. In August 1989 and 1990 we had a half-day workshop before the fall semester started. Prior to the workshop, I asked the TA's to fill out the questionnaire given in Figure 3 to tell me what they wanted and needed from their work as TA. I gave them my Teaching Assistant Job Description (Zahn 1989) to tell them what I expected from them. Discussion of these items gave us each a better idea of what the others' expectations are. It helped to get us onto one team.
I share the power in the course with the TA's by having structured weekly staff meetings with them as full voting partners in the production of the course. This reduces stress on me (once I got over my fears about loss of control and became aware of the paradox that the more power one gives away, the more power one has) and helps prepare them for the many nontechnical aspects of teaching. I distribute an agenda to everyone the day before the meeting using the form in Figure 4. I chair the meeting and one TA serves as time keeper and another as scribe. The meeting ends with a five-minute review of action items and decisions made. Then we a 1 participate in a five-minute critique of the process of the meeting to see where there is room for improvement.
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CURRENT CHALLENGES IN STATISTICS
COMMITMENT TO QUALITY
Figure 1. Joiner's Triangle
Design and redesign + Consumer Research Suppliers of materials /
and equipment /' b Consumers
Distribution Production, assem_bly, inspection
b-
D Tests of processes, machines, methods, costs
Students Students Students Prerequlslte courses Teaching Assistants Business School Faculty
High Schools Faculty Employers Society
Figure 2. A Diagram of the Course Process
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ZAHN
August 4. 1989
m. STA 3014 TA's and po:cntial STA 3014 TA's
FROM: Doug Zahn
SUBJECT: Qucsoonn-.n for :$e STA 3014 TA Workshop. Friday, A u p s t 2 5 . 1989; 9:W AM :o 12:W Nmn; Lccaoon: 205 OSB
PURPOSE OF WORKSHOP: 1. Clarify STA 3014 TA and STA 3014 faculry
undersran&ngs of their own and each othcr's jobs.
2. Lay a foundadon for on-going quality mprovements m STA 3014.
What did you want from yow TA u,hen you w e n an underpduatc student in a TA's nciration secoon?
Do you havc any questions a b u t the axached TA jo3 d e \ ~ p c o n ? If so. wha: are h c y ? Pleasc suggest any addidons o: deleoons rhn: %cur to you.
M a t would you suggest we mcluac in a STA 3014 faculry job descnpnon?
If you havc p m o u s l y been a STA 3014 TA, u,ha: are the roughest smauons you havc encountfled on h e job?
R I z t would have to happen in STA 3314 this fall so ha t you would be sans5ed on December 15, 1989, u?Li h e m e and effon you have expended as a TA?
U you o?zrated o p n d y as a TA in an :deal coune: a. N'nat would be happcrung then that is not happening now?
b. WXat is happening now ha: would not be happening then?
What do you plan to bc doing f i x ycars from nou?
What could you learn as an STA 3014 TA ha: could enhancc yow chances of acluevmg This goal?
What do you want 10 gain, learn or dscover in h ~ s workshop?
Figure 3. TA Workshop Questionnaire
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CURRENT CHALLENGES IN STATISTICS
Gro3y L X - Timekeepe:
Meeting Type Time Scribe
Place Chzirperson
BEGAN AT
Activity:
Decisions Made:
PRIORITY pOTEh7V.L AGEAQA F-?IS
Start
Set agenda, priorities, time esrimares
Repon on Action kerns
Policy questions, dccisons
Review of Action Items and Decisions
Critique of Process of Meeting
End
ACTION r n h 4 S L r n M Y :
By Whom
Figure 4. St& Meeting Agenda
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The careful structure in this meeting may seem overdone to some, given that there are only four people present. In my experience, it helps to keep the meetings on track and dealing with the highest priority items, thereby avoiding scrap and frenzy at the end of the meeting. It also helps us to manage the multitude of details present in running a large course. It frees my concentration to focus on the matters at hand, knowing that someone else is keeping track of the time and of the activities to be done. It reduces the rework resulting from unclear assignment of duties.
3.1.2. Students
Identifying what 250 students want and need from STA 3014 is challeng- ing. I approach this task using the General Information Handout, Assignment 1, and Minute Papers. My General Information Handout (Zahn 1990a) tells students what is available in the course and what I expect from them. The last page is Assignment 1 (Figure 5) in which they tell me what they expect of me, my TA's, and the course. The third tool is the Minute Paper (Figure 6), which offers a random sample of the students an opportunity each lecture to tell me whether they got what they came for and what parts of the lecture were unclear.
A key part of creating the sense of "All on one team" with the students is the term project that they do in teams of three to five students (Zahn 1989b). The purpose of the project is to challenge head-on the thought that the course is a make-work requirement that serves only as a filter to reduce Business School enrollment. The project gives students a chance to discover for themselves that the material in this course can be used to address business questions of interest to them. This is the most powerful tool I have found as of yet for getting the students to be participants in the course rather than spectators.
Each team selects the quality improvement question it wishes to study. The team then constructs a proposal for how it will conduct the study. This proposal is presented orally to the other students in the team's recitation section and in writing. Next data are gathered, analyzed, and the final report is prepared. This report is also presented orally and in writing. Appendix 1 gives two Executive Summaries of recent projects, along with letters written by managers at the businesses where the projects were done.
