Proceedings

  • Students in elementary statistics traditionally see experiments and data as words and numbers in a text. They receive little exposure to the important statistical activities of sample selection, data collection, experimental design, development of statistical models, the need for randomization, selection of factors, etc. They often leave the first course without a firm understanding of the role of applied statistics or of the statistician in scientific investigations. In an attempt to improve elementary statistics education, we have developed a statistics laboratory similar to those of other elementary science courses. We will discuss our experiences in teaching the one-semester hour Elementary Statistics Laboratory course that can be taken with or after the traditional elementary statistics course. In each session students, working in teams, discuss the design of an experiment, carry out the experiment, and analyze their data using Minitab on a Macintosh. The students then individually either answer a series of short answer questions or write a formal scientific report. The labs are designed to be relatively inexpensive and do not require a prior background in science, statistics or computing.

  • This paper is organized in three sections. First, we examine the justification for attending to non-cognitive issues within the larger context of the goals of statistics education Next, we briefly review and critique existing approaches to research on students' beliefs and attitudes towards statistics. Finally, we explore implications for assessment practices in statistics. Finally, we explore implications for assessment practices in statistics education and for further research. (By research we refer both to "academic" research done for increasing general knowledge, as well as to local research that individual teachers or statistics departments can, and in our view should, undertake in order to be informed about where their students stand and to be able to provide a better service to learners.)

  • The Quantitative Literacy (QL) project has affected how statistics is viewed and taught by high school mathematics teachers. Each summer since 1987 the ASA Center for Statistics Education has organized QL workshops at various places around the country. This movement has been in concert with the National Council of Teachers of Mathematics movement to revamp mathematics instruction with their Curriculum and Development Standards. Quantitative Literacy is now a major part of the thinking of national and local leaders in mathematics education. Unfortunately, few science teachers have been affected by the QL project. While mathematics teachers introduce boxplots in their algebra classes, the science teachers in the same building have each student complete a laboratory exercise and turn in a report, without ever considering how the results of the various students differ. The middle school or high school science laboratory is an excellent place in which to use statistical ideas, but rarely does this happen. In 1990 ASA organized a planning meeting that led to the formation of the SEAQL (Science Education And Quantitative Literacy) task force. This group of statisticians and science teachers is promoting the use of statistics in school science courses by focusing on common laboratory experiments that involve data collection. The task force will host a leadership conference in November for science curriculum supervisors. At the conference the task force will demonstrate some SEAQL laboratory activities and convey the philosophy of using data analysis as a science teaching tool.

  • During winter quarter 1993, we offered for three-credit-hour course "STT 343, Probability and Statistics for Elementary and Middle School Teachers." The Quantitative Literacy Series was used as the textbook, and the course was taught with the intention of remaining true to the philosophy of the Quantitative Literacy Workshops. This is a report on my experiences.

  • This paper describes a National Science foundation (NSF) Sponsored Teacher Enhancement Program (TEP) in statistics during the years 1991 - 1994 conducted at the University of Puerto Rico. The project evolved from the belief that statistics is more meaningful to students when they plan, experiment, collect and analyze data themselves rather than when they learn a set of formulas and techniques. This idea was first incorporated locally in an NSF Sponsored Young Scholars Program in statistics during the period 1989 - 1991 in which the author worked with talented students from high schools in the region. The experience and success of the Young Scholars Program and the education department's request to expand it led to the TEP in statistics presented herein. There are three special features of this project. The first feature is to introduce the modern method of teaching statistical reasoning to students primarily through the use of examples and class projects which are interesting to students and related to current issues. A second important feature is the comprehensive nature of training in the fields of statistics, computers and research methodology. the third important characteristic is the three year follow up phase of the project which provided time to integrate the philosophy of the project into the educational system.

  • Mathematicians from the Greeks on have used simple physical or visual models to understand and create new mathematics. The history of innovation in geometry, probability and calculus is full of examples of commonplace or mundane models explicating and motivating new ideas. Modern research statisticians also use the same strategies. Ask an expert in experimental design what he knows about and how he thinks about an industrial experiment. Often you will get an extraordinarily naive answer. You discover that he has cheerfully ignored important, even critical, physical details of the industrial process., and yet industry amply compensates our apparently naive experimental design colleagues. Perhaps industry has learned some lessons that we as teachers of statistics have forgotten. In this paper we argue that our undergraduate students need to be able to view, construct and manipulate mundane models and that this is a critical part of undergraduate mathematics and statistics education. All this may seem obvious, but in the past decades a number of forces have contributed to a decline of our students ability to approach statistics using visual model approaches to mathematics.

  • We describe an NSF-funded project to develop a new curriculum for introductory statistics for engineering, science and management students. The goals of the curriculum are to get students to think critically about data, and to demonstrate the role of statistics in scientific investigation. The curriculum features a number of one-week modules each keyed to project and laboratory experience. The modular structure offers flexibility in course design and gives students the ability to tailor the course to individual needs. The learning environment is problem-driven and alternative modes of delivery are emphasized.

  • We are proposing a statistical methods sequence, each having separate lecture-based and stat laboratory components. First, the lecture-based courses will allow a through examinations of the "when to" and "what-to", while the statistical lab will not only expose the student to the "how-to", but through simulations and discipline related problems motivate and demonstrate the underlying concepts discussed in the course lectures. The components should compliment each other, rather than be adversarial. Secondly, on many campuses across the country questions arise concerning how to incorporate writing in the undergraduate curriculum. The MTH 441/442 sequence is a perfect venue for incorporating student writing in a mathematics course. One objective of this revised MTH 441/442 sequence is to encourage student development of writing skills. The assignment in the stat lab sessions are to be completed in a report format. Using the edit options in the Primos System (if done on the mainframe) or the available text editing software on the PC, the student will be expected to "clean-up" the output from the statistical package and coherently express their analysis of the results in a written report. Not only should the statistical lab contribute to their mastery of the "how-to" of packages such as Minitab, SPSSx, and SAS, but the student should benefit from gaining stronger written communications skills.

  • In this paper, particular suggestions, borrowed from principles of effective teaching practice, are made to enable students to have a clear sense of the goals, sequence, and rationale for the course, and more generally, to engage students in meaningful and memorable learning. Finally, linkages should be clipped from newspaper and the popular press to illustrate the applicability to everyday life of the statistics being taught in class, and to make students understand how inundated they are with statistics on a daily basis, even though they probably do not realize it. A motivated student is an interested learner, and the more we, as instructors, can do to motivate our students, the more satisfied we can expect them to be.

  • In the best academic tradition, we start with a definition: the "statistically literate" individual can "dis-aggregate and re-aggregate" to operate effectively as a statistical consumer. That is, he/she can extract information and interpret this information and can transform and interpret this information and can transmit it in terms your father -- your boss -- your client can understand and use. Evidently, this is a specialized version of the criteria for the label "quantitatively literate" which we will also use. We have been concerned for some time about the ways in which introductory statistics courses can better contribute to the goal of quantitative literacy for all adults and, in particular, for those whose secondary school experience may have left them mathematically dysfunctional or underskilled. In particular, we have wondered about the large number of students in the two-year colleges.

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