Journal Article

  • In 2005, the Guidelines for Assessment and Instruction in Statistics Education (GAISE) college
    report described several recommendations for teaching introductory statistics. This paper
    discusses how a large multi-section introductory course was redesigned in order to implement
    these recommendations. The experience described discusses the key sections of the GAISE
    report and sheds light on the challenges that must be overcome in putting them in place. The
    result is a course which addresses both the ―how to‖ and big picture of statistics.

  • Service-learning can mean different things and look quite different in varying statistics curricula
    that may include undergraduates, graduates, majors and non-majors across a wide array of higher institutions. The terms community engagement, volunteerism, community-based projects and service-learning are tossed around on various institutions‟ websites. The purpose of this article is two-fold. First is to provide an historical review of the evolution of service-learning activities to try to unify and define the terminology as one might use this pedagogy for statistics
    instruction. Second is to present some examples of how a first and second course in business
    statistics can step up from service-learning and move up the continuum towards reaping the
    reciprocal benefits of SERVICE-LEARNING (SL). In this article, service learning (note the
    omission of a hyphen) is a valued classroom service activity that separates the activity from the
    learning goals of the class, while service-learning (note the presence of a hyphen) is a teaching
    methodology in which the service and learning goals are carefully given equal weight in the
    development of the project so that classroom goals and service outcomes enhance each other
    providing a reciprocal experience for all participants (Sigmon 1994). When this careful design is
    a “method of teaching through which students apply newly acquired academic skills and
    knowledge to address real-life needs in their own communities” (ASLER 1994), SL unifies what
    students are currently learning in the classroom with the service they are simultaneously
    providing in the community. Careful design opens the door to provide opportunities of SL in an
    introductory, non-majors statistics class.

  • We describe a web-based interactive graphic that can be used as a resource in introductory
    classes in mathematical statistics. This interactive graphic presents 76 common univariate
    distributions and gives details on (a) various features of the distribution such as the functional
    form of the probability density function and cumulative distribution function, graphs
    of the probability density function for various parameter settings, and values of population
    moments; (b) properties that the distribution possesses, for example, linear combinations
    of independent random variables from a particular distribution family also belong to the
    same distribution family; and (c) relationships between the various distributions, including
    special cases, transformations, limiting distributions, and Bayesian relationships. The interactive
    graphic went online on 11/30/12 at the URL www.math.wm.edu/ leemis/chart/UDR/
    UDR.html.

  • Over the past few decades there has been a large amount of research dedicated to the teaching of statistics. The impact of this research has started to change course content and structure, in both introductory and advanced courses for statisticians and those from other disciplines. In the light of these changes future directions in the teaching and learning of statistics must take into account new innovative pedagogical instructions, educational technologies and the abundance of Web resources that are now available. This article examines different aspects of currently identified challenges in the teaching and learning of statistics and gives an overview of useful strategies and innovations for developing research-based statistics courses in the context of recommendations for reforms, outlining the place of information technology within this framework. The article presents a review of the literature on the topic of statistics education and gives instructors a set of guidelines for generating new and effective teaching material. The summarised recommendations incorporate many innovations employed in a variety of successful statistics classes today. The review is complemented by a collection of statistics related online resources currently available on the Web

  • The Analysis of Variance is often taught in introductory statistics courses, but it is not clear that students really understand the method. This is because the derivation of the test statistic and p-value requires a relatively sophisticated mathematical background which may not be well-remembered or understood. Thus, the essential concept behind the Analysis of Variance can be obscured. On the other hand, it is possible to provide students with a graphical technique that makes the essential concept transparent. The technique discussed in this article can be understood by students with little or no background in probability or statistics. In fact, only the ability to add, subtract, compute averages, and interpret histograms is required.

  • Team-based learning (TBL) is a pedagogical strategy that uses groups of students working
    together in teams to learn course material. The main learning objective in TBL is to provide
    students the opportunity to practice course concepts during class-time. A key feature is
    multiple-choice quizzes that students take individually and then re-take as a team. TBL was
    originally conceived by Larry Michaelsen (University of Central Missouri) for his business
    classes and has proven to be especially effective in training medical students. In this paper, we
    describe an adaptation of TBL for an undergraduate statistical literacy course.

  • This paper reports on an instrument designed to assess the practices and beliefs of instructors of introductory statistics courses across the disciplines. Funded by a grant from the National Science Foundation, this project developed, piloted, and gathered validity evidence for the Statistics Teaching Inventory (STI). The instrument consists of 50 items in six parts and is administered online. The development of the instrument and the gathering and analysis of validity evidence are described. Plans and suggestions for use of the STI are offered.

  • Clinicians have characteristics – high scientific maturity, low tolerance for symbol manipulation and programming, limited time outside of class– that limit the effectiveness of traditional methods for teaching multi-predictor modeling. We describe an active-learning-based approach
    that shows particular promise for accommodating these characteristics.

  • Graduate students in the health sciences who hope to become independent researchers must be able to write up their results at a standard suitable for submission to peer-reviewed journals. Bayesian analyses are still rare in the medical literature, and students are often unclear on what should be included in a manuscript. Whilst there are published guidelines on reporting of Bayesian analyses, students should also be encouraged to think about why some items need to be reported whereas others do not. We describe a classroom activity in which students develop their own reporting guideline. The guideline that the students produce is not intended to replace existing guidelines, rather we have found that the process of developing the guideline is helpful in encouraging students to think through the “why?” as well as the “what?” of reporting.

  • The value to students of active learning has been recognized. This has led to the wide use of assignments in statistical methods courses where students use statistical software and computing equipment to analyze data. These assignments enable most students to master the mechanics of data analysis. The amount of experience that a student can get with such assignments, however, is limited. A sizable proportion of students have difficulty grasping some of the many concepts that are introduced in these courses. Nevertheless, these concepts are important for effective modeling and data analysis, and instructors should focus on them. By using current computing technology, it is possible to supplement standard data analysis assignments and algebraic derivations and have students become actively involved in the learning of important statistical concepts. The learning experience can be enhanced by giving students additional statistical "experiences" by using combinations of carefully designed and implemented multiple simulations and dynamic graphics to illustrate key ideas. In this article we describe and illustrate several instructional modules and corresponding software that have been designed to assist instructors in teaching introductory statistics courses.

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