In the past, I have had trouble creating an "All on one team" attitude during the lectures. Spring Semester 1990 (my tenth in teaching this course) was the first time when I felt some satisfaction with my crowd control activities and the creation of a team in the lecture. Here are the steps I took:
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CURRENT CHALLENGES IN STATISTICS
Name Quiz 1 Assignment
Recitation Section
Due in your recitation section on January 12, 1990
Note: There is no "right answer" to Questions 1 - 10. You will earn 10 of the 30 points on Quiz 1 for your effon in considering and answering the questions clearly. Use the back of this page if necessary.
1. What do you want to gain, learn, or discner in this course? Please be specific.
2 . What grade are you working for?
3. What do you think your job is in this course? Please be specific.
4 . What do you think Dr. Zahn's job is in this course? Please be specific.
5 . What do you think your TA's job is in this course? Please be specific.
6 . In lecture we will often work through a particular type of problem. Then I will ask, "Are you confident you can do a problem of this type on this week's quiz?" What percent of the students in attendance would you like to see say "Yes" before we move on to the next topic?
7. Topics I would like to see used in class examples to itlustrate the use of statistics are:
8. For the course project you will be working on a team that you have formed. a. If you operated optimally in an ideal team, how would this team function? b. What concerns do you have about working on a team on the course project? (In the Team
Working Agreement due on February 9, 1990, you and your teammates will have the opportunity to develop a smtegy for addressing these concerns.)
9 . What can your course project team count on you for during the term? Please be specific
10. Any other comments, questions, or concerns?
11. By my signature below, I certify that I have read the General Information Handout and choose to be in this course under these terns.
Figure 5. Assignment 1
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4 3 9 4 Z A H N
Please take a minute at the end of class to answer these questions to help me plan for tomorrow.
1 . Did you get what you came for today? If yes, what did you get? If no, what was missing?
2. What was the muddiest point in the lecture?
3. In your own words, what did you learn today?
4 . Any other comments?
If you have a specific request or question, please put your name on your minute paper so I may give you a response. Otherwise, names are optional on minute papers.
(For background information on minute papers and related ideas in statistical education, see the article "Broadening the scope of statistics and statistical education," by Frederick Mosteller, which appeared in The American Statistician, pp. 93-99, Vol. 42, No. 2, May 1988.)
Figure 6. Minute Paper Questions
1. When discussing Joiner's triangle, I indicated that for me "All on one team" includes the lecture and that each of us has a job to do to produce a high-quality lecture. Their job includes being prepared by reading the assigned reading, asking questions when anything is unclear, being here on time, staying to the end, not moving around during lecture, and not talking, except to me. My job includes being prepared, being sure that content is clear to them, managing the crowd control, and starting and ending on time.
2 . I described the creation of an effective environment in which we would work as, "going slow so that we could go fast later." I likened the process to that of building a vehicle for a mp and summarized the discussion by the transparency given in Figure 7.
3. Whenever I noticed a student violating an aspect of company policy, I would ask for his or her name and section number and ask him or her to see me after class. I then gave them Figure 8. A typed response to this assignment was their entry ticket to the next lecture.
The results of these steps included less movement during lecture, virtually no late arrivals or early leavers, and a much quieter class time than during any of my nine previous semesters in the cpurse. Also, I felt much less exasperated by these issues. The students that I named came by after class on one of the two times I used this tool. When they did not come by; I put their name on the next lecture's announcements transparency to come see me, which they did two lectures later. They then had additional work: in addition to completing Figure 8 for their D
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CURRENT CHALLENGES IN STATISTICS 4 3 9 5
Process Issues: Vehicle Construction
GOAL
1. Construct it, first shot.
2. Road Test. Refine, Repair.
3. Use it. Will confront breakdowns. Will do preventative maintenance.
4. Want to stay on road? Eliminate breakdowns.
5. Impossible? I disagree.
Figure 7. L'ehicle Construction
talking in class, they also had to do it for not doing their job of seeing me after class. I noticed that after I had used the system twice that all I had to do when I noticed talking was to pause and look around. Silence returned in less than five seconds and then I would resume. I also noticed that when I felt tired, I would sometimes not hold the students accountable for being late. How well I managed the quality of the lecture had a lot to do with how well I was managing my well- being. All in all, this system worked well enough that I plan to use it again.
3.1.3. Customers
To achieve the "all on one team" spirit with the executives of those companies that will be hiring the graduates of the Business School, I use every opportunity to ask them what they would like to see students learn in their first statistics course. On the basis of these conversations, I have incorporated topics and activities relating to quality improvement in the course: Ishikawa's Seven Tools (Ishikawa 1976), the comparison of analytic and enumerative studies (Gitlow, Gitlow, Oppenheim, and Oppenheim 1989), and the term project which is done in teams of three to five students. D
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Z A H N
PBS, Inc. Department of Statistics Florida State University Tallahassee, PL 32306
M E M O R A N D U M :
TO:
Someone arriving late and sitting in the middle of the class,
leaving early from the middle of the class,
reading a newspaper, or
talking
FROM: Douglas A . Zahn, CEO of FBS, Inc.
