Teaching

  • This paper will describe a content-pedagogy course designed to prepare elementary and middle school teachers to teach statistics in the schools. The course is organized around the newly revised content standards developed by the National Council of Teachers of Mathematics. A central objective is to encourage teachers to see statistics as a problem solving process. The course has been implemented as one component of the "Learning Math" Project. Produced by WGBH with funding from the Annenberg/Corporation for Public Broadcasting Math and Science Project, "Learning Math" is developing a series of five college-level courses designed to teach mathematics content to elementary and middle schools teachers. In the statistics course, nine video sessions follow an actual class of teachers through content classes, with footage edited to highlight critical statistical concepts. An on-line course, which parallels the nine videos, is also being developed.

  • Whatever the debates about the relation between mathematics and statistics as disciplines, the latter is typically offered within school mathematics curricula. This relatively new inclusion has enhanced the opportunity for learners to experience a greater relevance of mathematics curricula to their own lives, and hence also created the imperative to better understand how best to organise teaching and learning toward such goals. Not surprisingly, teacher education has had to take on such challenges and in so doing brought a focus also on what happens within the halls of tertiary institutions. The question this paper addresses is how best do we prepare teachers to connect mathematics and statistics education to learners' own realities. If project work, within a broad social, cultural political approach, is one means for forging such links then there is a need to analyse and better understand the kinds of teacher education pedagogies that may be engaged to build the necessary knowledge, skills, attitudes and values among teachers.

  • A common concern in the professional development of teachers is to provide them with appropriate technical support and training in the use of information and communication technology (ICT). The need for such training is a current concern in, amongst other places, the UK and the Northern Territory of Australia, and this has provided the first motivation for the development of an appropriate training course emphasizing ICT tools such as the web, email and MS(tm) Excel. The second motivation is through a desire to help the development of teaching data handling in schools. Consequently, the Royal Statistical Society (RSS) Centre for Statistical Education, UK, together with Dr Ian Roberts of Northern Territory University, Australia, developed an ICT-type training course that is centred on data provided by the UK-based CensusAtSchool project (http://www.censusatschool.ntu.ac.uk).

  • In their first and often only statistics course, health-care professionals are taught Bayes' theorem in the context of diagnostic testing. They learn the concepts of sensitivity/specificity and predictive value positive/negative and how Bayes' theorem can assist in diagnostic decision-making. Then the class moves on often spending weeks on tests of significance. This paper will argue for changing this practice, and instead focusing such courses on statistics for decision-making beyond diagnostic testing. It will argue that such changes will make our health-care professionals better consumers of statistical information and better decision makers.

  • In this paper we will explore the challenge of making statistics more meaningful to future nurses. In the fast moving undergraduate student world the expectations we place upon nursing students are considerable. Typically they experience high class-contact hours in addition to their clinical placements. Compounding the problem, undergraduate nursing students have diverse mathematical backgrounds and seldom perceive statistics as being relevant for them. Given these constraints we have adopted the relatively modest aim of producing informed and discriminating consumers of statistics and research, rather than skilled statistical practitioners or researchers. With a focus on computer output rather than by-hand calculations, we have made use of strategic examples, appropriate journal articles and an historical hypothetical. This approach has both relieved the anxiety and distraction associated with calculations and increased students' engagement in the learning process.

  • Each year, a new crop of physicians enters residency training programs in medical teaching institutions worldwide. Second and subsequent year residents continue with the programs in which they have participated in a prior year. The educational curriculum may include a biostatistical component, where the instructor is presented with an opportunity to focus on biostatistical issues bearing on various aspects of medical practice and research. This paper describes such a presentation in a university medical school residency training program. The training session centered on research findings published recently in the medical literature. Issues regarding topic, journal, and article selection, teaching aids, approaches to illustrating aspects of study design, power analysis, statistical methodology, and interpretation of results, promoting contact with a biostatistical consultant, and feedback from lecture attendees are described.

  • Teaching of statistics involves developing and adapting robust procedures for understanding statistical concepts, and for the management and analysis of statistical data. The field of statistics is constantly challenged problems that arise from science, industry and business. Traditionally, the statistics curriculum deals with data often collected to answer specific questions. However, in the modern 'information' age, vast amounts of data are collected, often automatically, with the advent of powerful computers. Data Mining is the process of extracting knowledge from large volumes of data. Since 'computation' plays a major role in this process, computer scientists have a significant claim over the ownership of data mining. Nevertheless, data mining techniques, in general, have a statistical base; and statisticians are beginning to show a significant interest in the area, including offering tertiary courses in 'statistical' data mining.

  • Multivariate data in ecological applications most often occur in the form of counts of species abundances in assemblages, where each species is a variable. These data do not generally conform to traditional statistical assumptions, and so special approaches and methods are needed in this context. Statisticians need to be informed about these special problems with ecological data. In addition, the rationale for complex experimental designs that is a trademark of most ecological studies needs to be well understood by applied statisticians in this area. On the other hand, successful approaches for teaching ecologists about the use of multivariate statistics include sticking to the conceptual, rather than the mathematical. I provide here an overview of the methods that have helped teaching across these two disciplines, including a general approach for the use of novel non-parametric methods in the analysis of ecological community data.

  • Much of the material in the graduate survey sampling course is tedious to teach and learn. The classroom is enlivened and students are better able to use the concepts taught in the course when they have some experience applying it in real populations. This paper discusses some activities or mini-projects that can be used in the classroom to give students this experience without introducing come of the complexities associated with a full-scale project.

  • An important topic presented in introductory statistics courses is the estimation of population parameters using samples. Students learn that when estimating population variances using sample data, we always get an underestimate of the population variance if we divide by n rather than n-1. One implication of this correction is that the degree of bias gets smaller as the sample gets larger and larger. This paper explains the nature of bias and correction in the estimated variance and discusses the properties of a good estimator (unbiasedness, consistency, efficiency, and sufficiency). A BASIC computer program that is based on Monte Carlo methods is introduced, which can be used to teach students the concept of bias in estimating variance. The program is included in this paper. This type of treatment is needed because surprisingly few students or researchers understand this bias and why a correction for bias is needed. One table and three graphs summarize the analyses. A 10-item list of references is included, and two appendices present the computer program and five examples of its use. (Author/SLD)

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