SUBJECT: Your behavior and the quality of the company's services
You have engaged in a behavior which does not contribute to improving the overall quality of the services provided by FBS, Inc. In fact, by your signature on Assignment 1, you have previously agreed to not do this.
As a consequence of this incident, your job is to give me a typed response to the three questions below prior to the next lecture, as your entry ticket to that lecture.
1. If you were managing a cortpany, had set a company policy, and had a worker uho agreed to this policy and then broke it, how would you deal with this worker?
2. How did today's incident occur?
3. In the rest of the term, what can I count on you for, relative to the behavior at issue today?
Figure 8. -4ssignment for Policy Breaker
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CURRENT CHALLENGES I N STATISTICS
3.2. Barriers Encountered
A barrier to creating "All on one team" is the traditionally adversarial relationship of faculty and students. Fifteen years in the U S . school system have left many students skeptical of us all being on one team. I seek to convince them of my commitment to being on one team with them by
* being clear as to what the course requirements are, * being clear about what will be on quizzes and tests, * giving sample projects to indicate what I am looking for on that
assignment, * having a stated policy on excused absences, * having clear deadlines, announced well in advance, * considering all their complaints about any aspect of the course.
I also respond in writing to each complaint they register in writing.
Over the years, dealing with this adversarial attitude has been one of the toughest aspects of quality improvement for me. It seems to lead the students to blame me for any aspects of the course that are not going well, such as when the quiz scores drop from an average of 85% to 55%, as they have several semesters when the course moves from descriptive statistics and probability to sampling distributions and inference. I too respond by thinking that it is their fault and we have a stand-off.
I have noticed that when things are not going well, many negative reactions show up in the students and me: anger, discomfort, anxiety, discourage- ment, conflict, and blame. To my chagrin, I realized early on that my course quality improvement plan did not include improving the quality of the process whereby I deal with complaints. This has changed. I now see my job as CEO as including the responsibility to take the first step toward addressing the impasses which tend to occur when complaints arise. My goal is, first, to stay present, open, honest, compassionate, and clear, in the face of the students complaints and, second, to keep talking with the students, even when all of us are upset, gathering information that can be used to sort out what are often conflicting or competing priorities or values. With the help of my coaches, I am slowly progressing toward this goal.
Of all the aspects of my quality improvement efforts, this is where progress is slowest and most difficult for me. I get discouraged here, especially in mid- semester when student complaints seem to peak. This is the place where I come face-to-face with a statement made by W. Edwards Deming which I think may be understated:
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4 3 9 8 ZAHN
Long-term commitment to new learning and new philosophy is required of any management that seeks transformation. The timid and the fainthearted, and people that expect quick results, are doomed to disappointment. (Deming, 1985, page x)
There is a paradox in this area: the more.effectively I create the sense of "All on one team" with my TA's and students, the more intensely they look at the quality of all aspects of the course. They begin to question aspects that I had never questioned before. This is often disappointing since I usually had assumed that these aspects of the course were working fine. Then I may begin to feel like I don't want to be on a team with them! Perhaps this is why people speak of never-ending quality improvement!
4. USE OF THE SCIENTIFIC METHOD
4.1. Actions Taken
One traditional source of data on the quality of the course in certain dimensions is the gradebook. The weekly quizzes, term project, and final exam reflect how well the students perform on those tasks.
Other data collection activities include:
* The Statistics Student Survey gathers attitudinal data on students as they enter and exit the course.
" Assignment 1 gathers data on the student's expectations as they enter the course.
* The Minute Papers gather data from a 10% sample at the end of each lecture on the quality of the lecture.
* Attendance counts are done each day. * The Midterm Course Evaluation assesses how well the students are being
served at midterm. * A poll on Day 26 (Fall, 1989) identified the highest and lowest quality aspects
of the course from the students' perspective. * A working group meeting during the last week of class in Fall 1989 produced
a cause-and-effect diagram for the quality problems of low quiz scores and 60% lecture attendance at the end of the term.
* The Course Evaluation Questionnaire gives a summary evaluation of all aspects of the course.
I have also observed and videotaped the TA's in their recitations sections, guiding my coaching by using a form contained in the "Teaching Assistant Job Description" which tells the TA's what I expect them to do. I have also videotaped most of my lectures over the past two years. D
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CURRENT CHALLENGES IN STATISTICS
4.2. Barriers Encountered
The first barrier encountered was resistance to gathering data on oneself. The resistance I noticed to videotaping my own consulting sessions showed up again (Zahn 1988). No one likes to stand in front of the mirror. This was a difficult banier for me. It also shows up whenever I invite others to gather these data on their own courses.
After getting some data collected, I ran into a second barrier: finding the time to analyze the data. I have gathered an enormous amount of data and analyzed only a small part of it. Of course, this also serves the defensive part of me that was not pleased in the first place by the idea of data collection. I have not yet figured out a way to find enough time on the job to do these analyses.
Then, as I get some data analyzed and it suggests a need for modifying some aspect of the course, there is a third barrier: resistance to change!
5. FINDINGS
5.1. Traditional Measures of Course Ouality
The most traditional and the easiest, though not necessarily the most useful, measures of course quality are the course grades and the final exam grades. The course grade distributions for the semesters Fall 1987 to Spring 1990 are given in Table 1. The count for "B" includes the grades "B+" and "B-." The two to six students per semester receiving grades of "W" or "I" are not included. The average final exam grades are also given in Table 1. The averages for the other instructors are within five percent of my averages. I have tried many strategies (making previous final exams and solutions available, having the quizzes be similar to the final exam, additional review sessions, reducing the amount of material in the course, reserving lecture time at the end of the semester to teach students how to diagnose what tool to use in word problems) to improve both the grades and the final exam score with apparently little success, at least as reflected in these data. In fact, the grades declined from Fall 1987 to Fall 1989. Some of this decline may be due to the fact that the FSU College of Business changed several of its admission requirements during this time period. Due to the changes, the course enrollment has shifted from 32% sophomore, 52% junior, 11% senior, 5% other in 1985-1987 to 64% sophomore, 28% junior, 4% senior and 4% other in Fall semester 1990.
Another traditional measure is the Student Instructional Rating System (SIRS) done by the 35-70% of the students attending class at the end of each semester. The results for two summary measures for the last ten semesters are given in Table 2. Both have been stable for the last six semesters. D
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4400 ZAHN
Table 1. Percent Earning Various Grades, Fall 1987 to Spring 1990
Semester A B C D F Number of Final Exam Students Average
Fall 1987 11% 43% 33% 10% 3% 247 67
Spring 1988 16% 34% 34% 11% 5% 230 7 1
Fall1988 11% 41% 33% 10% 6% 228 68
Spring 1989 17% 34% 29% 16% 3% 24 1 66
Fall 1989 9% 27% 35% 21% 9% 234 6 1
Spring 1990 11% 32% 35% 14% 8% 219 64
Table 2. SIRS Responses on Competence and Effectiveness
Semester F85 S86 F86 SS7 F87 S88 F88 S89 F89 S90
Question
The insmctor appeared to be 74 * 77 96 89 91 87 94 91 95 thoroughly competent in his or her area.
In general, the insmctor was 37 * 46 90 60 61 58 63 57 64 an effective teacher.
Kumber of students responding 166 123 89 95 89 156 131 117 118
Cell ennies are the percent of students responding "Strongly Agree" or "Agree."
" = Missing
5.2. Additional Measures of Course Quality
Since one of my main reasons for volunteering to teach this course was to increase the fraction of students leaving the course who think that statistics is useful and who are confident that they can successfully use statistical tools, I have developed my own Course Evaluation Questionnaire to measure these variables. Students receive it the last day of lecture and turn it in at the final examination, receiving 10 points extra credit (total points possible is 1000). The response rate is 90-99%.
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CURRENT CHALLENGES I N STATISTICS
--=- i Recognize i 1
Can do I -0- I Uses
Semester
Figure 9. CEQ: Outlook, Semester -4~erages Fall 1987 to Spring 1990
Figure 9 summarizes student responses to the items:
"Recognize": I can recognize where statistical thinking and procedures can help address a managerial question. "Can do": I can do a basic statistical study and interpret the results. "Uses": As of now I can see potential uses of statistics in my career. "Got it": Considering the course as a whole, did you get what you came for?
The available responses for the first three questions ranged from "Strongly agree" to "Strongly disagree," with "Strongly agree" and "Agree" being collapsed in the results represented in Figure 9. There has been improvement in the percents reporting that they agree that they can see uses for statistics and that "Yes" they
Figure 10 shows the percent of students agreeing that they were satisfied with the project (Sat-proj) and that the project was a valuable learning experience for them (Pr-lm), as well as the percent who got what they came for. The percent satisfied with the project has been stable, while the percent regarding it as a valuable learning experience has slowly increased. As the TA's and myself have gradually learned how to present a project assignment to a class of 250 clarifying the instructions each succeeding semester, the students have learned more from their projects. D
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....-............. ....... ...-..- ... I t - ,
- \+ ! Got it 1
X - i - ... - =/I. ..................... X.. .. . i pr-~rn
Y- 1-
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0 ~
F,87 Sp,88 F,88 Sp,89 F,89 Sp$O Semester
Figure 10. CEQ: Project Satisfaction Fall 1987 to Spring 1990
I
~ , 8 7 S& F,88 s p i 9 F,83 Sp,93 Semester
Figure 11. CEQ: Satisfaction Levels Fall 1987 to Spring 1990
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CURRENT CHALLENGES IN STATISTICS 4403
Figure 11 shows the percent of students reporting that they are satisfied with various aspects of the course: the course as a whole (Sat-crs), the lectures (Sat-lect), and the recitation sections (Sat-RS). Satisfaction dipped in Fall, 1989 when I had an entirely new team of three TA's and then returned to previous levels in Spring, 1990, when all three had by then worked with me at least one semester. Lecture satisfaction has been slowly climbing as I discover what class activities are useful to this group of students.
For six semesters at FSU (Fall 1985 to Spring 1988) and two semesters at each of the University of Kansas and the University of Wisconsin-Madison (Fall 1987 and Spring 1988), my colleagues (Pi-Erh Lin, Lawrence A. Sherr, and Robert B. Miller) and I gathered data on attitudes students had when entering and leaving the course. On the three questions reported in Table 3 the attitudes were remarkably similar, varying over semesters and schools less than plus or minus five percent from the averages reported.
These data offer a baseline that indicates that perhaps the material introduced in the last two years on quality and productivity is beginning to increase the percent of students who are shifting their attitude during the course as to whether they will have use for statistics in the future. A rough indicator of this is that the percent of students at the end of the course who agree that they can see potential uses of statistics in their careers has risen to 80% in the 1989-1990 year, as compared to the 61% who disagreed in 1985-1988 with the statement that they won't have much use for statistics beyond this course. I note that these are two different questions and yet the difference in the percents is substantial.
5.3. Data from Observing the TA's
TA's initially strenuously resisted my attending their classes to gather data for coaching. Videotaping was also resisted. I found that a way to really destroy the "All on one team" attitude was to show up at a recitation for supervision, with the camcorder, unannounced. Another way was to speak up in a recitation when I was observing. I knew better than this. It was surprisingly difficult to manage myself when I saw a TA making a mistake while I was sitting watching him or her. It took a while to mend the damage.
Here are some observations about coaching using videotape:
A. It is tougher than teaching itself because it requires watching both the TA and the class, learning how to identify critical aspects of effective and ineffective instruction on the tape, learning how to deliver coaching to the TA in a way that is constructive.
B. TA's resist being observed and being videotaped, though the resistance is lower now than it used to be, perhaps because they know that this is what is involved as my TA. Their resistance, D
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coupled with my discomfort in holding people accountable, can produce an unproductive "racket:" I don't coach and neither of us holds the other accountable for our failed commitment to doing supervision, coaching, and quality improvement. The following are quality problems which arise with most TA's: 1. Not mentally prepared and centered before the class starts,
often because of rushing to class from another class. 2. Starting to answer a question before they are clear what the
question is. 3. Not making sure that the rest of the class knows what the
question is before answering it. 4. Not checking whether the answer given answered the
question asked. 5. Speaking to the board. 6. Not checking to see if they are being understood. 7. Not handling talking in class effectively. 8. Not being aware of when they are "making students
wrong," blatantly or subtly. 9. Getting defensive in class.
10. Not waiting at least five seconds for an answer after they asked a question.
D. Of course, many of the quality problems listed in Part C also show up in the videotapes of my lectures.
5.4. Sources of Variation in Grades: A Few Analyses
The relative stability of the course and final exam grades from term to term led me to investigate whether the variation within terms is related to various performance or background variables. For Spring 1989 I regressed the final exam score on the quiz 10 grade, the quiz score total, homework points, extra credit points, SAT quantitative score, SAT verbal score, FSU grade point average, and attitudes before the course on the three questions listed in Table 3. A stepwise regression procedure selected quiz score total and SAT quantitative, producing the following regression equation:
9 = -12.8 + .40 * (quiz total) +.I7 * (SAT quantitative),
where 9 is the predicted final exam score and R2 = 38%. In Spring 1990, selecting from only current semester variables, stepwise regression yielded
9 = 45.9 + 2.2 * (quiz 9 score) + 1.4 * (midterm score),
and R~ = 42%.
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CURRENT CHALLENGES IN STATISTICS 4405
Table 3. Before and After Attitudes of Beginning Business Statistics Students a t FSU, KU, and UMr-M; Fall 1985 to Spring 1988
Question Response Before After
I won't have much use for Agree 12% 19% statistics beyond this Neutral 31% 20% course. Disagree 58% 6 1 %
I am worried about how well Agree 70% 6 6 % I will do (did) in this Neutral 18% 14% course. Disagree 13% 21%
I think that statistics is a Agree 19% 2 9% boring subject to learn. Neutral 54% 3 2 %
Disagree 28% 4 0%
The 1989 analysis indicated a relationship of final exam score to native quantitative ability and the total quiz score, a measure of consistency of perfor- mance during the semester. The 1990 analysis indicated a relationship of final exam score to the last quiz in the semester and to the midterm score, a measure of how well the student mastered the first half of the semester.
In Spring 1990 I collected names of students present in lecture on three consecutive days about two-thirds of the way through the semester. This is the point in the semester when regularly only about two-thirds of the students attend lecture. Figure 12 shows how the final exam score and the total score in the course varied by the number of these three days that a student was present. The histograms for those who were not present on any of these days show two groups of students: those who did well and those who did not do well on both the final and in the course as a whole. The groups that were present more days also had a higher grade distribution.
5.5. Quality Improvement Actions: Fall 1989 to S ~ r i n g 1990
In part because of my exasperation at how slow quality improvement is in STA 3014, I decided to implement additional steps. In December 1989, I asked all students in lecture one week before the end of the semester to write down the two highest and the two lowest quality aspects of the course. A week later I also asked them to name these aspects in the free response section of the SIRS form. The results indicated that in the students' view the highest quality aspects of the course were the project, the course organization, and the applications they saw of statistics to business. The lowest quality aspects were that there was too much D
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CURRENT CHALLENGES IN STATISTICS 4407
material in the course, the project was too much work, the class was too large, no quizzes were dropped, there was no midterm, and lecture time was wasted on activities like reporting on the Minute Papers or doing crowd management.
In December 1989 I also met with a group of eight students to construct cause-and-effect diagrams (Ishikawa 1976) for two persistent problems: the drop in quiz scores and attendance as the semester progresses. Figure 13 shows quiz scores for Spring 1989, a typical pattern. Figure 14 shows average attendance, excluding the first and last days of class, exam days, and the days before spring break and Thanksgiving. The meeting produced the diagrams presented in Figures 15 and 16.
As a result of these activities, I decided to implement the following changes in Spring 1990:
* assign daily homework to be done in notebooks to be collected three times in the semester,
* get into the more difficuIt material quicker, * make the earlier quizzes harder, * have a midterm exam, * reduce the amount of lecture time spent on announcements,
the Minute Paper report, and working homework problems from prior lectures,
* pay two points extra credit for the 10% sample chosen each lecture to do Minute Papers.
Following are the results from Spring 1990:
* quiz scores dipped earlier (the maximum quiz score was 30), recovered, and then dipped again at the end of the semester as seen in Figure 17,
* lecture attendance was about the same, perhaps a bit lower after the midterm (see Figure 18),
* the Minute Paper response rate improved, the "Got it" percent steadily improved during the semester, while the "No Mud" percent proved to be difficult to get above 60% (see Figure 19),
* complaints about the lack of homework and exams were drastically reduced, and
* the final exam average was similar to previous semesters.
The SIRS free responses indicated that to the 98 students who responded, the aspects of the course students liked best were the course structure, the commitment to improving the course, the project, the use of realistic examples,
and management of the course as though it were a business. The aspects of the course students liked least were the time demands of the project, the amount of D
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Z A H N
40 I 1
Quiz Kumber
Figure 13. Quiz Averages, Spring 1989
Figure 14. Attendance, Spring 1988 to Fall 1989 Special Causes Removed
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CURRENT CHALLENGES IN STATISTICS 4409
Figure 15. Cause-and-Effect Diagram for Quiz Score Drop, Constructed by Eight Students on December 5 , 1989
Figure 16. Cause-and-Effect Diagram for Attendance Drop, Constructed by Eight Students on December 5, 1989
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Z A H N
Quiz Number
Figure 17. Quiz Averages, Spring 1990
250
Attendance P
Figure 18. .4ttendance, Spring 1990
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CURRENT CHALLENGES I N STATISTICS
e-
Got it
0/6 No mud i
Figure 19. Percent Got It and Percent No Mud, Spring 1990
lecture time spent on managing the quality of the lecture, doing the project in teams, and rushing through the tougher material at the end of the semester.
Quality improvement is a never-ending process!
5.6. Unknown and Unknowable Variables
Deming (1985) discusses the dangers of managing on the basis of numbers alone, asserting that the most important numbers relating to a company's survival are unknown and unknowable. I think this is also true in the education industry. What are the losses to us, to future classes, to the business school, to future employers, or to society when
* a student leaves the course convinced that the rumors were right: Statistics is useless; the course serves only as a filter,
* a student leaves the course thinking he or she does not know how to use statistical tools, or
* a student is treated with disrespect by a TA or lecturer in the Help Room.
On the other hand, what are the benefits to these groups when Dow
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4 4 1 2 Z A H N
* energy is spent by a team working on their project and talking about statistics late at night over Pepsi and popcorn,
* students are talking to each other before and after class, rather than arriving and leaving silently in the typical anonymity of a large class,
* students come to office hours to discuss project ideas, * the syllabus reflects topics thought to be useful by practitioners, * the class has adopted the one-team concept so that it is silent in lecture,
people are in their seats on time and stay to the end, and * the steady flow of questions and ideas from the students' work on their
projects helps the instructor feel fresh rather than stagnant after teaching this course ten times in the past five years.
The benefits of the quality improvement efforts which are perhaps the most difficult to quantify are also perhaps the ones which most provide me with energy and inspiration to continue. These are the comments which appear on the Minute Papers, Course Evaluation Questionnaires, and the SIRS forms. While one to three percent of these comments continue to be negative, even radically so ("Fire him! He can't relate to students!"), the percent that are positive has grown steadily over the years. I find this as well as the content of the comments to be encouraging. I have reproduced some comments below to encourage those of you considering this activity to get started. Students do not often experience quality improvement efforts. When they do, they generally appreciate them; many will become part of a team to help improve the course. Here are six comments from the last two semesters:
At first, I didn't understand why a course project was required, but as we moved along in the semester as well as in our project, I started to realize the practical aspects of the statistics I learned, and enjoyed putting what I had learned to use, so quickly.
I saw many, many ways in which the material I learned can be applied to real life, everyday situations. STA 3014 truly has been a personal asset.
When I took this course last fall under a different instructor, at the end of the course I had no idea how you incorporated statistics to a business. Thanks to doing a project, I now understand.
I came to statistics to learn formulas and that I did, but I learned a lot more. ... I learned how to apply it to my career and just basic everyday things.
I learned what stats was all about and became aware of just how it can be used in the field of business. Before this class I did not know what it involved and now I can see its usefulness.
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I got an overview of just how important statistics are in everyday life, and especially their importance to businesses and their processes. This is what I expected to learn, and I did, especially with the project.
5.7. Most Improved Aspects of the Course
I list below the aspects of the course that I think have improved most in the last five years. Again, my goal is to encourage those who are considering systematic quality improvement. That these results have been achieved is due to a combination of the power of the quality improvement strategies, the quality of the staff, TA's, and students who have worked with me, and my commitment and persistence.
From my perspective the most improved aspects of the course are
1. the management of my anxiety before, during, and after the semester A. preparing for the first day of class, B. handling grade complaints during and after the semester, C. dealing with the thought "I should have covered more this
term," 2. the distinctions I am beginning to make as to what I am and am not
accountable for, what I do and do not have control over, 3. the crowd management aspects of lecture, 4. the use of lecture time, 5. the system for working with the TA's, 6. the project oral presentation days, 7. the TA job description, 8. the term project instructions (Zahn 1990b) and the overall quality of the
projects, (See Figures 20 and 21 for the Executive Summary and letter from a manager at the business studied for two recent projects.)
9. the general information handout, and 10. the course content.
5.8. Toughest Challenges to Continued Course Quality Improvement
From my perspective the toughest current challenges to continued improvement of the quality of STA 3014 are
* the lecture attendance drop, * the late-semester quiz score drop, * the low average score on the final, * the individuals who hear my comments about "quality improvement" and
"all on one team" as evidence that I am being totally unrealistic i n my approach to the course,
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E S r n v E SUMMARY
1. Our group of h e e studied the waiting time in the reception area for scheduled patiens prior to seeing the dentist, Dr. Glenn Beck, Jr. The quality improvement question we studied is a beneficial factor in aiding the doctor and his staff in keeping an effcient schedule. thus keeping patients satisfied.
2. Our study was conducted by sampling 16 hours, which is 40% of the 40 hour work week. We randomly selected the hours from the random numbers table to determine which hours we would time the patients waiting period. The team member doing the timing was given a daily schedule and was seated in the reception room. The stop watch was started when the patient went to the window and stated hisher name. We stopped timing when the patient's name was called and was then seated in an operaw.
3. Variable A - Our findings indicate that the average waiting time is longer than the 1200 seconds we had estimated
Variable B - We found that on the average a patient waits longer on Wednesday then followed by Thursday, Tuesday, Monday, and then Friday.
Variable C - Our statistics have shown conclusively that first time scheduled patients have a shorter waiting time than other patients.
4. We recommend that the doctor and hi staff work together to produce a more efficient schedule that runs more smoothly and keeps the patients waiting less time.
D r . Z a h n F l o r l d z S t a t e U n i v e r s i t y T a l l a h a s s c c , T 1
D e a r D r . Z a h n :
1 h a v c r c c r m l t l y b c c n ~ n l o r n t c d o f t h c r l n d i n q s i n t h c s t a t i s t l c a l s t u d y made b y a g r o c p o f y o u r s t u d e n t s i n - v o l v i n g t h c a v c r n y e t i m c a p a t l e n t s p c n d s i n o u r o f f i c e w a i t i n g t o s e e t h e d e n t l s t .
I f i n d t h e s e f i q u r e s t o b e v e r y a c c u r a t e according t o my own p e r s o n a l observations s l n c e b e c o m i n g t t , e c : i i c e .;a?- a g e r :or t h i s o f f i c e .
R y b e l n q a b l e t o d e t e r m i n e t h e a v e r a g e w a i t i n q t l m e o f a p a t l e n t , .I w l l l b e a b l e t o b e t t e r s c h r d u l e t h e d e n t i s t ' s t i m e a c c o r d i n q t o t i > e p r o c e e c r e h e w i l l b e performing o n s a i d p a t i e n t . I t w l l l r l s o a l l o w me t o g i v e t h e p a t i e n t a m o r e a c c u r a t e a c c o u n t l n q o f t h e t o t 2 1 t i m e h e o r s h e w l l l s p e n d i n o u r o f f i c e . T h e t l m e c h a t i s a l l o t t e d b y t h e d e n - t i s t i n v o l v e s h i s w o r k o n l y .
I h a v e a l s o p o i n t e d o u t t h a t a new p a t i e n t i s s e e n f a s t e r t h a n a r e g u l a r p a t i e n t a n d t h i s s t u d y c o n c u r s w i t h my own o b s e r v a t i o n s . By b e l n q able t o p r e s e n t t h i s t o t h e d e n t i s t , I w i l l b e r b l e t o e n c o u r a g e them t o w o r k o n t h e l r t l m e man- a g e m r c t i n r e g a r d s t o t h e l r r e g u l a r p a t i e n t s .
I s i n c e r e l y w o u l d l i k e t o t h a n k t h e s t u d e n t s l n v o l v e d w i t h t h i s p r o l e c t . I t h a s b e e n i n f o r m a t i v e a n d w l l l h e l p me b r i n g b e t t e r t i m e m a n a g e m e n t t o o u r o f f l c e . Sincerely y o u r s ,
Figure 21. Executive Summary and Manager's Letter for a Project from Fall 1989
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* the lack of routine use of computers in the course, * variation in input: my TA's and students come from many suppliers;
I do not hire them, * managing the course so that those who are committed to learning are not
impeded by those who are not, * how to create the project teams and how to facilitate them to manage
themselves so that the "free-loader" problem is handled, and * how to bridge the gaps between the many points of view that the faculty
in my department have toward this course so that quality improvement of all courses taught may become a departmental theme.
6. CONCLUSION: THE NEXT-GENERATION COURSE.
I propose that the defining characteristic of the next generation of large- lecture, introductory statistics course be that it contain both on- and off-line quality improvement activities. Admittedly, quality improvement is not easy in this environment: relationships are adversarial, fear is rampant, breakdowns are not seen as opportunities (Zahn 1988), collecting data on how well you are doing is potentially embarrassing, and resources are limited.
In spite of all this, I propose that we as a profession have only two choices: systematically improve the quality of the large-lecture course or offer excuses for not doing so. If we continue to make excuses for the poor quality of our introductory course and if statistics is really as useful as we say it is, then I predict that someone in another discipline will learn how to teach large-lecture, introductory statistics courses effectively and efficiently. We will then lose all of this business.
Do we have the courage to take on the task of improving the quality of the large-lecture, introductory statistics course? If not us, who? If not now, when? (This is a variation on the Talmud, Sayings of Our Fathers, which was modified by Ted Sorensen for John F. Kennedy's inaugural address.)
7. AN INVITATION
I am looking for coaches and partners. Coaches will help me consider the questions, "What is missing from the system? How can this work be done better?" Partners are individuals who are willing to collaborate in some way in the task of systematically improving the quality of these types of service course Please let me know if you are interested in either of these activities.
Networking is an essential part of quality improvement. One aspect of it that has been especially valuable to me has been the series of conferences on D
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Making Statistics More Effective in Schools of Business. Individuals from that conference have made valuable contributions to my efforts over the years (Zahn, Benson, Sherr, Miller, and Godfrey; 1987), (Zahn and Sherr; 1988). This brings me to my final purpose in this article: to offer support and encouragement to anyone thinking of starting an effort such as this. Please let me know if you wish to take me up on this offer.
ACKNOWLEDGEMENTS
I gratefully acknowledge the support I have received over the years in this project in so many ways from my wife, Andrea Zahn. Several colleagues have also provided valuable input: Lawrence A. Sherr, P. George Benson, Robert Miller, Blan Godfrey, Dan Boroto, Duane Meeter, and Ron Polland. All my Teaching Assistants have been helpful in improving the course; three especially stand out for special thanks for their contributions: Karen Kinard, V. Ramakrishn- an, and Heather Smith. Last, but by no means least, I am grateful to the over 2500 students with whom I have worked in STA 3014 over the past six years for all they have taught me about teaching, statistics, quality improvement, commit- ment, life, and myself.
REFERENCES
Boroto, D. R. (1990). On being valued and utilized: The problem of assessing what is wanted and needed. A presentation at the 1990 Winter Conference of the American Statistical Association, January 4, 1990, Orlando, FL.
Boroto, D. R. and Zahn, D. A. (1989a). The wanted-and-needed conversation: A tool to enhance consulting effectiveness. 1989 Proceedings of the Section on Statistical Education of the American Statistical Association.
Boroto, D. R. and Zahn, D. A. (1989b). Promoting Statistics: On Becoming Valued and Utilized. The American Statistician, 43, 71-72.
Derning, W. E. (1985). Out of the Crisis. Cambridge: MIT, Center for Advanced Engineering Technology.
Gitlow, H., Gitlow, S., Oppenheim, A., and Oppenheim, R. (1989). Tools and Methods for the Improvement of Quality. Homewood, IL: Irwin.
Ishikawa, K. (1976). Guide to Quality Control. Tokyo: Asian Productivity Organization.
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Joiner, B. L. (1985). The key role of statisticians in the transformation of North American industry. The American Statistician, 39, 224-227.
Zahn, D. A. (1988). Quality Breakdowns: An Opportunity in Disguise. Forty- second Annual Quality Congress Transactions, Milwaukee: American Society for Quality Control, 56-62.
Zahn, D. A. (1989a). Teaching assistant job description. A handout available on request.
Zahn, D. A. (1989b). Experiences with a course project in a large lecture course. A presentation at the Fourth Conference on Making Statistics More Effective in Schools of Business, Ann Arbor, MI, June 16-17, 1989.
Zahn, D. A. (1990a). General information handout. A handout available on request.
Zahn, D. A. (1990b). STA 3014: Course packet. A handout available on request.
Zahn, D. A. and Sherr, L. A. (1988). Systematically improving the quality of statistics courses: An invitation. 1988 Proceedings of the Busincss and Economic Statistics Section of the American Statistical Association
Zahn, D., Benson, G., Godfrey, B., Miller, R., and Sherr, L. (1987). Improving the quality of business statistics: A process approach. 1987 Proceedings of thc Section on Statistical Education of the American Statistical Associution.
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